It is sometimes said that you don’t need to know math to be a data scientist. Sometimes the opposite is said, after all, data science is supposed to be a science! Regardless, below are a few of my articles featuring how data science and math can benefit from each other – not just math to solve data science problems, but also data science to solve math problems.
- Variance, Attractors and Behavior of Chaotic Statistical Systems
- New Family of Generalized Gaussian Distributions
- Gentle Approach to Linear Algebra, with Machine Learning Applications
- Confidence Intervals Without Pain
- Re-sampling: Amazing Results and Applications
- New Perspectives on Statistical Distributions and Deep Learning
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- New Perspective on the Central Limit Theorem and Statistical Testing
- How to Lie with P-values
- A Strange Family of Statistical Distributions
- Six Degrees of Separation Between Any Two Data Sets
- Book: Statistics: New Foundations, Toolbox, and Machine Learning Recipes
- Book: Applied Stochastic Processes
- Comprehensive Repository of Data Science and ML Resources
Enjoy the reading!