Top June Stories: 5 Useful Statistics Data Scientists Need to Know; 7 Steps to Mastering Intermediate Machine Learning with Python – 2019 Edition

Here are the most popular posts in KDnuggets in May, based on the number of unique page views (UPV), and social share counts from Facebook, Twitter, and Addthis.

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

1. 5 Useful Statistics Data Scientists Need to Know, by George Seif
2. 7 Steps to Mastering Intermediate Machine Learning with Python - 2019 Edition, by Matthew Mayo
3. Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS, by Bob Muenchen
4. If you're a developer transitioning into data science, here are your best resources, by Cecilia Shao

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

5. How to choose a visualization, by Steven Franconeri
6. How to Learn Python for Data Science the Right Way, by Manu Jeevan
7. Understanding Cloud Data Services, by Charlie Crocker
8. Top 10 Statistics Mistakes Made by Data Scientists, by Norman Niemer (*)
9. PyViz: Simplifying the Data Visualisation Process in Python, by Parul Pandey (*)
10. Random Forest vs Neural Networks: Which is Better, and When?, by Piotr Plonski (*)
11. Spark NLP: Getting Started With The World's Most Widely Used NLP Library In The Enterprise, by David Talby
12. Math for Programmers. Manning (*)
13. The Infinity Stones of Data Science, by Matthew Mayo (*)
14. 7 Steps to Mastering Data Preparation for Machine Learning with Python - 2019 Edition, by Matthew Mayo

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Most Shared - Gold Badges (>600 shares)

1. Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS, by Bob Muenchen
2. 5 Useful Statistics Data Scientists Need to Know, by George Seif
3. How to Learn Python for Data Science the Right Way, by Manu Jeevan (*)
4. 7 Steps to Mastering Intermediate Machine Learning with Python - 2019 Edition, by Matthew Mayo
5. How to choose a visualization, by Steven Franconeri (*)

Most Shared - Silver Badges (>300 shares)

6. 7 Steps to Mastering Data Preparation for Machine Learning with Python - 2019 Edition, by Matthew Mayo
7. Top 10 Statistics Mistakes Made by Data Scientists, by Norman Niemer
8. Random Forest vs Neural Networks: Which is Better, and When?, by Piotr Plonski
9. Jupyter Notebooks: Data Science Reporting, by Asel Mendis
10. NLP and Computer Vision Integrated, by Sciforce (*)
11. Understanding Cloud Data Services, by Charlie Crocker
12. The Machine Learning Puzzle, Explained, by Matthew Mayo
13. What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem, by Gregory Piatetsky

(*) 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. Ten random useful things in R that you might not know about, by Keith McNulty
2. How To Get Funding For AI Startups, by Alexandre Gonfalonieri
3. Jupyter Notebooks: Data Science Reporting, by Asel Mendis
4. NLP and Computer Vision, by Integrated Sciforce
5. Evolving Deep Neural Networks, by Luis Da Silva
6. 7 Steps to Mastering Data Preparation for Machine Learning with Python - 2019 Edition, by Matthew Mayo