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Top December Stories: Why You Shouldn’t be a Data Science Generalist


Also: Common mistakes when carrying out machine learning and data science; Learning Machine Learning vs Learning Data Science; Here are the most popular Python IDEs / Editors; 10 More Must-See Free Courses for Machine Learning and Data Science.



Here are the most popular posts in KDnuggets in December, based on the number of unique page views (UPV), and social share counts from Facebook, Twitter, and Addthis. In December we had one dominant platinum blog..

Platinum BlogMost Viewed - Platinum Badges (>24,000 UPV)

  1. Why You Shouldn't be a Data Science Generalist, by Jeremie Harris (*)



 

Gold BlogMost Viewed - Gold Badges (>12,000 UPV)

  1. Common mistakes when carrying out machine learning and data science, by Jekaterina Kokatjuhha
  2. Learning Machine Learning vs Learning Data Science, by Terran Melconian and Trevor Bass (*)
  3. Here are the most popular Python IDEs / Editors, by Gregory Piatetsky (*)
  4. The Machine Learning Project Checklist, by Matthew Mayo (*)
  5. Introduction to Statistics for Data Science, by Diogo Menezes Borges (*)


Silver BlogMost Viewed - Silver Badges (> 6,000 UPV)

  1. The Essence of Machine Learning, by Matthew Mayo
  2. How to build a data science project from scratch, by Jekaterina Kokatjuhha (*)
  3. Machine Learning & AI Main Developments in 2018 and Key Trends for 2019, by Matthew Mayo
  4. 10 More Must-See Free Courses for Machine Learning and Data Science, by Matthew Mayo
  5. A Guide to Decision Trees for Machine Learning and Data Science, by George Seif (*)
  6. AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019, by Gregory Piatetsky
  7. Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools, by Altexsoft (*)
  8. Papers with Code: A Fantastic GitHub Resource for Machine Learning, by Matthew Mayo
  9. Should you become a data scientist?, by Sarah Nooravi (*)
  10. Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning, by Dan Clark (*)





Silver BlogMost Shared - Gold Badges (>600 shares)

  1. Why You Shouldn't be a Data Science Generalist, by Jeremie Harris
  2. 10 More Must-See Free Courses for Machine Learning and Data Science, by Matthew Mayo
  3. Introduction to Statistics for Data Science, by Diogo Menezes Borges
  4. Machine Learning & AI Main Developments in 2018 and Key Trends for 2019, by Matthew Mayo (*)
  5. Papers with Code: A Fantastic GitHub Resource for Machine Learning, by Matthew Mayo
  6. The Essence of Machine Learning, by Matthew Mayo
  7. AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019, by Gregory Piatetsky


Silver BlogMost Shared - Silver Badges (>300 shares)

  1. Learning Machine Learning vs Learning Data Science, by Terran Melconian and Trevor Bass
  2. Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning, by Dan Clark
  3. A Guide to Decision Trees for Machine Learning and Data Science, by George Seif
  4. Should you become a data scientist?, by Sarah Nooravi
  5. Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools, by Altexsoft
  6. Here are the most popular Python IDEs / Editors, by Gregory Piatetsky
  7. How to build a data science project from scratch, by Jekaterina Kokatjuhha
  8. Common mistakes when carrying out machine learning and data science, by Jekaterina Kokatjuhha
  9. The Machine Learning Project Checklist, by Matthew Mayo
  10. Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019, by Matthew Mayo (*)


(*) indicates that badge added or upgraded based on these monthly results.

Most Shareable (Viral) Blogs

Among the top blogs, here are the blogs with the highest ratio of shares/unique views, which suggests that people who read it really liked it.
  1. Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019, by Matthew Mayo
  2. Papers with Code: A Fantastic GitHub Resource for Machine Learning, by Matthew Mayo
  3. How will automation tools change data science?, by Ryohei Fujimaki
  4. 10 More Must-See Free Courses for Machine Learning and Data Science, by Matthew Mayo
  5. Six Steps to Master Machine Learning with Data Preparation, by David Levinger
  6. Machine Learning & AI Main Developments in 2018 and Key Trends for 2019, by Matthew Mayo



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