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Chapter 7:  Launching Your New Data Science Career

268

  • 90 Job Interview Questions

268

  • About Your Experience

268

  • Technical Questions

270

  • General Questions

272

  • Little Data Science Projects

274

  • Testing Your Visual Thinking

278

  • Detecting Patterns with the Naked Eye

278

  • Identifying Aberrations

282

  • Misleading Time Series and Random Walks

283

  • From Statistician to Data Scientist

285

  • Data Scientists are also Statistical Practitioners

285

  • Who Should Teach Statistics to Data Scientists?

286

  • Hiring Issues, Government Statisticians

286

  • Data Scientists Work Closely with Data Architects

287

  • Who should be Involved in Strategic Thinking?

287

  • Two Types of Statisticians

288

  • Useless Statistical Jargon and Complexity

288

  • Using Big Data Versus Sampling

289

  • Taxonomy of Data Scientists

290

  • Top Data Scientists on LinkedIn

290

  • Data Science Most Popular Skill Mixes

293

  • 400 Job Titles for Data Scientists

297

  • Salary Surveys

299

  • Create Your Own Salary Survey

299

  • Salary Breakdown Per Skill And Location

300

  • Summary

302

 

 

Chapter 8: Data Science Resources

303

In the next and final chapter, you will find several resources: conference listings, sample resumes and job ads, data science books, definitions, data sets, organizations, popular web sites, companies with many data scientists, and so on.

See updated table of contents at http://www.datasciencecentral.com/profiles/blogs/my-data-science-book

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Comment by Vincent Granville on December 11, 2013 at 2:07pm

@Faith, yep you definitely need a base model. For instance, in this article (predicting a random walk, it's in the book), I claim that no data science model can beat the base model; base model is throwing a dice (that is, not using any model).

Case studies are in Chapter 6, but there are also a few small examples discussed in section "Little Science Projects" in chapter 7.

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