1<!DOCTYPE html> 2<html> 3 <head> 4 <title>Sponsor Data Science Competition - DataSource.ai</title> 5<link rel="icon" type="image/x-icon" href="/assets/favicons/favicon.ico"> 6<meta name="description" content="We have a pool of Data Scientists who can help your company discover, improve and analyze data in the right way, predicting or classifying complex problems. Let us show you why we are the best community of data scientists"> 7<meta name="keywords" content="data science competitions, data science competition, data science challenges, machine learning challenges"> 8<meta property="og:type" content="website"> 9<meta property="og:title" content="DataSource.ai - Data Science Competitions For Startups & SMBs"> 10<meta property="og:description" content="We democratize data science competitions, allowing startups to sponsor challenges and award prizes accessible to them. We give startups access to a global talent pool to help them solve their problems using data science"> 11<meta property="og:url" content="view-source:https://www.datasource.ai/es"> 12<meta property="og:site_name" content="DataSource.ai"> 13<meta property="og:image" content="https://www.datasource.ai/datasource-home.png"> 14<meta name="twitter:card" content="summary"> 15<meta name="twitter:description" content="We democratize data science competitions, allowing startups to sponsor challenges and award prizes accessible to them. We give startups a..."> 16<meta name="twitter:title" content="DataSource.ai - Data Science Competitions For Startups & SMBs"> 17<meta name="twitter:image" content="https://www.datasource.ai/datasource-home.png"> 18<meta name="twitter:site" content="@datasourceAI"> 19<meta name="twitter:creator" content="DataSource.ai"> 20 <meta name="csrf-param" content="authenticity_token" /> 21<meta name="csrf-token" content="NXB2V03ntPC8NbzaIXeLWj9YfUeKr6x8Wb6xEUEke7p7Z5eFUPVCmIDUBFnqhXUTdU65HYpxIY86Y-osx9mRgw" /> 22 23 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> 24 <link rel="stylesheet" media="all" href="/assets/application-1b5619074341cd1c98284e8f417181a81578bfa0e8b15d8722c6bf4671bbb9b1.css" data-turbolinks-track="reload" /> 25 <!-- Stripe Public Key --> 26 <meta name="stripe-public-key" content="pk_live_C0IsUgnpWjMX01aFSWtStNqa00ja1iZhPn" /> 27 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/semantic-ui/2.5.0/components/dropdown.min.css" integrity="sha512-tdyzfjYDk2OR9L10jvzzuGUB0Rvl5fyMW/Z8yfbYyyENfuXxtcDtEq7d/PdiA0hzQzXjWwvZasjEp8Joli2tkw==" crossorigin="anonymous" referrerpolicy="no-referrer" /> 28 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/semantic-ui/2.5.0/components/transition.min.css" integrity="sha512-HgM+2tpAdAIDm77Pe0gKU78lxA5MPfurAZFf1KDm1l/0npc9YRh+7rqDXmBKajNu3oFtiOKX+fBrK0W/8wRecw==" crossorigin="anonymous" referrerpolicy="no-referrer" /> 29 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/semantic-ui/2.5.0/components/icon.min.css" integrity="sha512-rTyypI20S663Wq9zrzMSOP1MNPHaYX7+ug5OZ/DTqCDLwRdErCo2W30Hdme3aUzJSvAUap3SmBk0r5j0vRxyGw==" crossorigin="anonymous" referrerpolicy="no-referrer" /> 30 <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/select2.min.css" rel="stylesheet" /> 31 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css" integrity="sha512-iecdLmaskl7CVkqkXNQ/ZH/XLlvWZOJyj7Yy7tcenmpD1ypASozpmT/E0iPtmFIB46ZmdtAc9eNBvH0H/ZpiBw==" crossorigin="anonymous" referrerpolicy="no-referrer" /> 32 <link rel="shortcut icon" type="image/x-icon" href="/assets/favicons/favicon.ico" /> 33<link rel="apple-touch-icon" sizes="180x180" href="/assets/favicons/apple-touch-icon.png"> 34<link rel="icon" type="image/png" sizes="32x32" href="/assets/favicons/favicon-32x32.png"> 35<link rel="icon" type="image/png" sizes="16x16" href="/assets/favicons/favicon-16x16.png"> 36<link rel="manifest" href="/assets/favicons/site.webmanifest"> 37<link rel="mask-icon" href="/assets/favicons/safari-pinned-tab.svg" color="#5bbad5"> 38<meta name="msapplication-TileColor" content="#da532c"> 39<meta name="theme-color" content="#ffffff"> 40 41 <!-- Start of Zendesk Widget script --> 42
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186 <div class="potential-list mx-auto" style="max-width: 928px;"> 187 <div data-aos="fade-right" data-aos-delay="100" class="potential-item potential-item--top-left text-origin fs-36 fw-700" style="line-height: 1.3"> 188 <div class="title-fluid">Study and solve complex data science challenges</div> 189 <img src="/assets/application/laptop-and-cactus.png" alt="laptop and cactus"> 190 </div> 191 <div data-aos="fade-down" data-aos-delay="100" class="potential-item potential-item--top-right fs-36 fw-700"> 192 <div class="title-fluid">Master your data science skills with leading industry experts and gather peer reviews</div> 193 <img src="/assets/application/woman-and-gem.png" alt="women and gem"> 194 </div> 195 <div data-aos="fade-up" data-aos-delay="100" class="potential-item potential-item--bottom-left text-origin fs-36 fw-700"> 196 <div class="title-fluid">Earn monetary & other rewards</div> 197 <img src="/assets/application/money-and-trophy.png" alt="money and trophy"> 198 </div> 199 <div data-aos="fade-left" data-aos-delay="100" class="potential-item potential-item--bottom-right fs-32 fw-700"> 200 <div class="title-fluid">Power up your portfolio and validate your new skills with non-transferrable NFTs</div> 201 <img style="margin-right: 32px;" src="/assets/application/doc-and-arrow.png" alt="doc and arrow"> 202 </div> 203 </div> 204</section> 205 206<section class="container polish"> 207 <h2 class="polish__title text-origin">Polish your skills with DataSource</h2> 208 <h2 class="polish__subtitle"> 209 Grow your data science skills to unbelievable levels by competing in our data science tournaments, competitions, and data bounties. Push the boundaries of data science by using your creativity to find new solutions and win big prizes! 210 </h2> 211 <div class="polish-list"> 212 <div class="polish-item"> 213 <h3 class="polish-item__title"> 214 Data competitions 215 </h3> 216 <p class="polish-item__description">Fraud detection, customer segmentation, and forecasting - we have tons of data science competitions to align with your interests and skills. Solve the problem and win prizes!</p> 217 <img src="/assets/application/girl-with-laptop.png" alt="girl with laptop"> 218 </div> 219 <div class="polish-item"> 220 <h3 class="polish-item__title"> 221 Data tournaments 222 </h3> 223 <p class="polish-item__description">Take part in tournaments, find a new creative solution, earn rewards and gather feedback from industry experts. All the challanges are carefully analyzed and selected by the leading community and DataSource experts. </p> 224 <img src="/assets/application/girl-with-book.png" alt="girl with book"> 225 <button class="btn btn-outline-success polish-item__button"> 226 <svg width="24" height="24" viewbox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"> 227<g clip-path="url(#clip0_2873_117689)"> 228<path d="M6.382 5.968C7.97575 4.69107 9.95781 3.99674 12 4C14.125 4 16.078 4.736 17.618 5.968L19.071 4.515L20.485 5.929L19.032 7.382C20.0897 8.70615 20.7521 10.302 20.9428 11.986C21.1336 13.6699 20.845 15.3735 20.1103 16.9007C19.3756 18.4279 18.2246 19.7167 16.7898 20.6186C15.355 21.5205 13.6947 21.999 12 21.999C10.3053 21.999 8.64498 21.5205 7.2102 20.6186C5.77541 19.7167 4.62443 18.4279 3.88973 16.9007C3.15502 15.3735 2.86644 13.6699 3.0572 11.986C3.24796 10.302 3.9103 8.70615 4.96801 7.382L3.51501 5.93L4.92901 4.516L6.382 5.969V5.968ZM12 20C12.9193 20 13.8295 19.8189 14.6788 19.4672C15.5281 19.1154 16.2997 18.5998 16.9498 17.9497C17.5998 17.2997 18.1154 16.5281 18.4672 15.6788C18.8189 14.8295 19 13.9193 19 13C19 12.0807 18.8189 11.1705 18.4672 10.3212C18.1154 9.47194 17.5998 8.70026 16.9498 8.05025C16.2997 7.40024 15.5281 6.88463 14.6788 6.53284C13.8295 6.18106 12.9193 6 12 6C10.1435 6 8.36301 6.7375 7.05026 8.05025C5.7375 9.36301 5.00001 11.1435 5.00001 13C5.00001 14.8565 5.7375 16.637 7.05026 17.9497C8.36301 19.2625 10.1435 20 12 20ZM13 12H16L11 18.5V14H8.00001L13 7.495V12ZM8.00001 1H16V3H8.00001V1Z" fill="#080A25"></path> 229</g> 230<defs> 231<clippath id="clip0_2873_117689"> 232<rect width="24" height="24" fill="white"></rect> 233</clippath> 234</defs> 235</svg> 236
237 <span>Coming Soon</span> 238 </button> 239 </div> 240 <div class="polish-item"> 241 <h3 class="polish-item__title"> 242 Data bounties 243 </h3> 244 <p class="polish-item__description">Receive rewards while tackling data science bounties. Solve complex challenges, practise your skills, and receive rewards.</p> 245 <img src="/assets/application/girl-with-hair.png" alt="girl with hair"> 246 <button class="btn btn-outline-success polish-item__button"> 247 <svg width="24" height="24" viewbox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"> 248<g clip-path="url(#clip0_2873_117689)"> 249<path d="M6.382 5.968C7.97575 4.69107 9.95781 3.99674 12 4C14.125 4 16.078 4.736 17.618 5.968L19.071 4.515L20.485 5.929L19.032 7.382C20.0897 8.70615 20.7521 10.302 20.9428 11.986C21.1336 13.6699 20.845 15.3735 20.1103 16.9007C19.3756 18.4279 18.2246 19.7167 16.7898 20.6186C15.355 21.5205 13.6947 21.999 12 21.999C10.3053 21.999 8.64498 21.5205 7.2102 20.6186C5.77541 19.7167 4.62443 18.4279 3.88973 16.9007C3.15502 15.3735 2.86644 13.6699 3.0572 11.986C3.24796 10.302 3.9103 8.70615 4.96801 7.382L3.51501 5.93L4.92901 4.516L6.382 5.969V5.968ZM12 20C12.9193 20 13.8295 19.8189 14.6788 19.4672C15.5281 19.1154 16.2997 18.5998 16.9498 17.9497C17.5998 17.2997 18.1154 16.5281 18.4672 15.6788C18.8189 14.8295 19 13.9193 19 13C19 12.0807 18.8189 11.1705 18.4672 10.3212C18.1154 9.47194 17.5998 8.70026 16.9498 8.05025C16.2997 7.40024 15.5281 6.88463 14.6788 6.53284C13.8295 6.18106 12.9193 6 12 6C10.1435 6 8.36301 6.7375 7.05026 8.05025C5.7375 9.36301 5.00001 11.1435 5.00001 13C5.00001 14.8565 5.7375 16.637 7.05026 17.9497C8.36301 19.2625 10.1435 20 12 20ZM13 12H16L11 18.5V14H8.00001L13 7.495V12ZM8.00001 1H16V3H8.00001V1Z" fill="#080A25"></path> 250</g> 251<defs> 252<clippath id="clip0_2873_117689"> 253<rect width="24" height="24" fill="white"></rect> 254</clippath> 255</defs> 256</svg> 257 258 <span>Coming Soon</span> 259 </button> 260 </div> 261 </div> 262 <div class="d-flex align-items-center justify-content-center mt-5"> 263 <button name="button" type="submit" class="new-btn-green btn-landing-page" disabled="disabled">Start Earning Rewards Today!</button> 264 </div> 265</section> 266 267<section class="container use-case"> 268 <h2 class="use-case-title"> 269 Keep learning with DataSource 270 </h2> 271 <p class="use-case-desc">The best data science and machine learning articles. Written by data scientists for data scientists (and business people)</p> 272 <ul class="use-case-list use-case-list--grid"> 273 <li class="use-case-item"> 274 <div class="use-case-item--left"> 275 <div class="use-case-thumb"> 276 <!-- 277 <div class="use-case-thumb__status use-case-thumb__status--"> 278 ---> 279 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/ed9366d1b07faf1ae661cef37ef5a00a.jpg" alt="use-case"> 280 <div class="use-case-thumb__status d-none"> 281 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 282 <g clip-path="url(#clip0_522_18001)"> 283 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
28311.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 284 </g> 285 <defs> 286 <clipPath id="clip0_522_18001"> 287 <rect width="16" height="16" fill="white"/> 288 </clipPath> 289 </defs> 290 </svg> 291 N/A 292 </div> 293 </div> 294 </div> 295 <div class="use-case-item--right"> 296 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/the-impact-of-ai-and-data-science-on-modern-industry-challenges"> 297 The Impact of AI and Data Science on Modern Industry Challenges 298</a> <div class="use-case-tag"> 299 <span class="use-case-tag__item">Business</span> 300 </div> 301 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 302 The digital transformation sweeping through industries is making data science and artificialintelligence (AI) more essential than ever. From manufacturing to healthcare, companies areleveraging data and AI not just for operational efficiency but also for strategic growth. Here, weâllexplore how real-life data science and AI applications are solving industry challenges andshaping the future.Predictive Maintenance in ManufacturingManufacturers have long sought ways to reduce equipment downtime and prolong machinerylife. Predictive maintenance, powered by AI, enables businesses to foresee issues before theyarise. By analyzing data from sensors attached to machinery, AI can detect early warning signsof potential failures. This proactive approach reduces unexpected breakdowns and associatedcosts, boosting overall productivity.A prime example is the use of predictive maintenance in the elevator industry. Elevators arenow connected via GSM gateways, enabling real-time data communication through networkslike 3G and 4G. AI analyzes sensor data from various elevator components to detect anomaliessuch as changes in motor vibration or cable wear. When detected, these anomalies triggeralerts for technicians to address the issues before a breakdown occurs. Companies like KONEhave leveraged platforms like IBM Watson to enhance their predictive maintenance capabilities,ensuring safer and more reliable operations.Fraud Detection in Financial ServicesFraud is a persistent challenge in financial services, threatening the security of institutions andtheir customers. AI-driven solutions have revolutionized fraud detection by leveraging machinelearning algorithms to identify unusual transaction patterns and flag potential fraudulent activityin real-time.These systems are trained on extensive datasets, enabling them to learn and adapt to changingfraud tactics. For example, machine learning models analyze historical transaction data torecognize deviations from typical customer behavior. This allows financial institutions to quicklyidentify and halt suspicious transactions, minimizing the impact of fraud. Companies thatincorporate tools like Microsoft Power BI can further optimize their insights, making informeddecisions and bolstering security measures across the board.Healthcare DiagnosticsThe application of data science in healthcare is transformative, enabling faster and moreaccurate diagnostics. AI algorithms analyze complex medical data, such as imaging scans andpatient records, to identify diseases early and recommend treatment plans. This assists doctorsin diagnosing conditions more precisely and allows for more personalized patient care.In radiology, for instance, AI tools can process thousands of X-rays to detect abnormalities withan accuracy that sometimes surpasses that of human experts. AI is also proving indispensablein genomics, helping to identify hereditary disease markers and guiding the development ofpersonalized treatments. According to DataScientest, advancements in healthcare analytics arenot only improving diagnostic processes but also facilitating better patient outcomes byproviding actionable insights into medical data.Supply Chain OptimizationThe supply chain is the backbone of any product-driven industry. Effe
302ctive supply chainmanagement ensures that products reach customers promptly and efficiently. AI plays asignificant role here by improving demand forecasting, inventory management, and deliveryprocesses.Predictive analytics, for example, use historical sales data and external factors such as weatherand economic indicators to forecast product demand more accurately. This helps companiesavoid overstocking or understocking, leading to more efficient inventory management.Additionally, AI-driven route optimization ensures faster delivery times and reducedtransportation costs.Customer Experience EnhancementBusinesses today are increasingly turning to AI to enhance customer experiences. By analyzingcustomer data, AI can help predict customer needs and personalize interactions, makingservices more engaging and effective. AI-powered chatbots, for example, have become commonplace in handling basic customer inquiries. These bots, equipped with natural language processing (NLP), can understand and respond to questions, improving response times and overall customer satisfaction. Beyond chatbots, advanced recommendation engines are used in e-commerce platforms to suggestproducts based on user behavior.Key Challenges and ConsiderationsWhile the benefits of integrating AI and data science are clear, industries must navigate severalchallenges to make the most of these technologies. Data security is of paramount importance,especially in sectors like healthcare and finance where sensitive data is handled. Companiesmust ensure robust information security protocols and adhere to regulations such as theGeneral Data Protection Regulation (GDPR) in the EU and the Health Insurance Portability andAccountability Act (HIPAA) in the United States.Bias in AI models is another challenge that requires attention. If machine learning algorithms aretrained on non-representative data, they can perpetuate biases, leading to unfair outcomes. Forinstance, biased models in hiring practices could lead to skewed decisions, while biasedhealthcare algorithms might overlook critical patient needs. Regular audits and training ondiverse datasets can help mitigate these risks.Ethical Considerations and
302SustainabilityThe use of AI and data science should align with ethical practices. This includes ensuringtransparency in AI-driven decision-making and minimizing potential biases. It is also importantto prioritize sustainability. Companies should strive to implement energy-efficient AI models andconsider the environmental impact of their data centers and computation needs.Ethical data use and model interpretability are crucial for building trust with consumers andstakeholders. When companies openly communicate how their AI systems work and the stepstaken to prevent biases, they foster trust and encourage wider adoption.Advancing Workforce SkillsTo leverage the full potential of AI and data science, businesses must invest in upskilling theirworkforce. This includes training employees to understand and work with AI technologies, aswell as fostering a culture of data-driven decision-making.The Future of AI and Data Science in IndustryEmerging trends such as AI-powered automation and more sophisticated machine learningalgorithms will redefine how industries operate. Businesses that embrace these technologiesand focus on building a data-centric culture will be better positioned for long-term success.Theintegration of data science and AI into industry practices is more than just a trendâit is a crucialstrategy for gaining a competitive edge. 303 </div> 304 <div class="use-case-divider"></div> 305 <span class="use-case__time"> 306 5 minutes read 307 </span> 308 </div> 309</li> 310 311 <li class="use-case-item"> 312 <div class="use-case-item--left"> 313 <div class="use-case-thumb"> 314 <!-- 315 <div class="use-case-thumb__status use-case-thumb__status--"> 316 ---> 317 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/5d03fac407ac587523ea0d7728a14832.jpg" alt="use-case"> 318 <div class="use-case-thumb__status d-none"> 319 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 320 <g clip-path="url(#clip0_522_18001)"> 321 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
32111.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 322 </g> 323 <defs> 324 <clipPath id="clip0_522_18001"> 325 <rect width="16" height="16" fill="white"/> 326 </clipPath> 327 </defs> 328 </svg> 329 N/A 330 </div> 331 </div> 332 </div> 333 <div class="use-case-item--right"> 334 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/announcing-the-winners-of-the-vn1-forecasting-datathon-advancing-supply-chain-efficiency-and-reducing-forecasting-errors"> 335 Announcing the Winners of the VN1 Forecasting Datathon: Advancing Supply Chain Efficiency and Reducing Forecasting Errors 336</a> <div class="use-case-tag"> 337 <span class="use-case-tag__item">Datathons</span> 338 </div> 339 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 340 Today, we are thrilled to announce the winners of the VN1 Forecasting Datathon. The VN1 Forecasting Datathon saw an incredible turnout, with almost 1,000 registered participants submitting more than 3,000 entries across two competitive phases. VN1 Datathon was made possible by the support of our esteemed sponsorsâFlieber, Syrup Tech, and SupChains. Reducing forecasting error has become increasingly valuable in todayâs market, where supply chain efficiency can directly impact a companyâs profitability, customer satisfaction, and competitive advantage. A reduction in forecasting error, as demonstrated by the winners of our VN1 Forecasting Datathon,âitâs a strategic asset with tangible effects on the supply chain and logistics operations. Better supply chain planning with optimized logistics, purchases, and production planningInventory Optimization: Reducing forecast error means companies can optimize their inventory levels, striking a balance between overstocking and stockouts. This optimization prevents excess holding costs and minimizes the risk of stockouts, ensuring products are available when and where theyâre needed.Cost Efficiency: Lower forecasting error translates directly to reduced operational costs. Improved accuracy allows businesses to better plan production, minimize wastage, and streamline procurement. This means fewer emergency shipments, less expedited shipping, and a leaner supply chain overall, which is crucial as logistics costs continue to rise globally.Improved Customer Satisfaction: In the era of instant gratification, customers expect products to be available and delivered quickly. Accurate forecasting helps maintain the right stock levels and avoid the disappointing âout of stockâ message, contributing to better customer experiences and long-term loyalty.Better Cash Flow Management: Holding fewer surplus goods frees up capital, allowing businesses to allocate funds to other areas, such as R&D or expansion. A lower inventory burden also means fewer markdowns, improving profit margins and providing companies with greater financial flexibility.Agility in a Volatile Market: The global supply chain has faced significant challenges in recent years, from pandemic disruptions to geopolitical issues and fluctuating consumer demands. Improved forecasting enables businesses to respond with agility to these shifts, enhancing resilience in the face of unpredictable market conditions.Competition Highlights and WinnersThe VN1 Datathon challenged participants to explore novel methodologies in forecasting. Collectively, winners will share a total of $20,000 in prizes. After intense competition and remarkable innovations, here are the top winners of the VN1 Forecasting Datathon:Jakub Figura & Philip Stubbs - Achieved an error rate of 46.4%Justin Furlotte - 46.6%Arsa Nikzad - 47.6%Antoine Schwartz - 47.7%An Hoang - 48.1%To put these scores in perspective, a statistical benchmark achieved an error of around 80% on this dataset. Our winners displayed an exceptional level of accuracy, as they achieved a forecast value added (FVA) of around 40%.The innovations showcased by our Datathon winners illustrate how data-driven forecasting can reduce error rates significantly. Special Recognition: Best Notebook DocumentationA standout contribution came from Olivier Sprangers, who was awarded for his exceptional notebook documentation. Olivierâs work set a high standard, and his notebooks were used extensively by participants. Below are some of his notable contributions:NeuralForecast StarterUtilizes DeepNPTS to generate predictions with a neural network-based approach, perfect for capturing complex data patterns.MLForecast StarterUses LightGBM, a popular machine learning model, optimized for fast, accurate forecasting on large datasets.StatsForecast StarterEmploys AutoETS, a statistical approach known for its effectiveness in classical time-series forecasting.These notebooks will be publicly available post-webinar, offering a valuable resource for practitioners and students alike to refine their skills in forecasting.Join Our Upcoming WebinarIn celebration of these achievements and to dive deeper into the winning strategies, weâre hosting a Webinar on Wednesday, November 13th. You can register here: https://events.teams.microsoft.com/event/e2021207-9598-45b7-8fae-9b023c319e8f@e4fddd24-e7c0-4641-9390-846430093ede As an added bonus, weâll be publishing community notebooks. This will provide an invaluable resource for both professionals and students looking to practice and hone their skills in demand forecasting.A Heartfelt CongratulationsTo all participants, thank you for bringing your dedication, creativity, and talent to this competition. The collective work showcased here represents a leap forward in forecasting methodologies, pushing the boundaries of whatâs possible in the field.Congratulations again to our winners, and thank you to everyone who helped make the VN1 Forecasting Datathon a success. We look forward to seeing the continued impact of your work on the future of forecasting!Meet Our SponsorsFlieber: Known for helping brands forecast demand and optimize inventory, Flieber uses real-time data to help businesses meet their inventory needs accurately and efficie
340ntly, ultimately reducing stockouts and improving fulfillment rates.Syrup Tech: A cutting-edge provider of demand planning solutions, Syrup Tech leverages AI to drive profitability for e-commerce and retail brands, focusing on reducing waste and improving forecasting accuracy in highly dynamic markets.SupChains: Nicolas Vandeput's own firm, SupChains is dedicated to enhancing supply chain efficiency through education, and data-driven forecasting and inventory models, offering insights based on years of hands-on experience in the field.The challenge was hosted by Nicolas Vandeput, a renowned expert in forecasting and inventory optimization. Nicolas, is acknowledged for his commitment to advancing knowledge in supply chain forecasting. With best-selling titles like Data Science for Supply Chain Forecasting (2018) and Inventory Optimization: Models and Simulations (2020), Nicolas brings a wealth of expertise to the field, making this competition a unique and rewarding experience for participants.The innovations showcased by our Datathon winners illustrate how data-driven forecasting can reduce error rates significantly. By achieving accuracy as high as 46.4%, these participants are setting new standards for efficiency and resilience in supply chains. In sum, reducing forecasting error and optimizing logistics arenât just operational goalsâtheyâre strategic priorities that yield financial, operational, and environmental benefits. As supply chains become more sophisticated and customer expectations evolve, the ability to forecast and manage logistics effectively will remain critical to staying competitive in todayâs market. 341 </div> 342 <div class="use-case-divider"></div> 343 <span class="use-case__time"> 344 5 minutes read 345 </span> 346 </div> 347</li> 348 349 <li class="use-case-item"> 350 <div class="use-case-item--left"> 351 <div class="use-case-thumb"> 352 <!-- 353 <div class="use-case-thumb__status use-case-thumb__status--"> 354 ---> 355 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/070769b813a0814a0c4ea1287327d591.webp" alt="use-case"> 356 <div class="use-case-thumb__status d-none"> 357 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 358 <g clip-path="url(#clip0_522_18001)"> 359 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
35911.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 360 </g> 361 <defs> 362 <clipPath id="clip0_522_18001"> 363 <rect width="16" height="16" fill="white"/> 364 </clipPath> 365 </defs> 366 </svg> 367 N/A 368 </div> 369 </div> 370 </div> 371 <div class="use-case-item--right"> 372 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/rescheduling-of-vn1-forecasting-accuracy-datathon"> 373 Rescheduling of VN1 Forecasting - Accuracy Datathon 374</a> <div class="use-case-tag"> 375 <span class="use-case-tag__item">Datathons</span> 376 </div> 377 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 378 We are writing to inform you that the VN1 Forecasting - Accuracy Challenge Phase 1 has been rescheduled to launch on September 12th. This decision was made to ensure that we can provide you with the best possible experience.Recently, we upgraded our platform to enhance its capabilities, but this update required more time than anticipated to thoroughly test all types of datathons. Unfortunately, the unique elements of the upcoming challenge were not fully compatible with our existing ML endpoints structure. To address this and avoid any potential disruptions, we have decided to take the time needed to refine the challenge and make sure everything runs smoothly.We deeply appreciate the understanding and patience of our 430 registered participants, as well as the unwavering support from our sponsorsâFlieber, SyrupTech, and SupChains. Your commitment to this challenge inspires us to strive for excellence, and we are confident that the extra time will allow us to deliver a high-quality event that meets your expectations.Thank you for your continued support, and we look forward to seeing you on September 12th for the launch of the VN1 Forecasting - Accuracy Challenge Phase 1.Join the Challenge!We are excited to share that we have seen great interest from top professionals across the supply chain industry. If you haven't registered yet, now is the perfect time to join! Whether you're an expert in forecasting or just starting out your data science/ AI career, you can be part of this exciting challenge.Many of the 40 teams are still looking for teammates, so there are plenty of opportunities to connect with like-minded individuals, collaborate, and compete at the highest level. Don't miss out on this chance to be part of something special.We look forward to seeing you on September 12th for the launch of the VN1 Forecasting - Accuracy Challenge Phase 1. 379 </div> 380 <div class="use-case-divider"></div> 381 <span class="use-case__time"> 382 2 minutes read 383 </span> 384 </div> 385</li> 386 387 <li class="use-case-item"> 388 <div class="use-case-item--left"> 389 <div class="use-case-thumb"> 390 <!-- 391 <div class="use-case-thumb__status use-case-thumb__status--"> 392 ---> 393 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/5e298af24ecffb0178511b4187fd8c64.jpg" alt="use-case"> 394 <div class="use-case-thumb__status d-none"> 395 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 396 <g clip-path="url(#clip0_522_18001)"> 397 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982
39714.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769 11.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 398 </g> 399 <defs> 400 <clipPath id="clip0_522_18001"> 401 <rect width="16" height="16" fill="white"/> 402 </clipPath> 403 </defs> 404 </svg> 405 N/A 406 </div> 407 </div> 408 </div> 409 <div class="use-case-item--right"> 410 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/flieber-syrup-tech-and-supchains-launch-ai-supply-chain-forecasting-competition"> 411 Flieber, Syrup Tech, and SupChains Launch AI Supply Chain Forecasting Competition 412</a> <div class="use-case-tag"> 413 <span class="use-case-tag__item">Datathons</span> 414 </div> 415 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 416 Flieber, Syrup Tech, and SupChains Announce Strategic Partnership to Launch the VN1 Forecasting Challenge to Drive AI Innovation in Supply Chain ManagementFlieber, Syrup Tech, and SupChains are thrilled to announce the launch of the VN1 Forecasting Challenge, a datathon aimed at revolutionizing AI-driven supply chain forecasting. Led by Nicolas Vandeput and hosted by the datathon platform Datasource.ai, this competition challenges participants to develop advanced predictive models for supply chain management, with $20,000 in prizes up for grabs for the global AI & Data Science communities.Empowering AI-Driven Supply Chain SolutionsThe VN1 Forecasting Challenge is a collaborative effort by Flieber, Syrup Tech, and SupChains to push the boundaries of AI in supply chain management. By leveraging their combined expertise, the challenge aims to uncover innovative solutions to enhance supply chain efficiency, reduce waste, and drive profitability."Supply chain operations need to evolve to keep up with the demands of modern marketplaces," said Fabricio Miranda, CEO of Flieber. "This challenge provides a platform for data scientists and AI experts to showcase their skills and contribute to the future of supply chain management."Participants will use historical sales, inventory, and pricing data to develop robust predictive models that can accurately forecast sales trends for various products across different clients and warehouses.The VN1 Forecasting Challenge boasts a total prize pool of $20,000, distributed among the top-performing AI scientists. This competition is perfect for showcasing your skills and pushing forecasting models to the limit, but it's also meant to be a learning playground. The competition starts on the 15th of August, 2014. Participants can already register here: https://www.datasource.ai:443/en/home/data-science-competitions-for-startups/vn1-forecasting-accuracy-challenge-phase-1/descriptionAbout the PartnersFlieber is a multichannel inventory planning platform designed for modern commerce. Founded in 2019 and serving hundreds of brands, agencies and aggregators, Flieber offers a suite of tools that allows operations teams to make better inventory decisions in a fraction of the time. Features include: AI-based demand forecasting, multi-node inventory forecasting, replenishment simulator, native integrations with the main sales channels and warehousing systems, among others.Syrup Tech specializes in AI-driven demand forecasting and inventory optimization for the apparel and footwear industry. Intelligent allocation and buying workflows empower brands to run their businesses more effectively by recommending predictive inventory actions that drive profitability and efficie
416ncy. Syrup helps omnichannel brands like Faherty, Salomon, and Desigual reduce overstocks and held inventory while improving ful-price sell-through.SupChains founded by Nicolas Vandeput, SupChains empowers supply chain leaders to deliver higher service levels, increase forecast accuracy, and lower inventory levels by training demand and supply planners and creating leading-edge inventory and forecasting models. With its content (books, webinars, and articles), SupChains is a key player in transforming supply chain practices.The datathon is hosted in Datasource.ai datathon platform. 417 </div> 418 <div class="use-case-divider"></div> 419 <span class="use-case__time"> 420 2 minutes read 421 </span> 422 </div> 423</li> 424 425 <li class="use-case-item"> 426 <div class="use-case-item--left"> 427 <div class="use-case-thumb"> 428 <!-- 429 <div class="use-case-thumb__status use-case-thumb__status--"> 430 ---> 431 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/798c044ad7ebdf7fc1a36a6709654ea6.png" alt="use-case"> 432 <div class="use-case-thumb__status d-none"> 433 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 434 <g clip-path="url(#clip0_522_18001)"> 435 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
43511.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 436 </g> 437 <defs> 438 <clipPath id="clip0_522_18001"> 439 <rect width="16" height="16" fill="white"/> 440 </clipPath> 441 </defs> 442 </svg> 443 N/A 444 </div> 445 </div> 446 </div> 447 <div class="use-case-item--right"> 448 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/data-driven-creativity-enhancing-video-content-through-data-science"> 449 Data-Driven Creativity: Enhancing Video Content through Data Science 450</a> <div class="use-case-tag"> 451 <span class="use-case-tag__item">Data Science</span> 452 </div> 453 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 454 In the age of digital marketing and content creation, data-driven creativity is becoming an increasingly important concept. It's the fusion of artistic vision with the insights gleaned from data science to enhance the impact and effectiveness of video content. This 2500-word blog will explore how data science can be leveraged to elevate video content creation, ensuring that it not only engages but also resonates with the intended audience.Introduction to Data-Driven CreativityData-driven creativity marks a groundbreaking shift in video content creation, blending artistic vision with the insights provided by data science. This combination allows creators to break free from conventional creative limits, using data analytics to develop content that is both visually captivating and strategically significant. By delving into viewer behavior, preferences, and interactions, creators can refine their stories and visuals, achieving a deeper connection with their audience. This technique effectively transforms data into a guide for storytelling, steering content towards increased relevance and attractiveness. Consequently, video content becomes a more potent medium for engaging viewers and delivering impactful messages. Fundamentally, data-driven creativity is about converting data points into compelling stories and turning analytical insights into creative masterpieces, thereby redefining the standards of digital video content.Understanding the Role of Data in Video Content CreationExploring the Role of Data in Video Content Creation ventures into the rapidly growing realm of data-driven creativity, where data science emerges as a key instrument in enriching video content. In this realm, data transcends mere figures to become a narrative element, providing rich insights into what audiences prefer, how they behave, and emerging trends. Utilizing data, video creators can break free from conventional creative constraints, shaping their stories to more deeply connect with viewers. This process involves a detailed examination of viewer interactions, demographics, and feedback to hone storytelling skills, aiming to create videos that are not only watched but also emotionally impactful and memorable. Data-driven creativity is a fusion of art and science, where each view, reaction, and comment plays a role in directing the trajectory of video content, enhancing its relevance, engagement, and effect. This marks a transformative phase in content creation, where data equips creators to weave narratives that are not just creatively rich but also finely tuned to the dynamic preferences and interests of their audience.The Process of Gathering and Analyzing DataCollecting and analyzing data forms the foundation of data-driven creativity, especially in the realm of video content enhancement. This process involves the acquisition of key information, including audience demographics, interaction metrics, and performance measures, utilizing sophisticated tools and technologies. These range from social media analytics to advanced data mining applications designed to track a broad spectrum of viewer interactions. Once collected, this data undergoes thorough analysis to identify trends, preferences, and behaviors within the target audience. Such analysis equips content creators with insightful knowledge, allowing them to adjust their video content for greater appeal and connection with their audience. Leveraging these insights, creators can modify elements such as the tone, style, and themes of their content, revolutionizing storytelling methods and ensuring their content is both captivating and impactful. This integration of data science with creative storytelling heralds a transformative phase in video content production, where analytical findings significantly enhance artistic expression.Tailoring Content to Audience PreferencesAdapting content to audie
454nce preferences through data-driven creativity signifies a vital evolution in video content production. By incorporating data science, creators gain profound insights into audience behaviors, likes, and engagement patterns. This approach facilitates the creation of content that better resonates with viewers, ensuring everything from the plot to visual elements aligns with their interests. Utilizing analytics such as viewer habits and interaction rates, creators can pinpoint engaging aspects for better video content. Using a high-quality video editor tool is important to make the video look better. This knowledge allows precise adjustments, making the content not only captivating but also highly relevant. Ultimately, incorporating data in video content creation leads to more impactful and resonant viewer experiences, forging a deeper bond between the audience and the content.Enhancing Storytelling with Data InsightsUtilizing data insights to enhance storytelling is a groundbreaking method in video content production. Termed data-driven creativity, this technique blends the storytelling craft with data science accuracy. Content creators leverage analysis of viewer engagement, preferences, and behavior to fine-tune their narratives, ensuring a deeper connection with their audience. This integration results in not only engaging narratives but also ones that are in tune with audience interests and emerging trends. Insights from data grant a clearer understanding of what truly engages viewers, empowering creators to optimize their storytelling for the greatest effect. This modern approach reinvents traditional storytelling into an experience that's both more impactful and centered around the audience, with each creative decision being shaped and enriched by data.Using Data to Predict Future TrendsUtilizing data for future trends in data-driven creativity marks a revolutionary step in improving video content via data science. This technique focuses on analyzing viewer interactions, demographic information, and behavioral tendencies to predict future content direction. Using data enables creators to be proactive, crafting video content that resonates with emerging audience preferences and interests. Such a forward-thinking approach guarantees ongoing relevance in a dynamic digital world and fosters innovation and leadership among content creators. The blend of data analytics and artistic insight leads to the production of not just captivating but also pioneering videos, demonstrating the significant role of data in shaping the future of video content creation.Balancing Creativity and DataAchieving a harmonious blend of creativity and data in video content production is both subtle and potent. Data-driven creativity embodies the convergence of artistic flair and data analytics, providing an innovative method to boost video effectiveness. By weaving in data analysis, video creators unlock insights into what their audience prefers and how they behave, guiding their artistic choices. This integration results in content that is not only enthralling but also deeply meaningful to viewers. It is essential, however, to ensure that data serves as a guide, not a ruler, in the creative journey. This equilibrium keeps the content fresh and appealing while aligning it thoughtfully with data-driven knowledge. In essence, data-driven creativity in video content merges the narrative craft with analytical insights, culminating in videos that are both compelling and influential.Overcoming Challenges in Data-Driven CreativityOvercoming hurdles in data-driven creativity necessitates a nuanced integration of data science into the creation of video content. It involves striking a delicate balance between analytical methodologies and artistic expression, ensuring that data serves as an informative tool rather than a constraint on creativity. Accurate interpretation of data empowers content creators to avoid formulaic outputs, utilizing insights to enrich storytelling and enhance audience engagement. This intricate process demands a comprehensive understanding of the artistry of video creation and the scientific principles behind data analysis. Ethical considerations, including respecting audience privacy and obtaining data consent, are pivotal in this approach. Innovative strategies within data-driven creativity empower creators to produce content that forges deeper connections with viewers, setting new benchmarks in the digital landscape. Embracing these challenges is essential for unlocking the full potential of data-enhanced video content.Ethical Considerations in Data-Driven CreativityIn the domain of data-driven creativity, ethical considerations play a crucial role, especially when utilizing data science to enhance video content. While utilizing data insights can enhance creative processes, it is essential to address privacy concerns and ensure transparent, responsible data usage. Achieving the right equilibrium between creativity and ethical considerations becomes paramount as brands employ data to customize video content. Upholding user privacy and securing informed consent are fundamental principles in ethical data-driven creativity, fostering trust among audiences. Moreover, there is an obligation to avoid perpetuating biases and stereotypes in content creation, championing inclusivity and diversity. Ethical practices not only maintain brand integrity but also contribute to a positive and respectful digital environment for consumers.Tools and Resources for Data-Driven Video CreationExplore the potential of data-driven creativity using state-of-the-art tools and resources for crafting videos. In the current digital landscape, integrating data science and video content is transforming the landscape of creative processes. Immerse yourself in a domain where insights derived from data direct every facet of video production. These tools empower creators to customize content according to audie
454nce preferences, ensuring that each video is not only visually captivating but also strategically aligned. From scriptwriting informed by analytics to incorporating personalized visual elements, the utilization of data science takes video content to unprecedented levels. Delve into the crossroads of technology and creativity, where strategies driven by data redefine storytelling, captivating audiences in a personalized and meaningful manner.The Future of Data-Driven Creativity in Video ContentThe evolution of data-driven creativity in video content is set to transform our interaction with digital media. Through the incorporation of data science, creators gain valuable insights into viewer preferences, behavior, and trends. This collaboration enables a personalized and captivating viewing experience, heightening audience engagement. With the utilization of data-driven creativity, content producers can shape videos to suit the unique preferences of their target audience, resulting in more impactful storytelling and brand communication. As technology progresses, we anticipate a shift towards highly personalized content, driven by data insights, leading to innovative approaches in video production. This convergence of creativity and data science holds significant promise for the future development of video content within the digital landscape.ConclusionIn summary, the convergence of data-driven insights and creative components represents a transformative shift in the realm of video content creation. The fusion of Data Science and creativity provides content producers with the tools to precisely tailor videos to audience preferences, resulting in more impactful and engaging content. Leveraging the potential of data facilitates a deeper comprehension of viewer behavior, enabling targeted storytelling. Amidst the digital landscape, the symbiosis of data and creativity not only elevates video content but also fosters innovation and personalized experiences. Looking ahead, embracing Data-Driven Creativity becomes crucial for maintaining a leading edge in the continually evolving landscape of video content creation. 455 </div> 456 <div class="use-case-divider"></div> 457 <span class="use-case__time"> 458 8 minutes read 459 </span> 460 </div> 461</li> 462 463 <li class="use-case-item"> 464 <div class="use-case-item--left"> 465 <div class="use-case-thumb"> 466 <!-- 467 <div class="use-case-thumb__status use-case-thumb__status--"> 468 ---> 469 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/26acdc97d668602456b558732675f3d6.jpg" alt="use-case"> 470 <div class="use-case-thumb__status d-none"> 471 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 472 <g clip-path="url(#clip0_522_18001)"> 473 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
47311.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 474 </g> 475 <defs> 476 <clipPath id="clip0_522_18001"> 477 <rect width="16" height="16" fill="white"/> 478 </clipPath> 479 </defs> 480 </svg> 481 N/A 482 </div> 483 </div> 484 </div> 485 <div class="use-case-item--right"> 486 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/the-role-of-ai-in-unstructured-data-mining-challenges-and-opportunities"> 487 The Role of AI in Unstructured Data Mining: Challenges and Opportunities 488</a> <div class="use-case-tag"> 489 <span class="use-case-tag__item">Machine Learning</span> 490 </div> 491 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 492 In our fast-paced digital world, we're producing staggering volumes of data every day. This data falls into two key categories: structured, known for its order and efficiency, and unstructured, a captivating puzzle brimming with untapped potential.In this article, we will uncover how AI confronts the complexities of unstructured data, the hurdles it faces, and the intriguing opportunities it opens up to businesses from any kind of industry.Understanding Unstructured DataUnstructured data mining is the technique of extracting valuable and meaningful insights from an abundant well of unstructured data. It uncovers hidden gems of knowledge, making it a crucial pursuit in our data-rich era.In today's digital realm, unstructured data is generated in unprecedented quantities. Billions of text documents, images, and videos come to life daily, creating a treasure trove of information just waiting for organizations to explore.Unlocking the insights hidden within unstructured data can provide organizations with a competitive edge. This data can reveal customer sentiments, emerging trends, and valuable feedback that might otherwise go unnoticed.The Basics of Data MiningHow data mining works is that it discovers patterns, trends, and valuable information within a dataset. It involves various techniques to extract knowledge from raw data. While it's exceptionally effective with structured data, applying data mining to unstructured data requires a unique set of skills and tools.Unstructured Data MiningUnstructured data mining is a method focused on the extraction of valuable information from the vast, unstructured data available. This process uncovers hidden insights, making it a valuable endeavor in today's data-driven world.The AI RevolutionThe AI revolution has given rise to an exciting era of possibilities in unstructured data mining. AI's remarkable capabilities are instrumental in taming the unstructured data landscape, and it involves a multitude of components, including:Machine learning enables AI systems to learn from data, make predictions, and identify patterns, enhancing data mining capabilities.Deep learning uses neural networks to model complex patterns in unstructured data, which is particularly valuable in image and speech recognition.Sentiment analysis gauges emotional tones within textual data, helping to understand public opinion and tailor strategies.Pattern recognition identifies recurring structures in data, aiding in image processing and text mining.Knowledge graphs structure data relationships, improving contextual understanding and data retrieval.Anomaly detection identifies outliers in data, which is essential for fraud detection and data security.Challenges in Unstructured Data MiningAs promising as AI is at handling unstructured data, it's not without its set of challenges. Here, we delve into some of the major hurdles:Data QualityUnstructured data is inherently messy. It's laden with errors, inconsistencies, and biases, which makes it a challenge to extract meaningful insights from this data. AI systems need to be trained rigorously to navigate and decipher this diversity in data quality. Techniques like data cleansing, normalization, and the use of context are essential in ensuring that AI systems provide accurate results.ScalabilityAs the volume of unstructured data grows, AI systems must scale to handle the data influx effectively. Traditional hardware and algorithms might not be sufficient to handle this data influx. Scalable infrastru
492cture and distributed computing become crucial to ensuring that AI systems can process and analyze vast amounts of data efficiently.Privacy ConcernsMining unstructured data often raises ethical questions regarding privacy and data protection. Thatâs why itâs essential to strike the right balance between data utilization and respecting individual privacy. It's a challenge to ensure that AI systems are used responsibly and in compliance with data protection laws and regulations, such as GDPR in Europe. Techniques like anonymization and consent management play a vital role in addressing these privacy concerns.Opportunities and ApplicationsAI's role in unstructured data mining has opened up a world of opportunities across various industries. Let's explore some of the most promising applications:Customer InsightsUnstructured data, particularly sourced from social media and customer reviews, serves as a goldmine of information on customer behavior and preferences. By leveraging AI algorithms, companies can analyze sentiments, spot emerging trends, and even forecast future buying patterns. With these insights, they can fine-tune their marketing strategies, product development, and customer service to align with their ever-evolving audience's demands.Healthcare DiagnosisThe abundance of unstructured data found in medical records, radiological images, and wearable device data holds the key to transformative advancements. AI-powered systems, known for their proficiency in the analysis of this data, not only facilitate early disease detection but also provide highly individualized treatment plans, ultimately raising the standard of patient care. For example: AI expedites the process of analyzing medical images for anomalies, resulting in a significant reduction in the time required for diagnosing and treating severe conditions.Fraud DetectionWhen it comes to financial institutions, AI is a vital tool for exposing fraudulent activities that often hide within the vast volumes of unstructured transaction data. Through a meticulous examination of transaction patterns and anomalies, AI systems can rapidly pinpoint fraudulent actions, providing businesses with a robust defense against significant financial losses. The ability to detect and thwart fraud in real-time provides a critical advantage, resulting in annual savings of billions of dollars for businesses.ConclusionThe future belongs to those who embrace the AI revolution in unstructured data mining. In this future, data isn't just information; it's the key to success. So, let's move forward, embracing this tomorrow, where possibilities are limitless and opportunities are endless. 493 </div> 494 <div class="use-case-divider"></div> 495 <span class="use-case__time"> 496 5 minutes read 497 </span> 498 </div> 499</li> 500 501 <li class="use-case-item"> 502 <div class="use-case-item--left"> 503 <div class="use-case-thumb"> 504 <!-- 505 <div class="use-case-thumb__status use-case-thumb__status--"> 506 ---> 507 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/8520a2e0d97e18f0e00acb73a00d3e38.jpg" alt="use-case"> 508 <div class="use-case-thumb__status d-none"> 509 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 510 <g clip-path="url(#clip0_522_18001)"> 511 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
51111.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 512 </g> 513 <defs> 514 <clipPath id="clip0_522_18001"> 515 <rect width="16" height="16" fill="white"/> 516 </clipPath> 517 </defs> 518 </svg> 519 N/A 520 </div> 521 </div> 522 </div> 523 <div class="use-case-item--right"> 524 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/model-evaluation-metrics-in-machine-learning"> 525 Model Evaluation Metrics in Machine Learning 526</a> <div class="use-case-tag"> 527 <span class="use-case-tag__item">Data Science</span> 528 <span class="use-case-tag__item">Machine Learning</span> 529 </div> 530 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 531 CreditsPredictive models have become a trusted advisor to many businesses and for a good reason. These models can âforesee the futureâ, and there are many different methods available, meaning any industry can find one that fits their particular challenges.When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). In classification problems, we use two types of algorithms (dependent on the kind of output it creates):Class output: Algorithms like SVM and KNN create a class output. For instance, in a binary classification problem, the outputs will be either 0 or 1. However, today we have algorithms that can convert these class outputs to probability.Probability output: Algorithms like Logistic Regression, Random Forest, Gradient Boosting, Adaboost, etc. give probability outputs. Converting probability outputs to class output is just a matter of creating a threshold probability.IntroductionWhile data preparation and training a machine learning model is a key step in the machine learning pipeline, itâs equally important to measure the performance of this trained model. How well the model generalizes on the unseen data is what defines adaptive vs non-adaptive machine learning models.By using different metrics for performance evaluation, we should be in a position to improve the overall predictive power of our model before we roll it out for production on unseen data.Without doing a proper evaluation of the ML model using different metrics, and depending only on accuracy, it can lead to a problem when the respective model is deployed on unseen data and can result in poor predictions.This happens because, in cases like these, our models donât learn but instead memorize;hence, they cannot generalize well on unseen data.Model Evaluation MetricsLet us now define the evaluation metrics for evaluating the performance of a machine learning model, which is an integral component of any data science project. It aims to estimate the generalization accuracy of a model on the future (unseen/out-of-sample) data.Confusion MatrixA confusion matrix is a matrix representation of the prediction results of any binary testing that is often used to describe the performance of the classification model (or âclassifierâ) on a set of test data for which the true values are known.The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.Confusion matrix with 2 class labels.Each prediction can be one of the four outcomes, based on how it matches up to the actual value:True Positive (TP): Predicted True and True in reality.True Negative (TN): Predicted False and False in reality.False Positive (FP): Predicted True and False in reality.False Negative (FN): Predicted False and True in reality.Now let us understand this concept using hypothesis testing.A Hypothesis is speculation or theory based on insufficient evidence that lends itself to further testing and experimentation. With further testing, a hypothesis can usually be proven true or false.A Null Hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove.We would always reject the null hypothesis when it is false, and we would accept the null hypothesis when it is indeed true.Even though hypothesis tests are meant to be reliable, there are two types of errors that can occur.These errors are known as Type 1 and Type II errors.For example, when examining the effectiveness of a drug, the null hypothesis would be that the drug does not affect a disease.Type I Error:- equivalent to False Positives(FP).The first kind of error that is possible involves the rejection of a null hypothesis that is true.Letâs go back to the example of a drug being used to treat a disease. If we reject the null hypothesis in this situation, then we claim that the drug does have some effect on a disease. But if the null hypothesis is true, then, in reality, the drug does not combat the disease at all. The drug is falsely claimed to have a positive effect on a disease.Type II Error:- equivalent to False Negatives(FN).The other kind of error that occurs when we accept a false null hypothesis. This sort of error is called a type II error and is also referred to as an error of the second kind.If we think back again to the scenario in which we are testing a drug, what would a type II error look like? A type II error would occur if we accepted that the drug hs no effect on disease, but in reality, it did.A sample python implementation of the Confusion matrix.import warnings import pandas as pd from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt %matplotlib inline   #ignore warnings warnings.filterwarnings('ignore') # Load digits dataset url = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" df = pd.read_csv(url) # df = df.values X = df.iloc[:,0:4] y = df.iloc[:,4] #test size test_size = 0.33 #generate the same set of random numbers seed = 7 #Split data into train and test set. X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) #Train Model model = LogisticRegression() model.fit(X_train, y_train) pred = model.predict(X_test)  #Construct the Confusion Matrix labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'] cm = confusion_matrix(y_test, pred, labels) print(cm) fig = plt.figure() ax = fig.add_subplot(111) cax = ax.matshow(cm) plt.title('Confusion matrix') fig.colorbar(cax) ax.set_xticklabels([''] + labels) ax.set_yticklabels([''] + labels) plt.xlabel('Predicted Values') plt.ylabel('Actual Values') plt.show()Confusion matrix with 3 class labels.The diagonal elements represent the number of points for which the predicted label is equal to the true label, while anything off the diagonal was mislabeled by the classifier. Therefore, the higher the diagonal values of the confusion matrix the better, indicating many correct predictions.In our case, the classifier predicted all the 13 setosa and 18 virginica plants in the test data perfectly. However, it incorrectly classified 4 of the versicolor plants as virginica.There is also a list of rates that are often computed from a confusion matrix for a binary classifier:1. AccuracyOverall, how often is the classifier correct?Accuracy = (TP+TN)/totalWhen our classes are roughly equal in size, we can use accuracy, which will give us correctly classified values.Accuracy is a common evaluation metric for classification problems. Itâs the number of correct predictions made as a ratio of all predictions made.Misclassification Rate(Error Rate): Overall, how often is it wrong. Since accuracy is the percent we correctly classified (success rate), it follows that our error rate (the percentage we got wrong) can be calculated as follows:Misclassification Rate = (FP+FN)/totalWe use the sklearn module to compute the accuracy of a classification task, as shown below.#import modules import warnings import pandas as pd import numpy as np from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn import datasets from sklearn.metrics import accuracy_score #ignore warnings warnings.filterwarnings('ignore') # Load digits dataset iris = datasets.load_iris() # # Create feature matrixÂ
531X = iris.data # Create target vector y = iris.target #test size test_size = 0.33 #generate the same set of random numbers seed = 7 #cross-validation settings kfold = model_selection.KFold(n_splits=10, random_state=seed) #Model instance model = LogisticRegression() #Evaluate model performance scoring = 'accuracy' results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring) print('Accuracy -val set: %.2f%% (%.2f)' % (results.mean()*100, results.std()))  #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) #fit model model.fit(X_train, y_train) #accuracy on test set result = model.score(X_test, y_test) print("Accuracy - test set: %.2f%%" % (result*100.0))The classification accuracy is 88% on the validation set.2. PrecisionWhen it predicts yes, how often is it correct?Precision=TP/predicted yesWhen we have a class imbalance, accuracy can become an unreliable metric for measuring our performance. For instance, if we had a 99/1 split between two classes, A and B, where the rare event, B, is our positive class, we could build a model that was 99% accurate by just saying everything belonged to class A. Clearly, we shouldnât bother building a model if it doesnât do anything to identify class B; thus, we need different metrics that will discourage this behavior. For this, we use precision and recall instead of accuracy.3. Recall or SensitivityWhen itâs actually yes, how often does it predict yes?True Positive Rate = TP/actual yesRecall gives us the true positive rate (TPR), which is the ratio of true positives to everything positive.In the case of the 99/1 split between classes A and B, the model that classifies everything as A would have a recall of 0% for the positive class, B (precision would be undefined â 0/0). Precision and recall provide a better way of evaluating model performance in the face of a class imbalance. They will correctly tell us that the model has little value for our use case.Just like accuracy, both precision and recall are easy to compute and understand but require thresholds. Besides, precision and recall only consider half of the confusion matrix:4. F1 ScoreThe F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.Why harmonic mean? Since the harmonic mean of a list of numbers skews strongly toward the least elements of the list, it tends (compared to the arithmetic mean) to mitigate the impact of large outliers and aggravate the impact of small ones.An F1 score punishes extreme values more. Ideally, an F1 Score could be an effective evaluation metric in the following classification scenarios:When FP and FN are equally costly â meaning they miss on true positives or find false positives â both impact the model almost the same way, as in our cancer detection classification exampleAdding more data doesnât effectively change the outcome effectivelyTN is high (like with flood predictions, cancer predictions, etc.)A sample python implementation of the F1 score.import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss from sklearn.metrics import precision_recall_fscore_support as score, precision_score, recall_score, f1_score  warnings.filterwarnings('ignore')  url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] test_size = 0.33 seed = 7  model = LogisticRegression() #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train) precision = precision_score(y_test, pred) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(y_test, pred) print('Recall: %f' % recall) # f1: tp / (tp + fp + fn) f1 = f1_score(y_test, pred) print('F1 score: %f' % f1)5. SpecificityWhen itâs no, how often does it predict no?True Negative Rate=TN/actual noIt is the true negative rate or the proportion of true negatives to everything that should have been classified as negative.Note that, together, specificity and sensitivity consider the full confusion matrix:6. Receiver Operating Characteristics (ROC) CurveMeasuring the area under the ROC curve is also a very useful method for evaluating a model. By plotting the true positive rate (sensitivity) versus the false-positive rate (1 â specificity), we get the Receiver Operating Characteristic (ROC) curve. This curve allows us to visualize the trade-off between the true positive rate and the false positive rate.The following are examples of good ROC curves. The dashed line would be random guessing (no predictive value) and is used as a baseline; anything below that is considered worse than guessing. We want to be toward the top-left corner:A sample python implementation of the ROC curves.#Classification Area under curve import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import roc_auc_score, roc_curve  warnings.filterwarnings('ignore')  url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] seed = 7 #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train)  # predict probabilities probs = model.predict_proba(X_test) # keep probabilities for the positive outcome only probs = probs[:, 1]  auc = roc_auc_score(y_test, probs) print('AUC - Test Set: %.2f%%' % (auc*100))  # calculate roc curve fpr, tpr, thresholds = roc_curve(y_test, probs) # plot no skill plt.plot([0, 1], [0, 1], linestyle='--') # plot the roc curve for the model plt.plot(fpr, tpr, marker='.') plt.xlabel('False positive rate') plt.ylabel('Sensitivity/ Recall') # show the plot plt.show()In the example above, the AUC is relatively close to 1 and greater than 0.5. A perfect classifier will have the ROC curve go along the Y-axis and then along the X-axisLog LossLog Loss is the most important classification metric based on probabilities.As the predicted probability of the true class gets closer to zero, the loss increases exponentially:It measures the performance of a classification model where the prediction input is a probability value between 0 and 1. Log loss increases as the predicted probability diverge from the actual label. The goal of any machine learning model is to minimize this value. As such, smaller log loss is better, with a perfect model having a log loss of 0.A sample python implementation of the Log Loss.#Classification LogLoss import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss  warnings.filterwarnings('ignore') url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] seed = 7 #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train) #predict and compute logloss pred = model.predict(X_test) accuracy = log_loss(y_test, pred) print("Logloss: %.2f" % (accuracy))Logloss: 8.02 532Jaccard IndexJaccard Index is one of the simplest ways to calculate and find out the accuracy of a classification ML model. Letâs understand it with an example. Suppose we have a labeled test set, with labels as ây = [0,0,0,0,0,1,1,1,1,1]And our model has predicted the labels as ây1 = [1,1,0,0,0,1,1,1,1,1]The above Venn diagram shows us the labels of the test set and the labels of the predictions, and their intersection and union.Jaccard Index or Jaccard similarity coefficient is a statistic used in understanding the similarities between sample sets. The measurement emphasizes the similarity between finite sample sets and is formally defined as the size of the intersection divided by the size of the union of the two labeled sets, with formula as âJaccard Index or Intersection over Union(IoU)So, for our example, we can see that the intersection of the two sets is equal to 8 (since eight values are predicted correctly) and the union
532is 10 + 10â8 = 12. So, the Jaccard index gives us the accuracy as âSo, the accuracy of our model, according to Jaccard Index, becomes 0.66, or 66%.Higher the Jaccard index higher the accuracy of the classifier.A sample python implementation of the Jaccard index.import numpy as np def compute_jaccard_similarity_score(x, y):   intersection_cardinality = len(set(x).intersection(set(y)))   union_cardinality = len(set(x).union(set(y)))   return intersection_cardinality / float(union_cardinality)  score = compute_jaccard_similarity_score(np.array([0, 1, 2, 5, 6]), np.array([0, 2, 3, 5, 7, 9])) print "Jaccard Similarity Score : %s" %score passJaccard Similarity Score : 0.375Kolomogorov Smirnov chartK-S or Kolmogorov-Smirnov chart measures the performance of classification models. More accurately, K-S is a measure of the degree of separation between positive and negative distributions.The cumulative frequency for the observed and hypothesized distributions is plotted against the ordered frequencies. The vertical double arrow indicates the maximal vertical difference.The K-S is 100 if the scores partition the population into two separate groups in which one group contains all the positives and the other all the negatives. On the other hand, If the model cannot differentiate between positives and negatives, then it is as if the model selects cases randomly from the population. The K-S would be 0.In most classification models the K-S will fall between 0 and 100, and that the higher the value the better the model is at separating the positive from negative cases.The K-S may also be used to test whether two underlying one-dimensional probability distributions differ. It is a very efficient way to determine if two samples are significantly different from each other.A sample python implementation of the Kolmogorov-Smirnov.from scipy.stats import kstest import random   # N = int(input("Enter number of random numbers: ")) N = 10   actual =[] print("Enter outcomes: ")   for i in range(N):   # x = float(input("Outcomes of class "+str(i + 1)+": "))   actual.append(random.random())   print(actual) x = kstest(actual, "norm")   print(x)The Null hypothesis used here assumes that the numbers follow the normal distribution. It returns statistics and p-value. If the p-value is < alpha, we reject the Null hypothesis.Alpha is defined as the probability of rejecting the null hypothesis given the null hypothesis(H0) is true. For most of the practical applications, alpha is chosen as 0.05.Gain and Lift ChartGain or Lift is a measure of the effectiveness of a classification model calculated as the ratio between the results obtained with and without the model. Gain and lift charts are visual aids for evaluating the performance of classification models. However, in contrast to the confusion matrix that evaluates models on the whole population gain or lift chart evaluates model performance in a portion of the population.The higher the lift (i.e. the further up it is from the baseline), the better the model.The following gains chart, run on a validation set, shows that with 50% of the data, the model contains 90% of targets, Adding more data adds a negligible increase in the percentage of targets included in the model.Gain/lift chartLift charts are often shown as a cumulative lift chart, which is also known as a gains chart. Therefore, gains charts are sometimes (perhaps confusingly) called âlift chartsâ, but they are more accurately cumulative lift charts.It is one of their most common uses is in marketing, to decide if a prospective client is worth calling.Gini CoefficientThe Gini coefficient or Gini Index is a popular metric for imbalanced class values. The coefficient ranges from 0 to 1 where 0 represents perfect equality and 1 represents perfect inequality. Here, if the value of an index is higher, then the data will be more dispersed.Gini coefficient can be computed from the area under the ROC curve using the following formula:Gini Coefficient = (2 * ROC_curve) â 1ConclusionUnderstanding how well a machine learning model is going to perform on unseen data is the ultimate purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced and thereâs a class disparity, then other methods like ROC/AUC, Gini coefficient perform better in evaluating the model performance.Well, this concludes this article. I hope you guys have enjoyed reading it, feel free to share your comments/thoughts/feedback in the comment section.Thanks for reading !!! 533 </div> 534 <div class="use-case-divider"></div> 535 <span class="use-case__time"> 536 16 minutes read 537 </span> 538 </div> 539</li> 540 541 <li class="use-case-item"> 542 <div class="use-case-item--left"> 543 <div class="use-case-thumb"> 544 <!-- 545 <div class="use-case-thumb__status use-case-thumb__status--"> 546 ---> 547 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/68ba79548b8ad4787030bea930e992f9.jpg" alt="use-case"> 548 <div class="use-case-thumb__status d-none">
549 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 550 <g clip-path="url(#clip0_522_18001)"> 551 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769 11.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 552 </g> 553 <defs> 554 <clipPath id="clip0_522_18001"> 555 <rect width="16" height="16" fill="white"/> 556 </clipPath> 557 </defs> 558 </svg> 559 N/A 560 </div> 561 </div> 562 </div> 563 <div class="use-case-item--right"> 564 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/datasource-ai-hosts-ktm-ag-s-inaugural-ai-challenge-code-the-light-fantastic"> 565 DataSource AI Hosts KTM AG's Inaugural AI Challenge: "Code the Light Fantastic" 566</a> <div class="use-case-tag"> 567 <span class="use-case-tag__item">Data Science</span> 568 </div> 569 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 570 DataSource AI announces the launch of the KTM AG inaugural AI Challenge, an unprecedented 3-month online competition that aims to revolutionise two-wheeler innovation through artificial intelligence and deep learning. KTM AG is a global frontrunner in two-wheeler innovation, known for pushing the boundaries of what's possible in the world of motorcycles. With a rich history of groundbreaking engineering and a commitment to cutting-edge technology, KTM AG has set new standards in performance, design, and safety. As a global leader in two-wheeler innovation, KTM AG invites participants to embark on this groundbreaking innovation journey. At the core of this competition lies a challenge set to redefine the future of motorcycle lighting systems. Participants are tasked with developing an algorithm for a high-beam lighting system utilizing a pixel matrix. Participants can find detailed guidelines in the Datathon competition. The datathon unfolds in a 3-tiered cascade model: This Code Challenge by KTM AG promises not only substantial rewards but also an exciting opportunity to shape the future of two-wheeler technology, along with supporting the participants to upscale and test their knowledge in a global AI competition. The cumulative budget for this remarkable Code Challenge by KTM AG is a substantial â¬24,000, motivating participants with not only the opportunity to push the boundaries of two-wheeler technology but also significant rewards for those who rise to the occasion. With cumulative prizes, contestants have the chance to potentially take home a maximum reward of â¬10,800 in addition to contributing to cutting-edge advancements in the field.  We invite all aspiring innovators, data scientists, and AI enthusiasts to join us in this journey to "Code the Light Fantastic." For more information, rules, and registration details, please register hereAbout DataSource:  At DataSource AI, we are driven by a singular mission - to democratise the immense power of data science and AI/ML for businesses of all sizes and budgets. We facilitate AI competitions, for businesses of all sizes and budgets by harnessing our extensive data expert community that's collaborating over our intelligent AI algorithm crowdsourcing platform. Our community is at the heart of what we do. We've built a diverse and talented pool of data experts who are passionate about solving real-world problems. They collaborate, ideate, and innovate, driving forward the frontiers of data science.  571 </div> 572 <div class="use-case-divider"></div> 573 <span class="use-case__time"> 574 2 minutes read 575 </span> 576 </div> 577</li> 578 579 <li class="use-case-item"> 580 <div class="use-case-item--left"> 581 <div class="use-case-thumb"> 582 <!-- 583 <div class="use-case-thumb__status use-case-thumb__status--"> 584 ---> 585 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/4aca7a99d5eb8aa4d5002ddb4d7c3b84.jpg" alt="use-case"> 586 <div class="use-case-thumb__status d-none">
587 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 588 <g clip-path="url(#clip0_522_18001)"> 589 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769 11.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 590 </g> 591 <defs> 592 <clipPath id="clip0_522_18001"> 593 <rect width="16" height="16" fill="white"/> 594 </clipPath> 595 </defs> 596 </svg> 597 N/A 598 </div> 599 </div> 600 </div> 601 <div class="use-case-item--right"> 602 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/top-2-online-data-science-courses-to-improve-your-career-in-2023"> 603 Top 2 Online Data Science Courses to Improve your Career in 2023 604</a> <div class="use-case-tag"> 605 <span class="use-case-tag__item">Data Science</span> 606 </div> 607 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 608 The discipline of Data Science is expanding quickly and has enormous promise. It is used in various sectors, including manufacturing, retail, healthcare, and finance. Today, a wide variety of online Data Science courses are accessible. With so many choices, you might need help choosing the best one. This article will summarize the best Data Science programs so you can choose the program that's best for you. What Is a Data Science Course?The theoretical ideas of data science are taught to novices in a Data Science course. Additionally, you'll learn about the steps involved in Data Science, such as mathematical and statistical analysis, data preparation & staging, data interpretation, data visualization, and methods for presenting data insights in an organizational context. Advanced subjects, such as employing neural networks to develop recommendation engines, are covered in more specialized courses.Why Data Science?In the expanding field of data science, a data scientist earns one of the best jobs. Data science gained popularity and started to be utilised in an expanding number of applications when big data appeared and the necessity to manage these massive volumes of data arose. Data science, which enables companies to derive conclusions on the basis and take measures based on those conclusions, is one of the primary applications of artificial intelligence. Data Scientists are in great demand due to Data Science's importance for all industries. There is fierce rivalry everywhere. But if you can get an advantage over your competitors, you may easily land lucrative positions in demand. Taking data science courses online might give you that advantage. Data science involves:â   Analytical capabilities.â   A foundational understanding of the field.â   Practical abilities to produce outcomes. To understand data science, you don't need to spend years working with big data or have a tonne of expertise in the softw
608are sector. You may always study from the greatest online Data Science courses and create a way to join this area while working. These are the top data science programs you can take to further your career and understand the subject. Let's discover more about the top data science courses available online. 2 Online Data Science Courses for 2023 to Advance Your Career1.  Program for Business Analytics CertificationThis online Data Science course lasts three months and calls for 8 to 10 hours of study per week. The course created for analytics aspirants is one of the greatest data science courses in India and one of the market's top data science courses. It has more than 100 hours of material. The Data Science course was built with the help of business professionals from organizations like Flipkart, Gardener, and Actify. This is one of the finest online courses for learning the fundamentals of data science since it offers committed mentor assistance, prompt doubt resolution services, and live sessions with subject matter specialists. Students will gain knowledge in statistics, optimization, business problem-solving, and predictive modeling via this course. This online data science course was created for managers, engineers, recent graduates, software and IT workers, and marketing and salespeople. Students will concentrate on corporate problem-solving, insights, and narrative for the very first 3 weeks of the course. In this portion, you will discover how to formulate hypotheses, comprehend business issues, and concentrate on narrative. The following four weeks will be devoted to understanding statistics, optimization, and exploratory data analysis. A case study assignment will also be included. You will study several machine learning approaches to evaluate data and provide insights during the last five weeks, which will be devoted to predictive analysis. There will be three initiatives at the industry level: uber supply-demand gap, customer creditworthiness, and market mix modeling for e-commerce. Students who take this business analyst course have access to various options, including the ability to apply for managerial, business analyst, and data analyst employment. 2.  Data Science Master's DegreeIt is among the top online courses for Data Science. This masterâs in Data Science program lasts for 18 months and is delivered online. If you engage in expert online data science courses from a recognised and trusted provider, you may put your talents to the test on real assignments.This course is one of the best Data Science courses since it offers a variety of distinctive characteristics. There are several specialization options available for the course. Business intelligence/data analytics, Natural Language Processing, and Deep Learning,Business Analyst course, and Data Engineering are the available specialization areas. In addition to these specializations, the course offers its students a platform to study more than 14 programming languages and technologies that are utilized in the diverse area of data science, as well as industry mentoring and committed career assistance. One of the greatest data science courses in India, it includes tools like Python, Tableau, Hadoop, MySQL, Hive, Excel, PowerBI, MongoDB, Shiny, Keras, TensorFlow, PySpark, HBase, and Apache Airflow. More than 400 hours of learning material are planned for the online data science course. You will get a thorough understanding of data science and topics linked to it through these videos and publications, enabling you to succeed in any data science interviews. For the students to have a practical and hands-on understanding of all the tools and ideas covered in the course, the online data course includes more than ten industrial projects and case studies. The students may study various subjects, languages, and tools throughout the course. The first four weeks will be devoted to learning the fundamentals of Python and how to use Excel to deal with data. The 11 weeks will be devoted to teaching students how to utilize all the tools needed for data science and how to prepare and work with the provided data. You will acquire in-depth information on Python, Excel, and SQL in this part. Learning about machine learning and its many algorithms will be the main emphasis of the next nine weeks. ConclusionThe best online Data Science courses provide a good introduction to the subject, which is how the article can be summed up. They go through the fundamentals of data science, such as handling data, cleansing data, and doing statistical analysis. They also provide a more thorough examination of Data Science and machine learning Anyone interested in pursuing a career in data science must take these courses.    609 </div> 610 <div class="use-case-divider"></div> 611 <span class="use-case__time"> 612 5 minutes read 613 </span> 614 </div> 615</li> 616 617 <li class="use-case-item"> 618 <div class="use-case-item--left"> 619 <div class="use-case-thumb"> 620 <!-- 621 <div class="use-case-thumb__status use-case-thumb__status--"> 622 ---> 623 <img class="use-case-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/c4b3284533f1c88915c89c4cb3c2d254.png" alt="use-case"> 624 <div class="use-case-thumb__status d-none">
625 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 626 <g clip-path="url(#clip0_522_18001)"> 627 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769 11.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 628 </g> 629 <defs> 630 <clipPath id="clip0_522_18001"> 631 <rect width="16" height="16" fill="white"/> 632 </clipPath> 633 </defs> 634 </svg> 635 N/A 636 </div> 637 </div> 638 </div> 639 <div class="use-case-item--right"> 640 <a class="d-block use-case-item__title no-style overflow-ellipsis" href="/en/data-science-articles/5-tips-to-ace-your-job-interview-for-a-data-scientist-opening"> 641 5 Tips To Ace Your Job Interview For A Data Scientist Opening 642</a> <div class="use-case-tag"> 643 <span class="use-case-tag__item">Data Science</span> 644 </div> 645 <div class="use-case__desc text-ellipsis-2" style="margin-bottom:16px;"> 646 5 Tips To Ace Your Job Interview For A Data Scientist Opening.PNG 795.94 KBImage SourceAspiring data scientists have a bright future ahead of them. Theyâre about to enter a field thatâs exponentially expanding in terms of job growth and career opportunities. Reports say that the sector has seen a 650% job growth since 2012 and, according to predictions, there will be an estimated 11.5 million new jobs by 2026. All thatâs left for data scientist hopefuls is to develop their skills and ace their job interviews. While that may be the most daunting part, we're here to give you five tips on how to impress your interviewer and grab that opportunity. 647 648#1- Prepare answers for potential interview questions 649 650In our list of 10 highly probable data scientist interview questions, we highlight some of the most asked questions that youâll want to prepare for. These include situations related to machine learning, Python, and SQL. For example, you could get asked about the difference between classification and clustering, or the important features of dictionaries. While these questions can depend on your interviewer and the company youâre trying for, it wonât hurt to have prepared answers for these basic questions. Brush up on these topics and do your own research. 651 652#2- Recall your technical abilities 653 654Companies often have a separate technical screening portion prepared for you, but it would also be helpful to run over your technical abilities. This also depends on the specifications of the position youâre applying for; as a data scientist, they might inquire about your efficiency in developing algorithms for the collation and cleaning of datasets. Your interviewer could ask if youâve had the chance to create an original algorithm of your own. If youâve done any data projects, they might also inquire about the challenges you faced and how you were able to deal with them. 655 656#3- Communicate your strengths 657 658It would be helpful if you could confidently explain and articulate what kind of data scientist you are. To do this, you must know where your strengths lie and what your niche is. In any job interview, companies often ask about an applicantâs strengths because the way they approach this question says a lot about them. Think about what you could contribute to a team and what type of role you see yourself thriving in. Then, figure out a way to communicate why you think your unique strengths are an asset to the company. 659 660#4- When prompted, ask questions of your own 661 662Interviewers love when an applicant shows engagement and interest in the company. Throughout the process, jot down the questions that might come to you, and donât be afraid to ask away when prompted. The questions you ask in an interview could be a chance for you to learn more about your potential employer and the work environment. You could ask them simple questions like, âwhat is the most enjoyable part of working here?â, or âwhat are the companyâs goals over the next few years?â This shows them your passion and dedication for the role. 663 664#5- Stay updated on trends in the data science space 665 666The data science industry is always changing, and thereâs always something new to learn every day. If you want to gain an edge against your competitors, make sure youâre on top of the latest data science trends and news. One way to do so is to always be on the lookout for upcoming data science conferences and seminars you can attend. Attending events could earn you connections and help you learn things you wonât find in textbooks. You could also do some supplementary reading of the latest research papers. This will show your interviewers that youâre a motivated self-starter.With time, effort, and these five tips in mind, youâll be ready to answer any question thrown at you. Interviews are just the first step towards the career of your dreams, so make sure you prepare for every opportunity presented to you. 667 </div> 668 <div class="use-case-divider"></div> 669 <span class="use-case__time"> 670 3 minutes read 671 </span> 672 </div> 673</li> 674 675</ul>
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681 682 <div class="d-flex align-center justify-content-center mt-6"> 683 <a class="new-btn-gray" data-method="get" href="/en/data-science-articles"> 684 <span>See all</span> 685 <svg width="21" height="21" viewBox="0 0 21 21" fill="none" xmlns="http://www.w3.org/2000/svg"> 686 <path d="M17.6491 9.61698L11.8158 3.78365C11.4987 3.46806 11.0373 3.34561 10.6055 3.46241C10.1737 3.57921 9.83693 3.91752 9.72216 4.34991C9.60739 4.78229 9.73201 5.24306 10.0491 5.55865L13.7408 9.24198H4.23242C3.54207 9.24198 2.98242 9.80162 2.98242 10.492C2.98242 11.1823 3.54207 11.742 4.23242 11.742H13.7408L10.0491 15.4253C9.69205 15.7828 9.58532 16.32 9.77861 16.7868C9.9719 17.2537 10.4272 17.5582 10.9324 17.5586C11.2679 17.5537 11.5868 17.4122 11.8158 17.167L17.6491 11.3336C18.1365 10.8456 18.1365 10.055 17.6491 9.56698V9.61698Z" fill="#080A25"/> 687 </svg> 688</a></div> 689 690</section> 691 692 693<section class="container competition" id="top-competition"> 694 <h2 class="competition-title text-center mb-2"> 695 Top data science competitions 696 </h2> 697 <p class="competition-description text-center mx-auto">Browse winning solutions created by our competitors. Learn, discuss, and 10x your skills with us!</p> 698 <ul class="competition-list competition-list--grid"> 699 <li class="competition-item"> 700 <div class="competition-item--left" style="gap: 12px;"> 701 <div class="competition-thumb"> 702 <img class="competition-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/471ff43a067cd1f8fb871d9ee4e5e377.jpg" /> 703 <div class="competition-thumb__status competition-thumb__status--dark"> 704 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 705 <g clip-path="url(#clip0_522_18001)"> 706 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
70611.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 707 </g> 708 <defs> 709 <clipPath id="clip0_522_18001"> 710 <rect width="16" height="16" fill="white"/> 711 </clipPath> 712 </defs> 713 </svg> 714 Completed 715</div> 716 717 </div> 718 <a class="competition-host ff-main d-flex align-items-center" href="/en/company_detail/2"> 719 <img class="avatar-compe-landingpage competition-host__thumb mr-1" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/64c117f7f5ee7ebe158ef2ec8969f638.jpg" /> 720 <span style="max-width: 151px;" class="competition-host__name overflow-ellipsis">DataSource.AI</span> 721</a> </div> 722 <div class="competition-item--right"> 723 <a data-turbolinks="false" class="d-block competition-item__title ff-main overflow-ellipsis" href="/en/home/data-science-competitions-for-startups/vn2-inventory-planning-challenge/description"> 724 VN2 Inventory Planning Challenge 725</a> <div class="competition-tag ff-sub"> 726 <span class="competition-tag__item">E-commerce/Retail</span> 727 </div> 728 <div class="competition-description"> 729 <p class="competition__desc text-ellipsis-2">Test your inventory planning skills: cut holding costs, prevent shortages, and master real-world inventory planning.</p> 730 </div> 731 <div class="competition-extra"> 732 <p class="competition-extra__price ff-main"> 733 18,000 734 EURO 735 </p> 736 </div> 737 </div> 738</li> 739 <li class="competition-item"> 740 <div class="competition-item--left" style="gap: 12px;"> 741 <div class="competition-thumb"> 742 <img class="competition-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/35221c5db75c639c198fbd62d16f8d65.png" /> 743 <div class="competition-thumb__status competition-thumb__status--"> 744 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 745 <g clip-path="url(#clip0_522_18001)"> 746 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982 14.6663 8 14.6663C6.87018 14.6663 5.76332 14.3473 4.8068 13.746C3.85027 13.1448 3.08295 12.2856 2.59315 11.2675C2.10335 10.2494 1.91096 9.11362 2.03813 7.99098C2.16531 6.86834 2.60687 5.80442 3.312 4.92166L2.34334 3.95366L3.286 3.01099L4.25467 3.97966V3.97899ZM8 13.3337C8.61284 13.3337 9.21967 13.213 9.78586 12.9784C10.352 12.7439 10.8665 12.4002 11.2998 11.9668C11.7332 11.5335 12.0769
74611.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 747 </g> 748 <defs> 749 <clipPath id="clip0_522_18001"> 750 <rect width="16" height="16" fill="white"/> 751 </clipPath> 752 </defs> 753 </svg> 754 755</div> 756 757 </div> 758 <a class="competition-host ff-main d-flex align-items-center" href="/en/company_detail/51"> 759 <img class="avatar-compe-landingpage competition-host__thumb mr-1" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/4fcb50ead8d740216868f2246edd324c.jpg" /> 760 <span style="max-width: 151px;" class="competition-host__name overflow-ellipsis">VN1 Forecasting - Accuracy Challenge</span> 761</a> </div> 762 <div class="competition-item--right"> 763 <a data-turbolinks="false" class="d-block competition-item__title ff-main overflow-ellipsis" href="/en/home/data-science-competitions-for-startups/phase-2-vn1-forecasting-accuracy-challenge/description"> 764 [Phase 2] - VN1 Forecasting - Accuracy Challenge 765</a> <div class="competition-tag ff-sub"> 766 <span class="competition-tag__item">Machine Learning/AI</span> 767 <span class="competition-tag__item">Enterprise</span> 768 <span class="competition-tag__item">E-commerce/Retail</span> 769 </div> 770 <div class="competition-description"> 771 <p class="competition__desc text-ellipsis-2">Flieber, Syrup Tech, and SupChains Launch an AI-Driven Supply Chain Forecasting Competition</p> 772 </div> 773 <div class="competition-extra"> 774 <p class="competition-extra__price ff-main"> 775 20,000 776 USD 777 </p> 778 </div> 779 <div class="competition-divider"></div> 780 <div class="competition-participents text-ellipsis-1"> 781 <div class="competition-avatar"> 782 <img class="competition-avatar__item" src="https://www.gravatar.com/avatar/3ef5082dafad35b6df2d0e1f8edf2813?s=200&d=robohash" /> 783 <img class="competition-avatar__item" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/445d82ea99769a9eb364b28be72ce539.jpg" /> 784 <img class="competition-avatar__item" src="https://www.gravatar.com/avatar/837cba9c34d53ec67e2cede15ac11eff?s=200&d=robohash" /> 785 <img class="competition-avatar__item" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/e1e8820f424089da9861ad2022831e8e.jpg" /> 786 <img class="competition-avatar__item" src="https://www.gravatar.com/avatar/a6957e5d70fa8247cd97f04ec2edb0c4?s=200&d=robohash" /> 787 <div class="competition-avatar__item competition-avatar__item--more">230+</div> 788 <div class="competition-avatar__item competition-participents__text"> 789 Participants 790 </div> 791 </div> 792 </div> 793 </div> 794</li> 795 <li class="competition-item"> 796 <div class="competition-item--left" style="gap: 12px;"> 797 <div class="competition-thumb"> 798 <img class="competition-thumb__img" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/b4afeb3f7f1fd8cba2b5b78f7b9cf714.jpg" /> 799 <div class="competition-thumb__status competition-thumb__status--"> 800 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> 801 <g clip-path="url(#clip0_522_18001)"> 802 <path d="M4.25467 3.97899C5.31716 3.1277 6.63854 2.66482 8 2.66699C9.41667 2.66699 10.7187 3.15766 11.7453 3.97899L12.714 3.01033L13.6567 3.95299L12.688 4.92166C13.3931 5.80442 13.8347 6.86834 13.9619 7.99098C14.089 9.11362 13.8967 10.2494 13.4069 11.2675C12.9171 12.2856 12.1497 13.1448 11.1932 13.746C10.2367 14.3473 9.12982
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113711.019 12.3114 10.4528C12.546 9.88666 12.6667 9.27983 12.6667 8.66699C12.6667 8.05416 12.546 7.44732 12.3114 6.88114C12.0769 6.31495 11.7332 5.8005 11.2998 5.36716C10.8665 4.93382 10.352 4.59008 9.78586 4.35555C9.21967 4.12103 8.61284 4.00033 8 4.00033C6.76233 4.00033 5.57534 4.49199 4.70017 5.36716C3.825 6.24233 3.33334 7.42931 3.33334 8.66699C3.33334 9.90467 3.825 11.0917 4.70017 11.9668C5.57534 12.842 6.76233 13.3337 8 13.3337ZM8.66667 8.00033H10.6667L7.33334 12.3337V9.33366H5.33334L8.66667 4.99699V8.00033ZM5.33334 0.666992H10.6667V2.00033H5.33334V0.666992Z" fill="white"/> 1138 </g> 1139 <defs> 1140 <clipPath id="clip0_522_18001"> 1141 <rect width="16" height="16" fill="white"/> 1142 </clipPath> 1143 </defs> 1144 </svg> 1145 1146</div> 1147 1148 </div> 1149 <a class="competition-host ff-main d-flex align-items-center" href="/en/company_detail/2"> 1150 <img class="avatar-compe-landingpage competition-host__thumb mr-1" src="https://us-east-2-prod-datasource-bucket.s3.us-east-2.amazonaws.com/uploads/64c117f7f5ee7ebe158ef2ec8969f638.jpg" /> 1151 <span style="max-width: 151px;" class="competition-host__name overflow-ellipsis">DataSource.AI</span> 1152</a> </div> 1153 <div class="competition-item--right"> 1154 <a data-turbolinks="false" class="d-block competition-item__title ff-main overflow-ellipsis" href="/en/home/data-science-competitions-for-startups/google-play-store-rating-prediction/description"> 1155 Google Play Store Rating Prediction 1156</a> <div class="competition-tag ff-sub"> 1157 </div> 1158 <div class="competition-description"> 1159 <p class="competition__desc text-ellipsis-2"></p> 1160 </div> 1161 <div class="competition-extra"> 1162 <p class="competition-extra__price ff-main"> 1163 Knowledge 1164 </p> 1165 </div> 1166 <div class="competition-divider"></div> 1167 <div class="competition-participents text-ellipsis-1"> 1168 <div class="competition-avatar"> 1169 <div class="competition-avatar__item competition-participents__text"> 1170 0 Participants 1171 </div> 1172 </div> 1173 </div> 1174 </div> 1175</li> 1176</ul>
1177<script> 1178 $(document).on("turbolinks:load", function () { 1179 listenBarToSubmit("competition"); 1180 handleChangeView("competition"); 1181 startCountDown(); 1182 1183 $(".competition-list").on('DOMSubtreeModified', function(){ 1184 startCountDown() 1185 }); 1186 }); 1187</script>
1187 1188 <div class="d-flex align-items-center justify-content-center" style="margin-top: 64px;"> 1189 <a data-turbolinks="false" class="new-btn-green btn-landing-page" href="/en/competition-slug">Explore Datathons</a> 1190 </div> 1191</section> 1192<section class="p-0 screen"> 1193 <div class="screen-container container"> 1194 <div class="row"> 1195 <div class="col-lg-4 col-md-12 mb-4 mb-md-0"> 1196 <h2 class="screen-title">Our Platform</h2> 1197 <p class="screen-description"> 1198 We have a platform capable of hosting competitions, uploading files, evaluating them and ranking the best results in the competition. You will also be able to know the rankings of all competitors at any time throughout the competition. 1199 </p> 1200 <ul class="nav flex-column" role="tablist"> 1201 <li class="nav-item"> 1202 <a class="nav-link btn btn-light p-2 text-left active" href="#app-tab-1" data-toggle="tab" aria-controls="app-tab-1" aria-selected="true" role="tab"> 1203 <img class="icon" src="/assets/application/admin-settings.png" alt="admin settings"/>Host Competition 1204 </a> 1205 </li> 1206 <li class="nav-item"> 1207 <a class="nav-link btn btn-light p-2 text-left" href="#app-tab-2" data-toggle="tab" aria-controls="app-tab-2" aria-selected="false" role="tab"> 1208 <img class="icon" src="/assets/application/database.png" alt="database" />Submit Data 1209 </a> 1210 </li> 1211 <li class="nav-item"> 1212 <a class="nav-link btn btn-light p-2 text-left" href="#app-tab-3" data-toggle="tab" aria-controls="app-tab-3" aria-selected="false" role="tab"> 1213 <img class="icon" src="/assets/application/gold-collect.png" alt="gold collect icon" />Receive Model</a> 1214 </li> 1215 </ul> 1216 </div> 1217 <div class="col"> 1218 <div class="tab-content"> 1219 <div class="tab-pane fade show active" id="app-tab-1" role="tabpanel" aria-labelledby="app-tab-1"> 1220 <div class="popover-image"> 1221 <img src="/assets/application/host-competions.png" alt="host competions" class="rounded"> 1222 <div class="layer-middle"></div> 1223 <div class="layer-bottom"></div> 1224 </div> 1225 </div> 1226 <div class="tab-pane fade" id="app-tab-2" role="tabpanel" aria-labelledby="app-tab-2"> 1227 <div class="popover-image"> 1228 <img src="/assets/application/submit-data.png" alt="submit data" class="rounded"> 1229 <div class="layer-middle"></div> 1230 <div class="layer-bottom"></div> 1231 </div> 1232 </div> 1233 <div class="tab-pane fade" id="app-tab-3" role="tabpanel" aria-labelledby="app-tab-3"> 1234 <div class="popover-image"> 1235 <img src="/assets/application/receive-model.png" alt="receive model" class="rounded"> 1236 <div class="layer-middle"></div> 1237 <div class="layer-bottom"></div> 1238 </div> 1239 </div> 1240 </div> 1241 </div> 1242 </div> 1243 </div> 1244</section> 1245 1246<section class="container suggestion"> 1247 <div class="suggestion-inner row"> 1248 <div class="col-md-12 col-lg-5 suggestion-left"> 1249 <h2 class="suggestion__title">Test your knowledge and solve business problems with data science competitions</h2> 1250 <div class="suggestion__line"></div> 1251 </div> 1252 <div class="col-md-12 col-lg-6 suggestion-right"> 1253 <ul class="suggestion-list"> 1254 <li class="row suggestion-item" data-aos="fade-up" data-aos-delay="100"> 1255 <div class="suggestion-item__img suggestion-item__img--black"><img src="/assets/application/clock-and-calendar.png" alt="clock-and-calendar"></div> 1256 <div class="suggestion-show"> 1257 <div class="suggestion-show__title">Choose a Challenge</div> 1258 <p class="suggestion-show__description">Join a competition listed on our platform and grow your skills.</p> 1259 </div> 1260 </li> 1261 <li class="row suggestion-item" data-aos="fade-up" data-aos-delay="100"> 1262 <div class="suggestion-item__img suggestion-item__img--pink"><img src="/assets/application/resumes.png" alt="resumes"></div> 1263 <div class="suggestion-show"> 1264 <div class="suggestion-show__title">Complete Your Analysis</div> 1265 <p class="suggestion-show__description">Find your solution and submit your publication for voting and peer review</p> 1266 </div> 1267 </li>
1268 <li class="row suggestion-item" data-aos="fade-up" data-aos-delay="100"> 1269 <div class="suggestion-item__img suggestion-item__img--orange"><img src="/assets/application/glass-and-papers.png" alt="glass-and-papers"></div> 1270 <div class="suggestion-show"> 1271 <div class="suggestion-show__title">Vote and Learn</div> 1272 <p class="suggestion-show__description">Explore every submitted solution and upvote your favorites.</p> 1273 </div> 1274 </li> 1275 <li class="row suggestion-item" data-aos="fade-up" data-aos-delay="100"> 1276 <div class="suggestion-item__img suggestion-item__img--brown"><img src="/assets/application/board-and-notes.png" alt="board-and-notes"></div> 1277 <div class="suggestion-show"> 1278 <div class="suggestion-show__title">Receive Feedback from Seasoned Data Scientists</div> 1279 <p class="suggestion-show__description">Develop your skills and get feedback from battle-hardened data scientists.</p> 1280 </div> 1281 </li> 1282 <li class="row suggestion-item" data-aos="fade-up" data-aos-delay="100"> 1283 <div class="suggestion-item__img suggestion-item__img--blue"><img src="/assets/application/badge-and-coins.png" alt="badge-and-coins"></div> 1284 <div class="suggestion-show"> 1285 <div class="suggestion-show__title">Claim Your Reward</div> 1286 <p class="suggestion-show__description">Winners receive guaranteed blockchain payments thanks to smart contracts.</p> 1287 </div> 1288 </li> 1289 </ul> 1290 </div> 1291 </div> 1292</section> 1293 1294<section class="reason col container"> 1295 <h2 class="reason__title">Why Take Part in Datathons?</h2> 1296 <ul class="reason-list mx-auto" style="max-width: 928px"> 1297 <li class="reason-item" data-aos="fade-up" data-aos-delay="100"> 1298 <div class="reason-item__img align-items-end"> 1299 <img style="max-height: 400px; aspect-ratio: 448/400" src="/assets/application/businessman.png" alt="business man"> 1300 </div> 1301 <div class="reason-show"> 1302 <div class="reason-show__title">Real business cases</div> 1303 <p class="reason-show__description">All challenges are based on real business use cases. Get real-life experience and enrich your portfolio while getting rewarded.</p> 1304 </div> 1305 </li> 1306 <li class="reason-item" data-aos="fade-up" data-aos-delay="100"> 1307 <div class="reason-item__img align-items-center"> 1308 <img style="max-height: 400px; aspect-ratio: 448/400" src="/assets/application/nft.png" alt="nft"> 1309 </div> 1310 <div class="reason-show"> 1311 <div class="reason-show__title">NFT-proof your portfolio</div> 1312 <p class="reason-show__description">With our Soulbound NFTs, you can share your progress in external professional networks and showcase the data science milestones you achieve.</p> 1313 </div> 1314 </li> 1315 <li class="reason-item" data-aos="fade-up" data-aos-delay="100"> 1316 <div class="reason-item__img align-items-center justify-content-end"> 1317 <img style="max-height: 400px; aspect-ratio: 448/400" src="/assets/application/girl-planning.png" alt="girl planning"> 1318 </div> 1319 <div class="reason-show"> 1320 <div class="reason-show__title">Fair reward system</div> 1321 <p class="reason-show__description">The reward process is automated with smart contracts. Focus more on the process than on organizational procedures.</p> 1322 </div> 1323 </li> 1324 <li class="reason-item" data-aos="fade-up" data-aos-delay="100"> 1325 <div class="reason-item__img"> 1326 <img style="max-height: 400px; aspect-ratio: 448/400" src="/assets/application/boy-scientist.png" alt="boy scientist"> 1327 </div> 1328 <div class="reason-show"> 1329 <div class="reason-show__title">Feedback from seasoned data scientists</div> 1330 <p class="reason-show__description">With DataSource, you get feedback on your analysis. Fill in your skill gaps and build a more powerful data science portfolio.</p> 1331 </div> 1332 </li> 1333 </ul> 1334</section> 1335 1336<section class="reliable container" style="padding: 40px 100px;"> 1337 <h4 class="reliable__title">trusted by</h4> 1338 <div class="reliable-web"> 1339 <div class="reliable-web__icon"> 1340 <img src="/assets/application/battelle-925718a166b771ef45802c7b0a01d6dcdaca18299d43a12b44da6aed9599844b.png" alt="battelle"> 1341 </div> 1342 <div class="reliable-web__icon"> 1343 <img src="/assets/application/aurora-3f8b7fc11366b2f34d2601a78e6d2636330ab2959c52d27620b973536753f5f6.png" alt="aurora"> 1344 </div> 1345 <div class="reliable-web__icon"> 1346 <img src="/assets/application/aibc-413c6161da7fd569f6abb45293e61632c69188d326c3dc6b2474bd304722f25a.png" alt="aibc"> 1347 </div> 1348 <div class="reliable-web__icon"> 1349 <img src="/assets/application/napaglobal-8d6fc753e674c62d544b5f96ba437626b58cd730aa2430ca43da5332c5e44498.png" alt="napaglobal"> 1350 </div> 1351 </div> 1352</section> 1353 1354<section class="consultation col"> 1355 <div class="consultation__overlay"> 1356 <div class="consultation__title">Host your data science competition <strong>on DataSource</strong></div> 1357 <div class="consultation__desc">Find out how we can help your organisation.</div> 1358 <a class="btn-landing-page new-btn-green no-style" href="/en/request-talk-data-science-compet
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