Top February Stories: Data Scientists: Why are they so expensive to hire? Artificial Neural Network Implementation using NumPy and Image Classification

Here are the most popular posts in KDnuggets in February, 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. Data Scientists: Why are they so expensive to hire?, by Adam Carrigan
2. Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms, by Gregory Piatetsky
3. The Best and Worst Data Visualizations of 2018, by Dan Clark (*)

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

4. How to Setup a Python Environment for Machine Learning, by George Seif
5. An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning, by George Seif
6. Python Data Science for Beginners, by Saurabh Hooda
7. The Essential Data Science Venn Diagram, by Andrew Silver

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Most Shared - Gold Badges (>600 shares)

1. Artificial Neural Network Implementation using NumPy and Image Classification, by Ahmed Gad
2. The Essential Data Science Venn Diagram, by Andrew Silver

Most Shared - Silver Badges (>300 shares)

3. How to Setup a Python Environment for Machine Learning, by George Seif
4. An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning, by George Seif
5. The Best and Worst Data Visualizations of 2018, by Dan Clark
6. Data Scientists: Why are they so expensive to hire?, by Adam Carrigan
7. Python Data Science for Beginners, by Saurabh Hooda
8. Intuitive Visualization of Outlier Detection Methods, by Matthew Mayo
9. Understanding Gradient Boosting Machines, by Harshdeep Singh (*)
10. Running R and Python in Jupyter, by Asel Mendis

(*) 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. Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters, by Jesse Vig
2. Running R and Python in Jupyter, by Asel Mendis
3. Artificial Neural Network Implementation using NumPy and Image Classification, by Ahmed Gad
4. Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019, by Altexsoft
5. Understanding Gradient Boosting Machines, by Harshdeep Singh
6. Top 10 Data Science Use Cases in Telecom, by ActiveWizards
7. 6 Books About Open Data Every Data Scientist Should Read, by Kayla Matthews