Top 2016 KDnuggets Stories: Must-Know Data Science Interview Q&A, 10 Algorithms Machine Learning Engineers Need to Know

2016 was an amazing year for KDnuggets - we published nearly 1,300 stories, reached a record 3.6 million users who visited over 10.6 million pages.

Here are the most popular 2016 KDnuggets stories, as measured by unique page views. We see that KDnuggets readers want to learn to be Data Scientists and Machine Learning engineers, and are interested in algorithms, especially Deep Learning.



We awarded the top 3 most viewed and most shared blogs the 2016 KDnuggets Gold Blog badge, and numbers 4-12 receive 2016 KDnuggets Silver Blog badge.

Most Viewed in 2016

1. 21 Must-Know Data Science Interview Questions and Answers, by Gregory Piatetsky and KDnuggets Editors
2. The 10 Algorithms Machine Learning Engineers Need to Know, by James Le
3. 20 Questions to Detect Fake Data Scientists, By Andrew Fogg
   
   
   
4. Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers, by Gregory Piatetsky
5. When Does Deep Learning Work Better Than SVMs or Random Forests?, by Sebastian Raschka
6. TensorFlow is Terrific - A Sober Take on Deep Learning Acceleration, by Zack Lipton
7. Top 10 TED Talks for the Data Scientists, by Devendra Desale
8. Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn, by Matthew Mayo
9. How to Become a Data Scientist - Part 1, by Alec Smith
10. Top 10 Machine Learning Projects on Github, by Matthew Mayo
11. What is the Difference Between Deep Learning and "Regular" Machine Learning?, by Sebastian Raschka
12. Bayesian Machine Learning, Explained, By Zygmunt ZajÄ…c

Note: in analyzing most viewed 2016 stories we also included Dec 2015 stories that did not have a chance to shine in 2015 year-end summary, and 2 such stories: "TensorFlow is Terrific" and "Top 10 Machine Learning Projects on Github" indeed made it on the top 12 list.

Here are the most shared stories, as measured by the number of social interactions using Buzzsumo Chrome extension.

Most shared stories from 2016

1. The 10 Algorithms Machine Learning Engineers Need to Know, by James Le
2. Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?, by Gregory Piatetsky
3. Top Algorithms and Methods Used by Data Scientists, by Gregory Piatetsky
   
   
   
4. What is the Difference Between Deep Learning and "Regular" Machine Learning?, by Sebastian Raschka
5. Trump, Failure of Prediction, and Lessons for Data Scientists, by Gregory Piatetsky
6. 21 Must-Know Data Science Interview Questions and Answers, by Gregory Piatetsky and KDnuggets Editors
7. Why Big Data is in Trouble: They Forgot About Applied Statistics, by Jeff Leek
8. Top 10 TED Talks for the Data Scientists, by Devendra Desale
9. Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers, by Gregory Piatetsky
10. When Does Deep Learning Work Better Than SVMs or Random Forests?, by Sebastian Raschka
11. 20 Questions to Detect Fake Data Scientists, by Andrew Fogg
12. The Big Data Ecosystem is Too Damn Big, by Andrew Brust

In general, there was not a strong correlation between number of views and the number of shares. However, some stories were shared much more than others with a similar number of views.

Here are the most viral stories of 2016, with the highest shares/(unique page views).

Most viral stories from 2016

1. Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?, by Gregory Piatetsky
2. Data Science for Internet of Things (IoT) : Ten Differences From Traditional Data Science, by Ajit Jaokar
3. Machine Learning vs Statistics, by Aatash Shah
4. Top Algorithms and Methods Used by Data Scientists, by Gregory Piatetsky
5. The Data Science Puzzle, Explained, by Matthew Mayo
6. Trump, Failure of Prediction, and Lessons for Data Scientists, by Gregory Piatetsky
7. The Big Data Ecosystem is Too Damn Big, by Andrew Brust
8. What is the Difference Between Deep Learning and "Regular" Machine Learning?, by Sebastian Raschka
9. Why Big Data is in Trouble: They Forgot About Applied Statistics, by Jeff Leek