Math-Statistics-DataScience

Math and Statistics for Data Science

Topics

• Descriptive Statistics: useful for data exploration
  • Central Tendency
  • Variability
  • Correlation
• Distribution Types (Uniform, Binomial, Poisson, Normal): useful for data exploration
• Inferential Statistics: useful for data exploration and hypothesis testing
  • Linear regression
  • Visualization (Bar charts, Histograms, box plots, scatter plots)
• Linear Algebra: useful for PCA & Regression
• Calculus: useful for gradient decent

Python Libraries

• Numpy
• Scipy
• Pandas

References

Main References

• Math for Data Science (Learning Path) – Real Python
• Probabilistic Machine Learning: An Introduction
• ML for everyone
• Math is Fun

Probabilistic Classifiers

• Naive Bayes with SKLearn
• Classifier Comparison
• SKLearn Docs

Naive Bayes Examples:

• Glass dataset
• Titanic Dataset
• Adult dataset

Regression Machine learning Models

• Linear Model
• Linear Regression
• Linear Regression Example
• Polynomial Regression

Logistic Regression

• Logistic Regression in Python

Calculus

• Introduction to derivatives

Python Env

Anaconda https://repo.anaconda.com/archive/Anaconda3-2022.10-Windows-x86_64.exe

VS code

https://code.visualstudio.com/Download

Video Lectures

https://youtube.com/playlist?list=PL39RMbpB79NPKxyyAE6g2HFOM1sTXLrqU

Practice Numpy at Hacker Rank

Practice Numpy at Hacker Rank

100 Numpy exercises

https://github.com/rougier/numpy-100