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89        </div>
90        <div class="column col-sm-auto col-7">
91            <h1>Practical data science for journalists (and everyone else)</h1>
92            <p>If you know some Python and have dabbled in data, we're here for you!
93                Let's add a dash of machine learning and a sprinkling of stats to your skillset.</p>
94            <p><small>And if you don't know Python, <a href="http://littlecolumns.com/learn/python/">take this</a> and <a href="http://littlecolumns.com/tools/python-wrangler/">this</a> and call me in the morning</small>. Or <a href="http://ledeprogram.com/">go all-in</a>, maybe?</p>
95        </div>
96    </div>
97    <div class="columns">
98        <div class="column col-sm-auto col-4">
99            <h3>Topic walkthroughs</h3>
100            <p>Practical, start-to-finish guides on data science concepts and tools.
101                Not (too) boring, not (too) mathy, they're hopefully just what you're looking for.</p>
102            <p><a class="btn btn-primary" href="/topics/">
102See our topics guide</a></p>
103        </div>
104        <div class="column col-sm-auto col-4">
105            <h3>Real-life examples</h3>
106            <p>Theory on its own doesn't do much! Practice your skills by reproducing
107                published, award-winning investigations.</p>
108            <p><small>(The ones that didn't win "real" awards win an award called "I think this project is pretty neat")</small></p>
109            <p><a class="btn btn-primary" href="/projects/">See our projects page</a></p>
110        </div>
111        <div class="column col-sm-auto col-4">
112            <h3>Reference materials</h3>
113            <p>Most of the time we spend "programming" is finding things to cut and paste from the internet. Might as well put it all in one place, right? Somewhat-organized snippets to make our days go faster.</p>
114            <p><a class="btn btn-primary" href="/reference/">Check our references</a></p>
115        </div>
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120
121<div class="content section-topics">
122    <div class="columns">
123        <div class="column col-sm-auto col-8 col-mx-auto text-center">
124            <h3>Topics we cover</h3>
125            <p>There's more than one way to dice this onion, but here's a broad overview. You might also be interested in <a href="/topics/">our topics list</a>.</p>
126        </div>
127    </div>
128    <div class="columns callout">
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130            <img alt="design tools" src="/images/flame/flame-design-science.png">
131        </div>
132        <div class="column col-sm-auto col-5">
133            <h2>Regression (aka "how X affects Y")</h2>
134            <p>Learn what you <em>really</em> mean when you wonder if two things are "correlated."</p>
135
136            <ul>
137                <li>Unemployment and life expectancy from the Associated Press</li>
138                <li>Machine bias from ProPublica</li>
139                <li>More from Dallas Morning News, Reveal, APM Reports, and others</li>
140            </ul>
141            <p><a class="btn btn-default" href="/regression/what-is-regression/">Get started with regression now →</a></p>
142        </div>
143    </div>
144    <div class="columns callout">
145        <div class="column col-sm-auto col-5">
146            <h2>Text analysis</h2>
147            <p>From counting words to the terrors of sentiment analysis, you'll be covered.</p>
148
149            <ul>
150                <li>"Cut and paste" legislation from USA Today/Arizona Central</li>
151                <li>Democratic candidate topics from Bloomberg</li>
152                <li>More from New York Times and others</li>
153            </ul>
154            <p><a class="btn btn-default" href="/text-analysis/types-of-text-analysis">Get started with text analysis now →</a></p>
155        </div>
156        <div class="column col-sm-auto col-7">
157            <img alt="books with feelings" src="/images/flame/flame-books.png">
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159    </div>
160    <div class="columns callout">
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162            <img alt="robot saying X" src="/images/flame/flame-no-comments.png">
163        </div>
164        <div class="column col-sm-auto col-5">
165            <h2>Classification</h2>
166            <p>No time to look at 100,000 things? Teach computers to automatically classify documents, crimes, airplanes, or anything else!</p>
167
168            <ul>
169                <li>Misclassified crimes from LA Times</li>
170                <li>Finding spyplanes from BuzzFeed</li>
171                <li>More from The Washington Post, Atlanta Journal-Constitution, and others</li>
172            </ul>
173            <p><a class="btn btn-default" href="/classification/intro-to-classification/">Get started with classification now →</a></p>
174        </div>
175    </div>
176</div>
177
178<div class="divider"></div>
179
180<div class="content section-projects">
181    <div class="columns">
182        <div class="column col-sm-auto col-8 col-mx-auto text-center">
183            <h3>Published reproductions</h3>
184            <p>Data science and machine learning can be used anywhere! From a small visualization at The Upshot to a year-long investigations by Reveal, let's try to put these new skills in context.</p>
185        </div>
186        <div class="column col-12">
187            <div class="columns">
188    
189        
190            <div class="column col-4 col-lg-6 col-sm-12">
191                <article class="card">
192                    <div class="card-header">
193                        <h5><a href="/nyt-takata-airbags/">Searching for faulty airbags in vehicle complaints</a></h5>
194                    </div>
195                    <div class="card-body">
196                        <p>The National Highway Transportation Safety Administration receives thousands and thousands of vehicle complaints each year. Can we train a computer to filter out leads on Takata airbag malfunctions?</p>
197                    </div>
198                    <div class="card-footer">
199                        The New York Times
200                    </div>
201                </article>
202            </div>
203        
204    
205        
206            <div class="column col-4 col-lg-6 col-sm-12">
207                <article class="card">
208                    <div class="card-header">
209                        <h5><a href="/latimes-crime-classification/">Building a crime classification engine</a></h5>
210                    </div>
211                    <div class="card-body">
212                        <p>Using machine learning as an investigative tool to cast light on years of underreporting by the Los Angeles Police Department.</p>
213                    </div>
214                    <div class="card-footer">
215                        Los Angeles Times
216                    </div>
217                </article>
218            </div>
219        
220    
221        
222            <div class="column col-4 col-lg-6 col-sm-12">
223                <article class="card">
224                    <div class="card-header">
225                        <h5><a href="/caixin-museum-word-count/">Chinese museum analysis</a></h5>
226                    </div>
227                    <div class="card-body">
228                        <p>A word-count analysis of the names of around 4500 museums in China.</p>
229                    </div>
230                    <div class="card-footer">
231                        Caixin
232                    </div>
233                </article>
234            </div>
235        
236    
237        
238            <div class="column col-4 col-lg-6 col-sm-12">
239                <article class="card">
240                    <div class="card-header">
241                        <h5><a href="/wapo-app-reviews/">Analyzing online safety through app store reviews</a></h5>
242                    </div>
243                    <div class="card-body">
244                        <p>
244After downloading over a hundred thousand reviews of "random chat apps," how to find reports of bullying, racism, and unwanted sexual behavior.</p>
245                    </div>
246                    <div class="card-footer">
247                        The Washington Post
248                    </div>
249                </article>
250            </div>
251        
252    
253        
254            <div class="column col-4 col-lg-6 col-sm-12">
255                <article class="card">
256                    <div class="card-header">
257                        <h5><a href="/ajc-doctors-abuse/">Uncovering abusive doctors that were allowed to continue practicing</a></h5>
258                    </div>
259                    <div class="card-body">
260                        <p>How to comb through 100,000 disciplinary documents without reading each individual one.</p>
261                    </div>
262                    <div class="card-footer">
263                        Atlanta Journal-Constitution
264                    </div>
265                </article>
266            </div>
267        
268    
269        
270            <div class="column col-4 col-lg-6 col-sm-12">
271                <article class="card">
272                    <div class="card-header">
273                        <h5><a href="/upshot-trump-emolex/">Analyzing the tone of Trump&#39;s speeches</a></h5>
274                    </div>
275                    <div class="card-body">
276                        <p>Standard sentiment analysis scores a document on a positive-vs-negative scale. Using the Emotional Lexicon, though, you can add unique emotional measurements like anger, joy, surprise, or fear.</p>
277                    </div>
278                    <div class="card-footer">
279                        The New York Times
280                    </div>
281                </article>
282            </div>
283        
284    
285        
286            <div class="column col-4 col-lg-6 col-sm-12">
287                <article class="card">
288                    <div class="card-header">
289                        <h5><a href="/azcentral-text-reuse-model-legislation/">Detecting special interest model legislation in state laws</a></h5>
290                    </div>
291                    <div class="card-body">
292                        <p>Special interest groups use model legislation to push their agendas in state government. How can we find bills based on these "cut and paste" models?</p>
293                    </div>
294                    <div class="card-footer">
295                        USA Today, The Arizona Republic, and the Center for Public Integrity
296                    </div>
297                </article>
298            </div>
299        
300    
301        
302            <div class="column col-4 col-lg-6 col-sm-12">
303                <article class="card">
304                    <div class="card-header">
305                        <h5><a href="/fcc-comments/">Detecting bots in FCC comment submissions</a></h5>
306                    </div>
307                    <div class="card-body">
308                        <p>The comment period on the FCC's net neutrality decision was flooded with bots. See how one still-in-training data scientist tackled finding re-used comments.</p>
309                    </div>
310                    <div class="card-footer">
311                        
312                    </div>
313                </article>
314            </div>
315        
316    
317        
318            <div class="column col-4 col-lg-6 col-sm-12">
319                <article class="card">
320                    <div class="card-header">
321                        <h5><a href="/bloomberg-tweet-topics/">Figuring out what Democratic candidates care about</a></h5>
322                    </div>
323                    <div class="card-body">
324                        <p>In the wide field of Democratic presidential candidates, who cares about what topics and how do these topics change over time?</p>
325                    </div>
326                    <div class="card-footer">
327                        Bloomberg
328                    </div>
329                </article>
330            </div>
331        
332    
333        
334            <div class="column col-4 col-lg-6 col-sm-12">
335                <article class="card">
336                    <div class="card-header">
337                        <h5><a href="/nyt-trump-tweets/">What does Trump tweet about?</a></h5>
338                    </div>
339                    <div class="card-body">
340                        <p>What does Trump tweet about? An analysis of over 11,000 tweets.</p>
341                    </div>
342                    <div class="card-footer">
343                        The New York Times
344                    </div>
345                </article>
346            </div>
347        
348    
349        
350            <div class="column col-4 col-lg-6 col-sm-12">
351                <article class="card">
352                    <div class="card-header">
353                        <h5><a href="/ap-regression-unemployment/">Examining life expectancy at the local level</a></h5>
354                    </div>
355                    <div class="card-body">
356                        <p>Combine geographically granular life expectancy data with the American Community Survey to see how poverty, education, income, and demographics can affect a community.</p>
357                    </div>
358                    <div class="card-footer">
359                        The Associated Press
360                    </div>
361                </article>
362            </div>
363        
364    
365        
366            <div class="column col-4 col-lg-6 col-sm-12">
367                <article class="card">
368                    <div class="card-header">
369                        <h5>
369<a href="/fivethirtyeight-p-hacking/">p values and p-hacking</a></h5>
370                    </div>
371                    <div class="card-body">
372                        <p>p-values and the quest for "statistical significance"</p>
373                    </div>
374                    <div class="card-footer">
375                        FiveThirtyEight
376                    </div>
377                </article>
378            </div>
379        
380    
381        
382            <div class="column col-4 col-lg-6 col-sm-12">
383                <article class="card">
384                    <div class="card-header">
385                        <h5><a href="/milwaukee-potholes/">Predicting delays in patching potholes based on demographics</a></h5>
386                    </div>
387                    <div class="card-body">
388                        <p>An analysis of the relationship between race and city sanitation services in Milwaukee.</p>
389                    </div>
390                    <div class="card-footer">
391                        Milwaukee Journal-Sentinel
392                    </div>
393                </article>
394            </div>
395        
396    
397        
398            <div class="column col-4 col-lg-6 col-sm-12">
399                <article class="card">
400                    <div class="card-header">
401                        <h5><a href="/dmn-texas-school-cheating/">Finding cheating schools in Texas with linear regression</a></h5>
402                    </div>
403                    <div class="card-body">
404                        <p>Some schools in Texas had an odd jump in standardized test scores between different grades. Was it cheating? Linear regression is on the case!</p>
405                    </div>
406                    <div class="card-footer">
407                        Dallas Morning News
408                    </div>
409                </article>
410            </div>
411        
412    
413        
414            <div class="column col-4 col-lg-6 col-sm-12">
415                <article class="card">
416                    <div class="card-header">
417                        <h5><a href="/tampa-bay-times-schools/">Measuring the impact of re-segregation on Florida elementary schools</a></h5>
418                    </div>
419                    <div class="card-body">
420                        <p>Using race, income, and other data to predict the performance of schools in Pinellas County, Florida. Along with a linear regression-driven critique.</p>
421                    </div>
422                    <div class="card-footer">
423                        Tampa Bay Times
424                    </div>
425                </article>
426            </div>
427        
428    
429        
430            <div class="column col-4 col-lg-6 col-sm-12">
431                <article class="card">
432                    <div class="card-header">
433                        <h5><a href="/car-crashes-weight-regression/">Analyzing whether larger cars cause more deadly crashes</a></h5>
434                    </div>
435                    <div class="card-body">
436                        <p>Reproducing a research paper on the impact of weight on car accidents, along with a look at a state-based car crash database.</p>
437                    </div>
438                    <div class="card-footer">
439                        Review of Economic Studies
440                    </div>
441                </article>
442            </div>
443        
444    
445        
446            <div class="column col-4 col-lg-6 col-sm-12">
447                <article class="card">
448                    <div class="card-header">
449                        <h5><a href="/propublica-opportunity-gap/">Tracking equal access to school programs</a></h5>
450                    </div>
451                    <div class="card-body">
452                        <p>Looking at differences in access to advanced classes between schools with wealthy 
452students and schools with poor students.</p>
453                    </div>
454                    <div class="card-footer">
455                        ProPublica
456                    </div>
457                </article>
458            </div>
459        
460    
461        
462            <div class="column col-4 col-lg-6 col-sm-12">
463                <article class="card">
464                    <div class="card-header">
465                        <h5><a href="/boston-globe-tickets/">Investigating who gets a ticket and who gets a warning</a></h5>
466                    </div>
467                    <div class="card-body">
468                        <p>A classic piece of data journalism analyzing ticketing by Massachusetts police, and whether the race or gender of the driver might change the outcome.</p>
469                    </div>
470                    <div class="card-footer">
471                        The Boston Globe
472                    </div>
473                </article>
474            </div>
475        
476    
477        
478            <div class="column col-4 col-lg-6 col-sm-12">
479                <article class="card">
480                    <div class="card-header">
481                        <h5><a href="/stanford-open-policing/">Stanford Open Policing Data</a></h5>
482                    </div>
483                    <div class="card-body">
484                        <p>A giant dataset of standardized data policing data across different states</p>
485                    </div>
486                    <div class="card-footer">
487                        
488                    </div>
489                </article>
490            </div>
491        
492    
493        
494            <div class="column col-4 col-lg-6 col-sm-12">
495                <article class="card">
496                    <div class="card-header">
497                        <h5><a href="/buzzfeed-spy-planes/">Uncovering surveillance planes with BuzzFeed</a></h5>
498                    </div>
499                    <div class="card-body">
500                        <p>From a list of points along a flight's path, how can you say "this looks like a surveillance plane?" And once you've found them, what do you do with the results?</p>
501                    </div>
502                    <div class="card-footer">
503                        BuzzFeed News
504                    </div>
505                </article>
506            </div>
507        
508    
509        
510            <div class="column col-4 col-lg-6 col-sm-12">
511                <article class="card">
512                    <div class="card-header">
513                        <h5><a href="/reveal-mortgages/">Analyzing mortgage rejections for racial bias</a></h5>
514                    </div>
515                    <div class="card-body">
516                        <p>Based on government-mandated data collection on mortgage granting, are certain banks or areas discriminatory in their lending practices?</p>
517                    </div>
518                    <div class="card-footer">
519                        Reveal
520                    </div>
521                </article>
522            </div>
523        
524    
525        
526            <div class="column col-4 col-lg-6 col-sm-12">
527                <article class="card">
528                    <div class="card-header">
529                        <h5><a href="/apm-reports-jury-bias/">Bias in the jury selection process</a></h5>
530                    </div>
531                    <div class="card-body">
532                        <p>When selecting a jury, both the defense and the prosecution are allowed to strike potential jurors from the pool. While the potential jurors provide answers to a questionnaire, what kind of role might race play in their selection or rejection?</p>
533                    </div>
534                    <div class="card-footer">
535                        APM Reports
536                    </div>
537                </article>
538            </div>
539        
540    
541        
542            <div class="column col-4 col-lg-6 col-sm-12">
543                <article class="card">
544                    <div class="card-header">
545                        <h5><a href="/reuters-asylum/">Analyzing the impact of particular judges on the US asylum process</a></h5>
546                    </div>
547                    <div class="card-body">
548                        <p>
548In U.S. immigration courts, are certain judges and locations more likely to approve or deny claims of asylum?</p>
549                    </div>
550                    <div class="card-footer">
551                        Reuters
552                    </div>
553                </article>
554            </div>
555        
556    
557        
558            <div class="column col-4 col-lg-6 col-sm-12">
559                <article class="card">
560                    <div class="card-header">
561                        <h5><a href="/propublica-pardons/">Investigating who receives presidential pardons</a></h5>
562                    </div>
563                    <div class="card-body">
564                        <p>An analysis of the presidential pardon process, but also a look into what to do when a very personal dataset doesn't exist.</p>
565                    </div>
566                    <div class="card-footer">
567                        ProPublica
568                    </div>
569                </article>
570            </div>
571        
572    
573        
574            <div class="column col-4 col-lg-6 col-sm-12">
575                <article class="card">
576                    <div class="card-header">
577                        <h5><a href="/propublica-criminal-sentencing/">An analysis of racial bias in criminal sentencing</a></h5>
578                    </div>
579                    <div class="card-body">
580                        <p>Can an algorithm be racist? An examination of the COMPAS algorithm used as an aid in making sentencing and parole decisions. Also featuring a critique of the critique!</p>
581                    </div>
582                    <div class="card-footer">
583                        ProPublica
584                    </div>
585                </article>
586            </div>
587        
588    
589        
590            <div class="column col-4 col-lg-6 col-sm-12">
591                <article class="card">
592                    <div class="card-header">
593                        <h5><a href="/foia-predictor/">Predicting FOIA requests success rates</a></h5>
594                    </div>
595                    <div class="card-body">
596                        <p>Government agencies seem to fulfill or reject FOIA request without rhyme or reason. Can a journalist use machine learning to improve their chances?</p>
597                    </div>
598                    <div class="card-footer">
599                        data.world
600                    </div>
601                </article>
602            </div>
603        
604    
605        
606    
607        
608    
609        
610    
611        
612    
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619
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623            <h3>Data snippets library</h3>
624            <p>This doesn't need a section, but it'll feel left out if everything else gets one.</p>
625        </div>
626    </div>
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629            <h3>Vectorizing text</h3>
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734      Counting words
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737      <ol class="menu menu-nav">
738      
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740          
741  <a href="/text-analysis/counting-words-with-pythons-counter/">        
742      Simple word counting
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745
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749      
750      <li class="menu-item">
751          
752  <a href="/text-analysis/counting-words-with-scikit-learns-countvectorizer/">        
753      Counting words across many documents
754  </a>
755  <div class="toc"></div>
756
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760      
761      <li class="menu-item">
762          
763  <a href="/text-analysis/splitting-words-in-east-asian-languages/">        
764      Segmenting words in East Asian languages
765  </a>
766  <div class="toc"></div>
767
768
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771      
772      <li class="menu-item">
773          
774  <a href="/caixin-museum-word-count/counting-words-in-chinese-museum-names/">        
775      Project: Caixin museums
776  </a>
777  <div class="toc"></div>
778
779
780
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783      <li class="menu-item">
784          
785  <a href="/text-analysis/how-to-make-scikit-learn-natural-language-processing-work-with-japanese-chinese/">        
786      Using scikit-learn vectorizers with East Asian languages
787  </a>
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789
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814      Upgraded word counts with TF-IDF
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817
818
819
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821      
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823          
824  <a href="/text-analysis/explaining-n-grams-in-natural-language-processing/">        
825      Multi-word phrases and n-grams
826  </a>
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828
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832      
833      <li class="menu-item">
834          
835  <a href="/text-analysis/stemming-and-lemmatization/">        
836      Standardizing text with stemming and lemmatization
837  </a>
838  <div class="toc"></div>
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840
841
842      </li>
843      
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845          
846  <a href="/text-analysis/using-tf-idf-with-chinese/">        
847      Using TF-IDF with Chinese text
848  </a>
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850
851
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855    </ol>
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867      Sentiment analysis
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870      <ol class="menu menu-nav">
871      
872      <li class="menu-item">
873          
874  <a href="/investigating-sentiment-analysis/comparing-sentiment-analysis-tools/">        
875      Comparing sentiment analysis tools
876  </a>
877  <div class="toc"></div>
878
879
880
881      </li>
882      
883      <li class="menu-item">
884          
885  <a href="/investigating-sentiment-analysis/designing-your-own-sentiment-analysis-tool/">        
886      Design your own sentiment analyzer
887  </a>
888  <div class="toc"></div>
889
890
891
892      </li>
893      
894      <li class="menu-item">
895          
896  <a href="/investigating-sentiment-analysis/more-data-to-train-our-sentiment-analysis-tool/">        
897      Improving your tool
898  </a>
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901
902
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904      
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906          
907  <a href="/upshot-trump-emolex/nrc-emotional-lexicon/">        
908      NRC Emotional Lexicon
909  </a>
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911
912
913
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915      
916      <li class="menu-item">
917          
918  <a href="/upshot-trump-emolex/trump-vs-state-of-the-union-addresses/">        
919      Project: UpShot State of the Union
920  </a>
921  <div class="toc"></div>
922
923
924
925      </li>
926      
927      <li class="menu-item">
928          
929  <a href="/nyt-trump-tweets/">        
930      Project: NYT Trump tweets
931  </a>
932  <div class="toc"></div>
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937      
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953      <ol class="menu menu-nav">
954      
955      <li class="menu-item">
956          
957  <a href="/text-analysis/processing-documents-with-apache-tika/">        
958      Converting documents to text (English)
959  </a>
960  <div class="toc"></div>
961
962
963
964      </li>
965      
966      <li class="menu-item">
967          
968  <a href="/text-analysis/processing-documents-with-apache-tika-greek/">        
969      Converting documents to text (non-English)
970  </a>
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972
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976      
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989      Concepts, people and places
990  </label>
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992      <ol class="menu menu-nav">
993      
994      <li class="menu-item">
995          
996  <a href="/text-analysis/introduction-to-topic-modeling/">        
997      Extracting topics from documents
998  </a>
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1000
1001
1002
1003      </li>
1004      
1005      <li class="menu-item">
1006          
1007  <a href="/text-analysis/choosing-the-right-number-of-topics-for-a-scikit-learn-topic-model/">        
1008      Choosing the right number of topics
1009  </a>
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1011
1012
1013
1014      </li>
1015      
1016      <li class="menu-item">
1017          
1018  <a href="/text-analysis/topic-models-with-gensim/">        
1019      Topic models with Gensim
1020  </a>
1021  <div class="toc"></div>
1022
1023
1024
1025      </li>
1026      
1027      <li class="menu-item">
1028          
1029  <a href="/text-analysis/topic-modeling-and-clustering/">        
1030      Topic models vs clustering
1031  </a>
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1033
1034
1035
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1037      
1038      <li class="menu-item">
1039          
1040  <a href="/text-analysis/named-entity-recognition/">        
1041      Entity recognition
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1044
1045
1046
1047      </li>
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1049      <li class="menu-item">
1050          
1051  <a href="/text-analysis/word-embeddings/">        
1052      Intro to word embeddings
1053  </a>
1054  <div class="toc"></div>
1055
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1057
1058      </li>
1059      
1060      <li class="menu-item">
1061          
1062  <a href="/text-analysis/document-similarity-using-word-embeddings/">        
1063      Conceptual document similarity
1064  </a>
1065  <div class="toc"></div>
1066
1067
1068
1069      </li>
1070      
1071      <li class="menu-item">
1072          
1073  <a href="/text-analysis/comparing-documents-in-different-languages/">        
1074      Comparing documents in different languages
1075  </a>
1076  <div class="toc"></div>
1077
1078
1079
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1081      
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1083  </div>
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1086            
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1088        
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1090            <a href="#puttingthingsincategoriesautomatically">Putting things in categories automatically</a>
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1093            
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1095                
1096  <a href="/classification/intro-to-classification/">        
1097      Introduction to Classification
1098  </a>
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1120      Evaluating classifiers
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1123
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1125
1126      </li>
1127      
1128      <li class="menu-item">
1129          
1130  <a href="/classification/scikit-learn-and-categorical-features/">        
1131      Categorical features
1132  </a>
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1134
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1140          
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1142      Classifiers with text
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1151          
1152  <a href="/classification/correcting-for-imbalanced-datasets/">        
1153      Correcting for imbalanced datasets
1154  </a>
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1156
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1181      BuzzFeed: Spy planes
1182  </a>
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1190          
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1192      WaPo chat: App reviews
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1203      NYT: Faulty airbag search
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1214      LA Times: crime classifier
1215  </a>
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1230            <a href="#howxaffectsy">How X affects Y</a>
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1235                
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1237      Finding relationships with regression
1238  </a>
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1271      Linear regression for humans
1272  </a>
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1280          
1281  <a href="/regression/linear-regression-part-two/">        
1282      Putting regression to use
1283  </a>
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1285
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1291          
1292  <a href="/regression/linear-regression-evaluation/">        
1293      Evaluating regressions
1294  </a>
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1296
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1304      Associated Press: Life expectancy and unemployment
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1332      Logistic regression (Quickstart)
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1338      </li>
1339      
1340      <li class="menu-item">
1341          
1342  <a href="/regression/logistic-regression/">        
1343      Logistic regression for humans
1344  </a>
1345  <div class="toc"></div>
1346
1347
1348
1349      </li>
1350      
1351      <li class="menu-item">
1352          
1353  <a href="/regression/logistic-regression-part-two/">        
1354      More complex logistic regressions
1355  </a>
1356  <div class="toc"></div>
1357
1358
1359
1360      </li>
1361      
1362      <li class="menu-item">
1363          
1364  <a href="/regression/evaluating-logistic-regressions/">        
1365      Evaluating logistic regressions
1366  </a>
1367  <div class="toc"></div>
1368
1369
1370
1371      </li>
1372      
1373      <li class="menu-item">
1374          
1375  <a href="/boston-globe-tickets/boston-globe-ticketing-regression/">        
1376      Boston Globe: Speeding tickets
1377  </a>
1378  <div class="toc"></div>
1379
1380
1381
1382      </li>
1383      
1384      <li class="menu-item">
1385          
1386  <a href="/apm-reports-jury-bias/in-the-dark-alternative-formula-methods/">        
1387      APM Reports: Jury selection
1388  </a>
1389  <div class="toc"></div>
1390
1391
1392
1393      </li>
1394      
1395    </ol>
1396  </div>
1397
1398              </li>
1399            
1400          </ol>
1401        
1402          <h4 id="pythondatasciencereference" class="sidebar-sticky">
1403            <a href="#pythondatasciencereference">Python data science reference</a>
1404          </h4>
1405          <ol class="menu menu-nav">
1406            
1407              <li class="menu-item">
1408                
1409  <a href="/reference/">        
1410      Introduction
1411  </a>
1412  <div class="toc"></div>
1413
1414
1415
1416              </li>
1417            
1418              <li class="menu-item">
1419                
1420  <a href="/reference/vectorizing/">        
1421      Vectorizing
1422  </a>
1423  <div class="toc"></div>
1424
1425
1426
1427              </li>
1428            
1429              <li class="menu-item">
1430                
1431  <a href="/reference/text-analysis/">        
1432      Text Analysis
1433  </a>
1434  <div class="toc"></div>
1435
1436
1437
1438              </li>
1439            
1440              <li class="menu-item">
1441                
1442  <a href="/reference/regression/">        
1443      Regression
1444  </a>
1445  <div class="toc"></div>
1446
1447
1448
1449              </li>
1450            
1451              <li class="menu-item">
1452                
1453  <a href="/reference/classification/">        
1454      Classification
1455  </a>
1456  <div class="toc"></div>
1457
1458
1459
1460              </li>
1461            
1462          </ol>
1463        
1464          <h4 id="allprojects" class="sidebar-sticky">
1465            <a href="#allprojects">All Projects</a>
1466          </h4>
1467          <ol class="menu menu-nav">
1468            
1469              <li class="menu-item">
1470                
1471  <a href="/projects/">        
1472      Project Summaries
1473  </a>
1474  <div class="toc"></div>
1475
1476
1477
1478              </li>
1479            
1480              <li class="menu-item">
1481                
1482
1483
1484  <input id="accordion-strongnytstrongtakataairbagsearch" type="checkbox" name="accordion-checkbox" hidden="">
1485  <label class="accordion-header c-hand" for="accordion-strongnytstrongtakataairbagsearch">
1486      <i class="icon icon-arrow-right mr-1"></i>
1487      <strong>NYT:</strong>  Takata airbag search
1488  </label>
1489  <div class="accordion-body">
1490      <ol class="menu menu-nav">
1491      
1492      <li class="menu-item">
1493          
1494  <a href="/nyt-takata-airbags/">        
1495      Summary
1496  </a>
1497  <div class="toc"></div>
1498
1499
1500
1501      </li>
1502      
1503      <li class="menu-item">
1504          
1505  <a href="/nyt-takata-airbags/airbag-classifier-search-binary/">        
1506      A simplistic reproduction of the NYT's research using logistic regression
1507  </a>
1508  <div class="toc"></div>
1509
1510
1511
1512      </li>
1513      
1514      <li class="menu-item">
1515          
1516  <a href="/nyt-takata-airbags/airbag-classifier-search-decision-tree/">        
1517      A decision-tree reproduction of the NYT's research
1518  </a>
1519  <div class="toc"></div>
1520
1521
1522
1523      </li>
1524      
1525      <li class="menu-item">
1526          
1527  <a href="/nyt-takata-airbags/airbag-classifier-search-countvectorizer/">        
1528      Combining a text vectorizer and a classifier to track down suspicious complaints
1529  </a>
1530  <div class="toc"></div>
1531
1532
1533
1534      </li>
1535      
1536    </ol>
1537  </div>
1538
1539              </li>
1540            
1541              <li class="menu-item">
1542                
1543
1544
1545  <input id="accordion-stronglatimesstrongcrimeclassification" type="checkbox" name="accordion-checkbox" hidden="">
1546  <label class="accordion-header c-hand" for="accordion-stronglatimesstrongcrimeclassification">
1547      <i class="icon icon-arrow-right mr-1"></i>
1548      <strong>LA Times:</strong>  Crime classification
1549  </label>
1550  <div class="accordion-body">
1551      <ol class="menu menu-nav">
1552      
1553      <li class="menu-item">
1554          
1555  <a href="/latimes-crime-classification/">        
1556      Summary
1557  </a>
1558  <div class="toc"></div>
1559
1560
1561
1562      </li>
1563      
1564      <li class="menu-item">
1565          
1566  <a href="/latimes-crime-classification/using-a-classifier-to-find-misclassified-crimes/">        
1567      Predicting downgraded assaults with machine learning
1568  </a>
1569  <div class="toc"></div>
1570
1571
1572
1573      </li>
1574      
1575      <li class="menu-item">
1576          
1577  <a href="/latimes-crime-classification/inspecting-classifications/">        
1578      Taking a closer look at our classifier and its misclassifications
1579  </a>
1580  <div class="toc"></div>
1581
1582
1583
1584      </li>
1585      
1586      <li class="menu-item">
1587          
1588  <a href="/latimes-crime-classification/trying-out-different-classifiers/">        
1589      Trying out and combining different classifiers
1590  </a>
1591  <div class="toc"></div>
1592
1593
1594
1595      </li>
1596      
1597    </ol>
1598  </div>
1599
1600              </li>
1601            
1602              <li class="menu-item">
1603                
1604
1605
1606  <input id="accordion-strongcaixinstrongmuseumnames" type="checkbox" name="accordion-checkbox" hidden="">
1607  <label class="accordion-header c-hand" for="accordion-strongcaixinstrongmuseumnames">
1608      <i class="icon icon-arrow-right mr-1"></i>
1609      <strong>Caixin:</strong>  Museum names
1610  </label>
1611  <div class="accordion-body">
1612      <ol class="menu menu-nav">
1613      
1614      <li class="menu-item">
1615          
1616  <a href="/caixin-museum-word-count/">        
1617      Summary
1618  </a>
1619  <div class="toc"></div>
1620
1621
1622
1623      </li>
1624      
1625      <li class="menu-item">
1626          
1627  <a href="/caixin-museum-word-count/chinese-museum-dataset-cleanup/">        
1628      Chinese museum dataset cleanup
1629  </a>
1630  <div class="toc"></div>
1631
1632
1633
1634      </li>
1635      
1636      <li class="menu-item">
1637          
1638  <a href="/caixin-museum-word-count/chinese-museums-per-capita-analysis/">        
1639      Chinese museums per capita analysis
1640  </a>
1641  <div class="toc"></div>
1642
1643
1644
1645      </li>
1646      
1647      <li class="menu-item">
1648          
1649  <a href="/caixin-museum-word-count/counting-words-in-chinese-museum-names/">        
1650      Counting words in Chinese museum names
1651  </a>
1652  <div class="toc"></div>
1653
1654
1655
1656      </li>
1657      
1658    </ol>
1659  </div>
1660
1661              </li>
1662            
1663              <li class="menu-item">
1664                
1665
1666
1667  <input id="accordion-strongwapostrongrandomchatappsafety" type="checkbox" name="accordion-checkbox" hidden="">
1668  <label class="accordion-header c-hand" for="accordion-strongwapostrongrandomchatappsafety">
1669      <i class="icon icon-arrow-right mr-1"></i>
1670      <strong>WaPo:</strong>  Random chat app safety
1671  </label>
1672  <div class="accordion-body">
1673      <ol class="menu menu-nav">
1674      
1675      <li class="menu-item">
1676          
1677  <a href="/wapo-app-reviews/">        
1678      Summary
1679  </a>
1680  <div class="toc"></div>
1681
1682
1683
1684      </li>
1685      
1686      <li class="menu-item">
1687          
1688  <a href="/wapo-app-reviews/scrape-app-store-reviews/">        
1689      Scrape and combine app store reviews
1690  </a>
1691  <div class="toc"></div>
1692
1693
1694
1695      </li>
1696      
1697      <li class="menu-item">
1698          
1699  <a href="/wapo-app-reviews/predict-reviews/">        
1700      Build a classifier to detect reviews about bad behavior
1701  </a>
1702  <div class="toc"></div>
1703
1704
1705
1706      </li>
1707      
1708    </ol>
1709  </div>
1710
1711              </li>
1712            
1713              <li class="menu-item">
1714                
1715  <a href="/ajc-doctors-abuse/">        
1716      <strong>AJC:</strong>  Doctors and sex abuse
1717  </a>
1718  <div class="toc"></div>
1719
1720
1721
1722              </li>
1723            
1724              <li class="menu-item">
1725                
1726
1727
1728  <input id="accordion-strongtheupshotstrongtrumpspeeches" type="checkbox" name="accordion-checkbox" hidden="">
1729  <label class="accordion-header c-hand" for="accordion-strongtheupshotstrongtrumpspeeches">
1730      <i class="icon icon-arrow-right mr-1"></i>
1731      <strong>The UpShot:</strong>  Trump speeches
1732  </label>
1733  <div class="accordion-body">
1734      <ol class="menu menu-nav">
1735      
1736      <li class="menu-item">
1737          
1738  <a href="/upshot-trump-emolex/">        
1739      Summary
1740  </a>
1741  <div class="toc"></div>
1742
1743
1744
1745      </li>
1746      
1747      <li class="menu-item">
1748          
1749  <a href="/upshot-trump-emolex/nrc-emotional-lexicon/">        
1750      An introduction to the NRC Emotional Lexicon
1751  </a>
1752  <div class="toc"></div>
1753
1754
1755
1756      </li>
1757      
1758      <li class="menu-item">
1759          
1760  <a href="/upshot-trump-emolex/trump-vs-state-of-the-union-addresses/">        
1761      Reproducing The UpShot's Trump State of the Union visualization
1762  </a>
1763  <div class="toc"></div>
1764
1765
1766
1767      </li>
1768      
1769    </ol>
1770  </div>
1771
1772              </li>
1773            
1774              <li class="menu-item">
1775                
1776
1777
1778  <input id="accordion-strongusatodaystrongmodellegislation" type="checkbox" name="accordion-checkbox" hidden="">
1779  <label class="accordion-header c-hand" for="accordion-strongusatodaystrongmodellegislation">
1780      <i class="icon icon-arrow-right mr-1"></i>
1781      <strong>USA Today:</strong>  Model legislation
1782  </label>
1783  <div class="accordion-body">
1784      <ol class="menu menu-nav">
1785      
1786      <li class="menu-item">
1787          
1788  <a href="/azcentral-text-reuse-model-legislation/">        
1789      Summary
1790  </a>
1791  <div class="toc"></div>
1792
1793
1794
1795      </li>
1796      
1797      <li class="menu-item">
1798          
1799  <a href="/azcentral-text-reuse-model-legislation/01-downloading-one-million-pieces-of-legislation-from-legiscan/">        
1800      Downloading one million pieces of legislation from LegiScan
1801  </a>
1802  <div class="toc"></div>
1803
1804
1805
1806      </li>
1807      
1808      <li class="menu-item">
1809          
1810  <a href="/azcentral-text-reuse-model-legislation/02-taking-a-mill
1810ion-pieces-of-legislation-from-a-csv-and-inserting-them-into-postgres/">        
1811      Taking a million pieces of legislation from a CSV and inserting them into Postgres
1812  </a>
1813  <div class="toc"></div>
1814
1815
1816
1817      </li>
1818      
1819      <li class="menu-item">
1820          
1821  <a href="/azcentral-text-reuse-model-legislation/03-download-word-pdf-and-html-content-and-process-it-into-text-with-tika/">        
1822      Download Word, PDF and HTML content and process it into text with Tika
1823  </a>
1824  <div class="toc"></div>
1825
1826
1827
1828      </li>
1829      
1830      <li class="menu-item">
1831          
1832  <a href="/azcentral-text-reuse-model-legislation/04-import-content-into-solr-for-advanced-text-searching/">        
1833      Import content into Solr for advanced text searching
1834  </a>
1835  <div class="toc"></div>
1836
1837
1838
1839      </li>
1840      
1841      <li class="menu-item">
1842          
1843  <a href="/azcentral-text-reuse-model-legislation/05-checking-for-legislative-text-reuse-using-python-solr-and-ngrams/">        
1844      Checking for legislative text reuse using Python, Solr, and ngrams
1845  </a>
1846  <div class="toc"></div>
1847
1848
1849
1850      </li>
1851      
1852      <li class="menu-item">
1853          
1854  <a href="/azcentral-text-reuse-model-legislation/05-checking-for-legislative-text-reuse-using-python-solr-and-simple-text-search/">        
1855      Checking for legislative text reuse using Python, Solr, and simple text search
1856  </a>
1857  <div class="toc"></div>
1858
1859
1860
1861      </li>
1862      
1863      <li class="menu-item">
1864          
1865  <a href="/azcentral-text-reuse-model-legislation/06-search-for-model-legislation-in-over-one-million-bills-using-postgres-and-solr/">        
1866      Search for model legislation in over one million bills using Postgres and Solr
1867  </a>
1868  <div class="toc"></div>
1869
1870
1871
1872      </li>
1873      
1874      <li class="menu-item">
1875          
1876  <a href="/azcentral-text-reuse-model-legislation/using-topic-modeling-to-categorize-legislation/">        
1877      Using topic modeling to categorize legislation
1878  </a>
1879  <div class="toc"></div>
1880
1881
1882
1883      </li>
1884      
1885    </ol>
1886  </div>
1887
1888              </li>
1889            
1890              <li class="menu-item">
1891                
1892  <a href="/fcc-comments/">        
1893      FCC comment bots
1894  </a>
1895  <div class="toc"></div>
1896
1897
1898
1899              </li>
1900            
1901              <li class="menu-item">
1902                
1903
1904
1905  <input id="accordion-strongbloombergstrongdemocraticcandidatetweets" type="checkbox" name="accordion-checkbox" hidden="">
1906  <label class="accordion-header c-hand" for="accordion-strongbloombergstrongdemocraticcandidatetweets">
1907      <i class="icon icon-arrow-right mr-1"></i>
1908      <strong>Bloomberg:</strong>  Democratic Candidate Tweets
1909  </label>
1910  <div class="accordion-body">
1911      <ol class="menu menu-nav">
1912      
1913      <li class="menu-item">
1914          
1915  <a href="/bloomberg-tweet-topics/">        
1916      Summary
1917  </a>
1918  <div class="toc"></div>
1919
1920
1921
1922      </li>
1923      
1924      <li class="menu-item">
1925          
1926  <a href="/bloomberg-tweet-topics/scrape-tweets-from-presidential-primary-candidates/">        
1927      Downloading all 2019 tweets from Democratic presidential candidates
1928  </a>
1929  <div class="toc"></div>
1930
1931
1932
1933      </li>
1934      
1935      <li class="menu-item">
1936          
1937  <a href="/bloomberg-tweet-topics/topic-modeling-for-tweets/">        
1938      Using topic modeling to analyze presidential candidate tweets
1939  </a>
1940  <div class="toc"></div>
1941
1942
1943
1944      </li>
1945      
1946      <li class="menu-item">
1947          
1948  <a href="/bloomberg-tweet-topics/assigning-categories-to-text-using-keyword-matching/">        
1949      Assigning categories to tweets using keyword matching
1950  </a>
1951  <div class="toc"></div>
1952
1953
1954
1955      </li>
1956      
1957      <li class="menu-item">
1958          
1959  <a href="/bloomberg-tweet-topics/building-streamgraphs-from-candidate-tweets/">        
1960      Building streamgraphs from categorized and dated datasets
1961  </a>
1962  <div class="toc"></div>
1963
1964
1965
1966      </li>
1967      
1968    </ol>
1969  </div>
1970
1971              </li>
1972            
1973              <li class="menu-item">
1974                
1975  <a href="/nyt-trump-tweets/">        
1976      <strong>NYT:</strong>  Trump tweets
1977  </a>
1978  <div class="toc"></div>
1979
1980
1981
1982              </li>
1983            
1984              <li class="menu-item">
1985                
1986
1987
1988  <input id="accordion-strongapstronglifeexpectancy" type="checkbox" name="accordion-checkbox" hidden="">
1989  <label class="accordion-header c-hand" for="accordion-strongapstronglifeexpectancy">
1990      <i class="icon icon-arrow-right mr-1"></i>
1991      <strong>AP:</strong>  Life expectancy
1992  </label>
1993  <div class="accordion-body">
1994      <ol class="menu menu-nav">
1995      
1996      <li class="menu-item">
1997          
1998  <a href="/ap-regression-unemployment/">        
1999      Summary
2000  </a>
2001  <div class="toc"></div>
2002
2003
2004
2005      </li>
2006      
2007      <li class="menu-item">
2008          
2009  <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-formulas/">        
2010      Simple logistic regression using statsmodels (formula version)
2011  </a>
2012  <div class="toc"></div>
2013
2014
2015
2016      </li>
2017      
2018      <li class="menu-item">
2019          
2020  <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-dataframes/">        
2021      Simple logistic regression using statsmodels (dataframes version)
2022  </a>
2023  <div class="toc"></div>
2024
2025
2026
2027      </li>
2028      
2029    </ol>
2030  </div>
2031
2032              </li>
2033            
2034              <li class="menu-item">
2035                
2036  <a href="/fivethirtyeight-p-hacking/">        
2037      <strong>FiveThirtyEight:</strong>  P-values
2038  </a>
2039  <div class="toc"></div>
2040
2041
2042
2043              </li>
2044            
2045              <li class="menu-item">
2046                
2047
2048
2049  <input id="accordion-strongmilwaukeejournalsentinelstrongpotholes" type="checkbox" name="accordion-checkbox" hidden="">
2050  <label class="accordion-header c-hand" for="accordion-strongmilwaukeejournalsentinelstrongpotholes">
2051      <i class="icon icon-arrow-right mr-1"></i>
2052      <strong>Milwaukee Journal-Sentinel:</strong>  Potholes
2053  </label>
2054  <div class="accordion-body">
2055      <ol class="menu menu-nav">
2056      
2057      <li class="menu-item">
2058          
2059  <a href="/milwaukee-potholes/">        
2060      Summary
2061  </a>
2062  <div class="toc"></div>
2063
2064
2065
2066      </li>
2067      
2068      <li class="menu-item">
2069          
2070  <a href="/milwaukee-potholes/milwaukee-journal-sentinel-and-potholes-full-walkthrough/">        
2071      Pothole geographic analysis and linear regression, complete walkthrough
2072  </a>
2073  <div class="toc"></div>
2074
2075
2076
2077      </li>
2078      
2079      <li class="menu-item">
2080          
2081  <a href="/milwaukee-potholes/milwaukee-journal-sentinel-and-potholes-without-merging/">        
2082      Pothole demographics linear regression, no spatial analysis
2083  </a>
2084  <div class="toc"></div>
2085
2086
2087
2088      </li>
2089      
2090    </ol>
2091  </div>
2092
2093              </li>
2094            
2095              <li class="menu-item">
2096                
2097
2098
2099  <input id="accordion-strongdallasmorningnewsstrongcheatingschools" type="checkbox" name="accordion-checkbox" hidden="">
2100  <label class="accordion-header c-hand" for="accordion-strongdallasmorningnewsstrongcheatingschools">
2101      <i class="icon icon-arrow-right mr-1"></i>
2102      <strong>Dallas Morning News:</strong>  Cheating schools
2103  </label>
2104  <div class="accordion-body">
2105      <ol class="menu menu-nav">
2106      
2107      <li class="menu-item">
2108          
2109  <a href="/dmn-texas-school-cheating/">        
2110      Summary
2111  </a>
2112  <div class="toc"></div>
2113
2114
2115
2116      </li>
2117      
2118      <li class="menu-item">
2119          
2120  <a href="/dmn-texas-school-cheating/texas-school-cheating-finding-outliers-with-standard-deviation-and-regression/">        
2121      Finding outliers with standard deviation and regression
2122  </a>
2123  <div class="toc"></div>
2124
2125
2126
2127      </li>
2128      
2129      <li class="menu-item">
2130          
2131  <a href="/dmn-texas-school-cheating/texas-school-cheating-finding-outliers-with-regression-residuals/">        
2132      Finding outliers with regression residuals (short version)
2133  </a>
2134  <div class="toc"></div>
2135
2136
2137
2138      </li>
2139      
2140      <li class="menu-item">
2141          
2142  <a href="/dmn-texas-school-cheating/texas-school-cheating-graph-reproductions/">        
2143      Reproducing the graphics from The Dallas Morning News piece
2144  </a>
2145  <div class="toc"></div>
2146
2147
2148
2149      </li>
2150      
2151    </ol>
2152  </div>
2153
2154              </li>
2155            
2156              <li class="menu-item">
2157                
2158
2159
2160  <input id="accordion-strongtampabaytimesstrongfailurefactories" type="checkbox" name="accordion-checkbox" hidden="">
2161  <label class="accordion-header c-hand" for="accordion-strongtampabaytimesstrongfailurefactories">
2162      <i class="icon icon-arrow-right mr-1"></i>
2163      <strong>Tampa Bay Times:</strong>  Failure factories
2164  </label>
2165  <div class="accordion-body">
2166      <ol class="menu menu-nav">
2167      
2168      <li class="menu-item">
2169          
2170  <a href="/tampa-bay-times-schools/">        
2171      Summary
2172  </a>
2173  <div class="toc"></div>
2174
2175
2176
2177      </li>
2178      
2179      <li class="menu-item">
2180          
2181  <a href="/tampa-bay-times-schools/linear-regression-on-florida-schools/">        
2182      Linear regression on Florida schools, complete walkthrough
2183  </a>
2184  <div class="toc"></div>
2185
2186
2187
2188      </li>
2189      
2190      <li class="menu-item">
2191          
2192  <a href="/tampa-bay-times-schools/linear-regression-on-florida-schools-no-cleaning/">        
2193      Linear regression on Florida schools, no cleaning
2194  </a>
2195  <div class="toc"></div>
2196
2197
2198
2199      </li>
2200      
2201    </ol>
2202  </div>
2203
2204              </li>
2205            
2206              <li class="menu-item">
2207                
2208
2209
2210  <input id="accordion-caraccidentsandcarweight" type="checkbox" name="accordion-checkbox" hidden="">
2211  <label class="accordion-header c-hand" for="accordion-caraccidentsandcarweight">
2212      <i class="icon icon-arrow-right mr-1"></i>
2213      Car accidents and car weight
2214  </label>
2215  <div class="accordion-body">
2216      <ol class="menu menu-nav">
2217      
2218      <li class="menu-item">
2219          
2220  <a href="/car-crashes-weight-regression/">        
2221      Summary
2222  </a>
2223  <div class="toc"></div>
2224
2225
2226
2227      </li>
2228      
2229      <li class="menu-item">
2230          
2231  <a href="/car-crashes-weight-regression/car-crashes-feature-selection-and-engineering/">        
2232      Feature selection and engineering
2233  </a>
2234  <div class="toc"></div>
2235
2236
2237
2238      </li>
2239      
2240      <li class="menu-item">
2241          
2242  <a href="/car-crashes-weight-regression/01-combine-excel-files-across-multiple-sheets-and-save-as-csv-files/">        
2243      Combine Excel files across multiple sheets and save as CSV files
2244  </a>
2245  <div class="toc"></div>
2246
2247
2248
2249      </li>
2250      
2251      <li class="menu-item">
2252          
2253  <a href="/car-crashes-weight-regression/02-create-make-model-weights-csv/">        
2254      Create make model weights csv
2255  </a>
2256  <div class="toc"></div>
2257
2258
2259
2260      </li>
2261      
2262      <li class="menu-item">
2263          
2264  <a href="/car-crashes-weight-regression/03-find-car-data-from-vins/">        
2265      Find car data from VINs
2266  </a>
2267  <div class="toc"></div>
2268
2269
2270
2271      </li>
2272      
2273      <li class="menu-item">
2274          
2275  <a href="/car-crashes-weight-regression/04-combine-vins-and-weights/">        
2276      Combine VINs and weights
2277  </a>
2278  <div class="toc"></div>
2279
2280
2281
2282      </li>
2283      
2284      <li class="menu-item">
2285          
2286  <a href="/car-crashes-weight-regression/05-clean-combine-and-filter-data/">        
2287      Clean combine and filter data
2288  </a>
2289  <div class="toc"></div>
2290
2291
2292
2293      </li>
2294      
2295    </ol>
2296  </div>
2297
2298              </li>
2299            
2300              <li class="menu-item">
2301                
2302  <a href="/propublica-opportunity-gap/">        
2303      <strong>ProPublica:</strong>  Opportunity Gap
2304  </a>
2305  <div class="toc"></div>
2306
2307
2308
2309              </li>
2310            
2311              <li class="menu-item">
2312                
2313
2314
2315  <input id="accordion-strongbostonglobestrongticketingbias" type="checkbox" name="accordion-checkbox" hidden="">
2316  <label class="accordion-header c-hand" for="accordion-strongbostonglobestrongticketingbias">
2317      <i class="icon icon-arrow-right mr-1"></i>
2318      <strong>Boston Globe:</strong>  Ticketing bias
2319  </label>
2320  <div class="accordion-body">
2321      <ol class="menu menu-nav">
2322      
2323      <li class="menu-item">
2324          
2325  <a href="/boston-globe-tickets/">        
2326      Summary
2327  </a>
2328  <div class="toc"></div>
2329
2330
2331
2332      </li>
2333      
2334      <li class="menu-item">
2335          
2336  <a href="/boston-globe-tickets/boston-globe-ticketing-regression/">        
2337      Logistic regression for speeding tickets
2338  </a>
2339  <div class="toc"></div>
2340
2341
2342
2343      </li>
2344      
2345    </ol>
2346  </div>
2347
2348              </li>
2349            
2350              <li class="menu-item">
2351                
2352  <a href="/stanford-open-policing/">        
2353      <strong>Stanford:</strong>  Open Policing Data
2354  </a>
2355  <div class="toc"></div>
2356
2357
2358
2359              </li>
2360            
2361              <li class="menu-item">
2362                
2363
2364
2365  <input id="accordion-strongbuzzfeedstrongsurveillanceplanes" type="checkbox" name="accordion-checkbox" hidden="">
2366  <label class="accordion-header c-hand" for="accordion-strongbuzzfeedstrongsurveillanceplanes">
2367      <i class="icon icon-arrow-right mr-1"></i>
2368      <strong>BuzzFeed:</strong>  Surveillance planes
2369  </label>
2370  <div class="accordion-body">
2371      <ol class="menu menu-nav">
2372      
2373      <li class="menu-item">
2374          
2375  <a href="/buzzfeed-spy-planes/">        
2376      Summary
2377  </a>
2378  <div class="toc"></div>
2379
2380
2381
2382      </li>
2383      
2384      <li class="menu-item">
2385          
2386  <a href="/buzzfeed-spy-planes/feature-engineering-buzzfeed-spy-planes/">        
2387      Feature engineering - BuzzFeed spy planes
2388  </a>
2389  <div class="toc"></div>
2390
2391
2392
2393      </li>
2394      
2395      <li class="menu-item">
2396          
2397  <a href="/buzzfeed-spy-planes/drawing-flight-paths-on-maps-with-cartopy/">        
2398      Drawing flight paths on maps with cartopy
2399  </a>
2400  <div class="toc"></div>
2401
2402
2403
2404      </li>
2405      
2406      <li class="menu-item">
2407          
2408  <a href="/buzzfeed-spy-planes/buzzfeed-surveillance-planes-random-forests/">        
2409      Finding surveillance planes using random forests
2410  </a>
2411  <div class="toc"></div>
2412
2413
2414
2415      </li>
2416      
2417    </ol>
2418  </div>
2419
2420              </li>
2421            
2422              <li class="menu-item">
2423                
2424
2425
2426  <input id="accordion-strongrevealstrongmortgagelendingbias" type="checkbox" name="accordion-checkbox" hidden="">
2427  <label class="accordion-header c-hand" for="accordion-strongrevealstrongmortgagelendingbias">
2428      <i class="icon icon-arrow-right mr-1"></i>
2429      <strong>Reveal:</strong>  Mortgage lending bias
2430  </label>
2431  <div class="accordion-body">
2432      <ol class="menu menu-nav">
2433      
2434      <li class="menu-item">
2435          
2436  <a href="/reveal-mortgages/">        
2437      Summary
2438  </a>
2439  <div class="toc"></div>
2440
2441
2442
2443      </li>
2444      
2445      <li class="menu-item">
2446          
2447  <a href="/reveal-mortgages/reveal-mortgage-analysis-cleaning-and-combining-data/">        
2448      Cleaning and combining data for the Reveal Mortgage Analysis
2449  </a>
2450  <div class="toc"></div>
2451
2452
2453
2454      </li>
2455      
2456      <li class="menu-item">
2457          
2458  <a href="/reveal-mortgages/reveal-mortgage-analysis-wild-formulas-in-statsmodels-using-patsy-short-version/">        
2459      Wild formulas in statsmodels using Patsy (short version)
2460  </a>
2461  <div class="toc"></div>
2462
2463
2464
2465      </li>
2466      
2467      <li class="menu-item">
2468          
2469  <a href="/reveal-mortgages/reveal-mortgage-analysis-logistic-regression-using-statsmodels-formulas/">        
2470      Reveal Mortgage Analysis - Logistic Regression using statsmodels formulas
2471  </a>
2472  <div class="toc"></div>
2473
2474
2475
2476      </li>
2477      
2478      <li class="menu-item">
2479          
2480  <a href="/reveal-mortgages/reveal-mortgage-analysis-logistic-regression/">        
2481      Reveal Mortgage Analysis - Logistic Regression
2482  </a>
2483  <div class="toc"></div>
2484
2485
2486
2487      </li>
2488      
2489    </ol>
2490  </div>
2491
2492              </li>
2493            
2494              <li class="menu-item">
2495                
2496
2497
2498  <input id="accordion-strongapmreportsstrongjuryselectionbias" type="checkbox" name="accordion-checkbox" hidden="">
2499  <label class="accordion-header c-hand" for="accordion-strongapmreportsstrongjuryselectionbias">
2500      <i class="icon icon-arrow-right mr-1"></i>
2501      <strong>APM Reports:</strong>  Jury selection bias
2502  </label>
2503  <div class="accordion-body">
2504      <ol class="menu menu-nav">
2505      
2506      <li class="menu-item">
2507          
2508  <a href="/apm-reports-jury-bias/">        
2509      Summary
2510  </a>
2511  <div class="toc"></div>
2512
2513
2514
2515      </li>
2516      
2517      <li class="menu-item">
2518          
2519  <a href="/apm-reports-jury-bias/in-the-dark-combining-datasets-and-cleaning-the-data/">        
2520      Combining and cleaning the initial dataset
2521  </a>
2522  <div class="toc"></div>
2523
2524
2525
2526      </li>
2527      
2528      <li class="menu-item">
2529          
2530  <a href="/apm-reports-jury-bias/in-the-dark-feature-selection-with-p-values/">        
2531      Picking what matters and what doesn't in a regression
2532  </a>
2533  <div class="toc"></div>
2534
2535
2536
2537      </li>
2538      
2539      <li class="menu-item">
2540          
2541  <a href="/apm-reports-jury-bias/in-the-dark-jury-selection-regression-walkthrough/">        
2542      Analyzing data using statsmodels formulas
2543  </a>
2544  <div class="toc"></div>
2545
2546
2547
2548      </li>
2549      
2550      <li class="menu-item">
2551          
2552  <a href="/apm-reports-jury-bias/in-the-dark-alternative-formula-methods/">        
2553      Alternative techniques with statsmodels formulas
2554  </a>
2555  <div class="toc"></div>
2556
2557
2558
2559      </li>
2560      
2561    </ol>
2562  </div>
2563
2564              </li>
2565            
2566              <li class="menu-item">
2567                
2568
2569
2570  <input id="accordion-strongreutersstrongasylumdenials" type="checkbox" name="accordion-checkbox" hidden="">
2571  <label class="accordion-header c-hand" for="accordion-strongreutersstrongasylumdenials">
2572      <i class="icon icon-arrow-right mr-1"></i>
2573      <strong>Reuters:</strong>  Asylum denials
2574  </label>
2575  <div class="accordion-body">
2576      <ol class="menu menu-nav">
2577      
2578      <li class="menu-item">
2579          
2580  <a href="/reuters-asylum/">        
2581      Summary
2582  </a>
2583  <div class="toc"></div>
2584
2585
2586
2587      </li>
2588      
2589      <li class="menu-item">
2590          
2591  <a href="/reuters-asylum/cleaning-the-eoir-immigration-court-dataset/">        
2592      Preparing the EOIR immigration court data for analysis
2593  </a>
2594  <div class="toc"></div>
2595
2596
2597
2598      </li>
2599      
2600      <li class="menu-item">
2601          
2602  <a href="/reuters-asylum/using-regression-to-analyze-asylum-cases/">        
2603      How nationality and judges affect your chance of asylum in immigration court
2604  </a>
2605  <div class="toc"></div>
2606
2607
2608
2609      </li>
2610      
2611    </ol>
2612  </div>
2613
2614              </li>
2615            
2616              <li class="menu-item">
2617                
2618  <a href="/propublica-pardons/">        
2619      <strong>ProPublica:</strong>  Presidential pardons
2620  </a>
2621  <div class="toc"></div>
2622
2623
2624
2625              </li>
2626            
2627              <li class="menu-item">
2628                
2629
2630
2631  <input id="accordion-strongpropublicastrongcriminalsentencing" t
2631ype="checkbox" name="accordion-checkbox" hidden="">
2632  <label class="accordion-header c-hand" for="accordion-strongpropublicastrongcriminalsentencing">
2633      <i class="icon icon-arrow-right mr-1"></i>
2634      <strong>ProPublica:</strong>  Criminal sentencing
2635  </label>
2636  <div class="accordion-body">
2637      <ol class="menu menu-nav">
2638      
2639      <li class="menu-item">
2640          
2641  <a href="/propublica-criminal-sentencing/">        
2642      Summary
2643  </a>
2644  <div class="toc"></div>
2645
2646
2647
2648      </li>
2649      
2650      <li class="menu-item">
2651          
2652  <a href="/propublica-criminal-sentencing/week-5-1-machine-bias-class/">        
2653      Breaking down machine bias
2654  </a>
2655  <div class="toc"></div>
2656
2657
2658
2659      </li>
2660      
2661    </ol>
2662  </div>
2663
2664              </li>
2665            
2666              <li class="menu-item">
2667                
2668  <a href="/foia-predictor/">        
2669      <strong>data.world:</strong>  The FOIA Predictor
2670  </a>
2671  <div class="toc"></div>
2672
2673
2674
2675              </li>
2676            
2677          </ol>
2678        
2679    </div>
2680  </div>
2681</div>
2682      </div>
2683    </div>
2684  </div>
2685    
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