YikangGui / Data-Science-Interview-Resources

A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

Data-Science-Interview-Resources

With an increase in demand for so many Data Scientists, it's really hard to successfully get screened and accepted for an interview. Now that you have got one, make sure to nail it with the following resources.

Every Resource I list here is personally verified by me and most of them I have used personally, which have helped me a lot in fetching my first Data Science job.

Word of Caution: Data Science/Machine Learning has a very big domain and there are a lot of things to learn. This by no means is an exhaustive list and is just for helping you out if you are struggling to find some good resources to start your preparation.

Note: For contribution, refer Contribution.md

Video Resources

• Common Data Science Interview Questions - Edureka
• Common Machine Learning Interview Question - Edureka
• Top 5 algorithms used in Data Science
• Common Data Science Interview Questions - Analytics University
• 3 types of Data Science Interview Questions
• Lessons learned the hard way - Hacking the Data Science Interview
• What it's like to Interview as a Data Scientist
• 5 Tips for getting a Data Science Job
• 8 Frequently used Data Science Algorithms
• How to Select Features
• Linear Regression - Understand Everything (Theory + Maths + Coding) in 1 video
• Logistic Regression - Understand Everything (Theory + Maths + Coding) in 1 video
• Naive Bayes - Understand Everything (Theory + Maths + Coding) in 1 video
• KNN Algorithm - Understand Everything (Theory + Maths + Coding) in 1 video
• The Complete Machine Learning Interview Preparation Playlist
• Dimensionally Reducing Squeezing out the good stuuf
• Why Regularization reduces overfitting in Deep Neural Networks
• Scenario Based Practical Interview
• KNN v/s K Means
• Bias and Variance - Very clearly explained
• Logistic Regression - Short and Clear Explanation
• Vanishing and Exploding Gradient - Clearly Explained
• Probability v/s Likelihood
• Ridge Regression - Clearly Explained
• Lasso Regression - Clearly Explained

Reading Resources

• The Data Science Interview Guide
• 35 Important Data Science Interview Questions
• The Most Comprehensive Data Science Interview Guide
• 41 essential ML interview questions - Springboard
• 109 Data Science Interview Questions - Springboard
• Most asked Data Science interview questions in India - Springboard
• List of AI Startups in India and resources for preparing for the interview
• 5 interview questions to predict a good Data Scientist
• 8 proven ways to improve the accuracy of your ML model
• 30 Questions to test a Data Scientist on Linear Regression
• 30 Questions to test a Data Scientist on KNN
• 12 tips to make most out of Naive Bayes
• Smarter ways to encode categorical data
• The Big List of DS and ML interview Resources
• 100 Basic Data Science Interview Questions along with answers
• 40 interview questions asked at Startups in ML/DS Interview
• Understand the basics of Descriptive Statistics(Really Important for an interview)
• The DOs and DONTs of PCA(Principal Component Analysis)
• An Introduction to SVMs
• An introduction to t-SNE : DataCamp
• Get your Data Science Resume past the ATS
• Probability and Statistics in the context of Deep Learning
• Pros and Cons of Neural Networks
• My Data Science/Machine Learning Job Interview Experience : List of DS/ML/DL Questions – Machine Learning in Action
• How do I prepare for a Data Science phone interview at Airbnb
• Best ML algorithm for regression problems
• How to ace the In person Data Science Interview
• Advice on building Data Portfolio Projects
• Feature Selection Techniques
• Bootstrap Methods - The Swiss Army Knife of any Data Scientist
• How to land a Data Scientist job at Airbnb
• A Data Scientist's guide to Data Structures and Algorithms
• 120 Data Science Interview Questions(from all domains)
• 40 Question on probability for a Data Science Interview
• When to use which plot for visualization
• Understanding the Bias-Variance Tradeoff
• You Need these Cheatsheets if you are tackling ML algorithms
• Ways to detect and remove Outliers
• 3 Methods to deal with outliers
• Red Flags in a Data Science Interview
• Understand these 4 ML concepts to sound like a master
• Comprehensive guide to Ensemble Models
• Activation Functions in a Neural Network - Explained
• Kaggle Data Science Glossary
• Google Machine Learning Glossary
• Why use ReLU over Sigmoid
• A Data Scientist's take on Interview Questions
• What is Cross Entropy(Nice and Short Explanation)
• What does an ideal Data Scientist's profile look like
• Dimensionality Reduction for Dummies : Part 1 - Intuition
• 25 Fun Questions for a Machine Learning interview
• Dealing with Class Imbalances in Machine Learning
• How to Prepare for Machine Learning Interviews
• How to develop a Machine Learning Model from scratch
• End to End guide for a Machine Learning Project
• 6 easy steps to learn Naive Bayes
• Classification v/s Regression
• Must Know mathematical measures for Every Data Scientist
• Where did the least square come from
• Why, how and When to scale your features
• Regularization in Machine Learning - Explained
• Understand the Data Science pipeline
• Gentle Introduction to EDA
• Numpy and Pandas Cheatsheet

Additional/Interesting Reads

• 3 Common Data Science Career Transitions and how to make them happen
• How to deploy a Keras model as a web app through Flask
• How to solve 90% of NLP Problems
• Navigating the Data Science Career Landscape
• 2 way to deploy your ML models
• Which model and how much data
• Implementation of SMOTE algorithm in Python to handle class imbalance