# Probability & Statistics for Data Science (Series)

This is the pilot post of blog post series ‘Probability & Statistics for Data Science’, this post covers the context, table of content & links to upcoming posts of this series topic-wise.

I haven’t attended any formal education in probability & statistics, whatever I have learnt in bits and pieces till now is through working on data science problems. When I look at the literature available on probability & statistics, I find it too theoretical and generalized. I have felt that there should be some literature on probability & statistics specifically focused on data science.

Recently couple of books have been written like ‘Practical Statistics for Data Scientists: 50 Essential Concepts’ by Peter Bruce/Andrew Bruce, which are good and cover some of the context but I want to cover everything about probability & statistics from basics to statistical learning. I would like to mention that my focus in these posts would be to give intuition on every topic and how it relates to data science rather going deep into mathematical formulas.

This series will contain 6 posts, this one is the pilot which gives an overview and set the context of subsequent posts.

Second post will cover probability & its types, random variables & probability distributions and how they are important from data science perspective.

Probability

• Introduction
• Conditional Probability
• Random Variables
• Probability Distributions

Third, Fourth & Fifth posts will cover every topic related to statistics & its significance in data science.

Statistics

• Introduction
• Descriptive Statistics
• Inferential Statistics
• Bayesian Statistics

Sixth (final) post will cover statistical learning, it will be about looking at machine learning or data science from statistical perspective.

Statistical Learning

• Introduction
• Prediction & Inference
• Parametric & Non-parametric methods
• Prediction Accuracy and Model Interpretability