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Top June Stories: Top 15 Python Libraries for Data Science in 2017


 
 
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Also 6 Interesting Things You Can Do with Python on Facebook Data; 7 Steps to Mastering Data Preparation with Python.



For the month of June, we continue to recognize the most popular posts and blogger based on unique page views (UPV) and social shares.

Platinum Blog, June 2017Most Viewed - Platinum Badge
(>24,000 UPV)

  1. Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov. (*)


Gold Blog, June 2017Most Viewed - Gold Badges (>12,000 UPV)

  1. 6 Interesting Things You Can Do with Python on Facebook Data, by Nour Galaby (*)
  2. 7 Steps to Mastering Data Preparation with Python, by Matthew Mayo (*)


Silver Blog, June 2017Most Viewed - Silver Badges (>6,000 unique PV)

  1. Emerging Ecosystem: Data Science and Machine Learning Software, Analyzed, by Gregory Piatetsky
  2. Is Regression Analysis Really Machine Learning?, by Matthew Mayo
  3. Which Machine Learning Algorithm Should I Use?, by Hui Li
  4. Deep Learning Papers Reading Roadmap, by Flood Sung
  5. Applying Deep Learning to Real-world Problems, by Rasmus Rothe
  6. 7 Techniques to Handle Imbalanced Data, by Ye Wu & Rick Radewagen (*)
  7. Data Scientist: Learn the Skills you need for free, by Mohamed Tharwat (*)



Platinum Blog, June 2017Most Shared - Platinum Badge
(>2,400 shares)

  1. Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov.



 

Gold Blog, June 2017Most Shared - Gold Badges (>1,200 shares)

  1. 6 Interesting Things You Can Do with Python on Facebook Data, by Nour Galaby
  2. 7 Steps to Mastering Data Preparation with Python, by Matthew Mayo
  3. Is Regression Analysis Really Machine Learning?, by Matthew Mayo
  4. Which Machine Learning Algorithm Should I Use?, by Hui Li
  5. Deep Learning Papers Reading Roadmap, by Flood Sung
  6. Applying Deep Learning to Real-world Problems, by Rasmus Rothe


Silver Blog, June 2017Most Shared - Silver Gold Badges (>600 shares)

  1. Emerging Ecosystem: Data Science and Machine Learning Software, Analyzed, by Gregory Piatetsky
  2. A Practical Guide to Machine Learning: Understand, Differentiate, and Apply, by Rob Thomas and Jean-Francois Puget (*)
  3. Text Clustering: Get quick insights from Unstructured Data, by Vivek Kalyanarangan.
  4. The Machine Learning Algorithms Used in Self-Driving Cars, by Savaram Ravindra
  5. 7 Techniques to Handle Imbalanced Data, by Ye Wu & Rick Radewagen
  6. The world's first protein database for Machine Learning and AI, by Kamil Tamiola (*)
  7. The Practical Importance of Feature Selection, by Matthew Mayo (*)
  8. Understanding Deep Learning Requires Re-thinking Generalization, by Adrian Colyer (*)
  9. Making Sense of Machine Learning, by Kevin Gray (*)
  10. Deep Learning 101: Demystifying Tensors (*), by Ted Dunning

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