Author: Vincent Granville

Not all these contributions were from 2018, but the few selected below were among the most visited in 2018. Some were heavily featured, so it does not mean that they represent the average DSC interest. A bigger list featuring 900+ most popular articles can be found here. I am still working on categorizing them, and may hire an intern to work on this project, using material described in this article or this one.

- Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics
- Shooting yourself in the foot in various programming languages
- Advanced Machine Learning with Basic Excel
- 10 Open Source ETL Tools
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Python, SAS
- Free Book: Applied Stochastic Processes
- 10 Great Healthcare Data Sets
- The Death of the Data Scientist
- Top 6 Data Modeling Tools
- What you won’t learn in stats classes
- Artificial Intelligence vs. Machine Learning vs. Deep Learning
- Batch vs. Real Time Data Processing
- 40 Techniques Used by Data Scientists
- 50 Questions to Test True Data Science Knowledge
- How to Become a Data Scientist – On your own
- 24 Uses of Statistical Modeling (Part I)
- Big data sets available for free
- Introduction to Classification & Regression Trees (CART)
- 20 Cheat Sheets: Python, ML, Data Science, R, and More
- Is it still possible today to become a self-taught data scientist?
- Hitchhiker’s Guide to Data Science, Machine Learning, R, Python
- Some Thoughts on Mid-Career Switching Into Data Science
- How to Become a Data Scientist
- Difference Between Correlation and Regression in Statistics
- 10 types of regressions. Which one to use?
- 22 Differences Between Junior and Senior Data Scientists
- Six categories of Data Scientists
- 5 Myths About PhD Data Scientists
- Interesting Problem: Self-correcting Random Walks
- 10 great books about R
- How to Detect if Numbers are Random or Not
- Top 20 Python libraries for data science in 2018
- 12 Algorithms Every Data Scientist Should Know
- Python: Implementing a k-means algorithm with sklearn
- The Mathematics of Machine Learning
- Random Forests explained intuitively
- Nice Generalization of the K-NN Clustering Algorithm — Also Useful for Data Reduction
- Data Science and Machine Learning Without Mathematics
- Learn Real Data Science from Pros – Join Data Science Central
- Comprehensive Repository of Data Science and ML Resources

Likewise, one of my old academic papers published in 1994 in *IEEE Pattern Analysis and Machine Intelligence*, an obscure, very theoretical math paper, entitled “Simulated Annealing: a Proof of Convergence” is getting a lot of traction recently in AI circles – giving me an academic score better than many university professors. I will investigate this and try to figure out why it is so popular today.

**DSC Resources**

- Book and Resources for DSC Members
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions