- ankitrathi

# Probability & Statistics for Data Science (Series)

Updated: Jan 18

*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.*

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 content on probability & statistics specifically focused on data science.

I want to cover here 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.

*Before continuing with this post, if you are loving the content, check out my post on ‘How to launch your DS/AI Career in 12 weeks?’*

__How to launch your DS/AI career in 12 weeks?__

__How to launch your DS/AI career in 12 weeks?__

*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

__Descriptive Statistics for Data Science__

__Descriptive Statistics for Data Science__

Inferential Statistics

__Inferential Statistics for Data Science__

__Inferential Statistics for Data Science__

Bayesian Statistics

__Bayesian Statistics for Data Science__

__Bayesian Statistics for Data Science__

*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**Bias-Variance Trade-Off*

__Statistical Learning for Data Science__

__Statistical Learning for Data Science__

So if you are looking for a similar kind of learning curve, kindly stay tuned for my upcoming posts.

If you liked this post, have a look at this post where I talk about how Kaggle is similar (and different from real-world DS/AI projects).

__To Kaggle Or Not To Kaggle?__

__To Kaggle Or Not To Kaggle?__

*Ankit Rathi* *is an AI architect, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture.*

*Why don’t you connect with Ankit on* *Twitter**,* *LinkedIn* *or* *Instagram*