Data Science: Do We Really Need Math?

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!

Reference: Source link

Sr. SDET M Mehedi Zaman

Currently working as Sr. SDET at Robi Axiata Limited, a subsidiary of Axiata Group.

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