Cracking the Data Science Interview

Welcome to the Cracking the Data Science Interview Github page. Here you will find data science related links, tutorials, blog posts, code snippets, interview prep material, case studies, and more! Have fun!

Data Science Interview Preparation Materials

General

• How Do I Prepare For a Data Science Interview (Quora)

• How to Ace a Data Science Interview (Blog)

• How To Learn Data Science If You’re Broke (Towards Data Science)

• Data Science Interview Questions (PDF)

• 120 Data Science Interview Questions

• Kaggle Kernels

• Data Science Cheatsheets (Github Repo)

Online Resources for Practice

• Leetcode (Over 1350 Qustions To Practice Coding)

• HackerRank (Coding)

• SQLZoo (Place to Practice SQL)

• SQLCourse- Interactive Online SQL Training

Data Science Interview Prep Material

Mathematical Prequisites

Statistics

• Statistics for Data Science (Blog)

• The Math Behind A/B Testing with Example Python Code (Towards Data Science)

Probability

• Probability Cheatsheet (PDF), (Github Repo)

• Basics of Probability for Data Science explained with examples in R (Analytics Vidhya)

• What is an intuitive explanation of Bayes' Rule? (Quora)

Linear Algebra

• Linear Algebra Cheat Sheet for Deep Learning (Towards Data Science)

• No Bullshit Guide to Linear ALgebra- Linear algebra explained in four pages (PDF)

Computer Science

Data Structures

• A Data Scientist’s Guide to Data Structures & Algorithms, Part 1 (Towards Data Science)

• A Data Scientist’s Guide to Data Structures & Algorithms, Part 2 (Towards Data Science)

• Python Data Science Handbook

Algorithms

• Top Algorithms/Data Structures/Concepts every computer science student should know

Databases

• CAP Theorem (Wikipedia)
• Choosing The Right Database (Towards Data Science)

SQL

• How To Ace Data Science Interviews: SQL (Towards Data Science)

Python Packages/Libraries

• Pandas

• NumPy

• SciPy

• Scikit-learn

• Statsmodels

• PySpark

• Matplotlib

• IPython

• SymPy

Data Wrangling

• Fundamental Techniques of Feature Engineering for Machine Learning

• The Ultimate Guide to Data Cleaning

Machine Learning

• A Tour of Machine Learning Algorithms (Blog)

• 41 Essential Machine Learning Interview Questions (Blog)

Supervised Learning Algorithms

• Linear Regression — Detailed View

• 7 Regression Techniques you should know!

• Naive Bayes Classification — Theory

• SVM (Support Vector Machine) — Theory

• Decision Trees - Explained, Demystified and Simplified

• An Implementation and Explanation of the Random Forest in Python

• A Kaggle Master Explains Gradient Boosting

• A Gentle Introduction to XGBoost for Applied Machine Learning

Unsupervised Learning Algorithms

• Unsupervised Learning and Data Clustering

• Understanding K-means Clustering in Machine Learning

• Hierarchical Clustering

• Introduction to Autoencoders

• An Intuitive Introduction to Generative Adversarial Networks

Reinforcement Learning Algorithms

• Applications of Reinforcement Learning in Real World (Towards Data Science)

• Open AI Gym

• Simple Reinforcement Learning Methods to Learn CartPole (Blog)

• An introduction to Policy Gradients with Cartpole and Doom (freeCodeCamp)

• Implementation of Reinforcement Learning Algorithms (Github Repo)

Miscellaneous

• A Gentle Introduction To Graph Theory

• How to get started with machine learning on graphs

• Solving the Knapsack Problem with Dynamic Programming

• An Overview of Monte Carlo Methods

• Introductory Guide on Linear Programming

• ARIMA Models

Product

• Key Performance Indicators

Data Science Case Studies

• Uber Data Science

• BCG Gamma

• McKinsey Solutions

• Bain Advanced Analytics

• Facebook Data Science

• Kaggle

Books

• Cracking the Coding Interview

• The Hundred-Page Machine Learning Book

• Deep Learning

• Practical Statistics for Data Scientists: 50 Essential Concepts

• Introduction to Algorithms

Blogs

• Machine Learning Mastery

• GeeksforGeeks

• No Free Hunch- The Official Blog of Kaggle.com

• Towards Data Science

• The Art of Data Science

Courses

• CS 4780- Machine Learning for Intelligent Systems (Cornell) | Homepage, Video Lectures, Written Assignments