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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?

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


Probability for Data Science


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


  • Inferential Statistics

Inferential Statistics for Data Science


  • Bayesian Statistics

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


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?

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.

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