PNSuchismita/cracking-the-data-science-interview

A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep

Here are the sections:

• Data Science Cheatsheets
• Data Science EBooks
• Data Science Question Bank
• Data Science Case Studies
• Data Science Portfolio
• Data Journalism Portfolio
• Downloadable Cheatsheets

Data Science Cheatsheets

This section contains cheatsheets of basic concepts in data science that will be asked in interviews:

• SQL
• Statistics and Probability
• Mathematics
• Machine Learning Concepts
• Deep Learning Concepts
• Supervised Learning
• Unsupervised Learning
• Computer Vision
• Natural Language Processing
• Stanford Materials

Data Science EBooks

This section contains books that I have read about data science and machine learning:

• Intro To Machine Learning with Python
• Machine Learning In Action
• Python Data Science Handbook
• Doing Data Science - Straight Talk From The Front Line
• Machine Learning For Finance
• Practical Statistics for Data Science
• A/B Testing

Data Science Question Bank

This section contains sample questions that were asked in actual data science interviews:

• Data Interview Qs
• Data Science Prep
• Interview Query
• Analytics Vidhya
• Springboard
• Elite Data Science
• Workera
• 150 Essential Data Science Questions and Answers

Data Science Case Studies

This section contains case study questions that concern designing machine learning systems to solve practical problems.

Data Science Portfolio

This section contains portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

For a more visually pleasant experience for browsing the portfolio, check out jameskle.com/data-portfolio

• Recommendation Systems

  • Transfer Rec: My ongoing research work that intersects deep learning and recommendation systems.

  • Movie Recommendation: Designed 4 different models that recommend items on the MovieLens dataset.

  Tools: PyTorch, TensorBoard, Keras, Pandas, NumPy, SciPy, Matplotlib, Seaborn, Scikit-Learn, Surprise, Wordcloud

• Machine Learning

  • Trip Optimizer: Used XGBoost and evolutionary algorithms to optimize the travel time for taxi vehicles in New York City.

  • Instacart Market Basket Analysis: Tackled the Instacart Market Basket Analysis challenge to predict which products will be in a user's next order.

  Tools: Pandas, NumPy, Matplotlib, XGBoost, Geopy, Scikit-Learn

• Computer Vision

  • Fashion Recommendation: Built a ResNet-based model that classifies and recommends fashion images in the DeepFashion database based on semantic similarity.

  • Fashion Classification: Developed 4 different Convolutional Neural Networks that classify images in the Fashion MNIST dataset.

  • Dog Breed Classification: Designed a Convolutional Neural Network that identifies dog breed.

  • Road Segmentation: Implemented a Fully-Convolutional Network for semantic segmentation task in the Kitty Road Dataset.

  Tools: TensorFlow, Keras, Pandas, NumPy, Matplotlib, Scikit-Learn, TensorBoard

• Natural Language Processing

  • Classifying Tweets with Weights & Biases: Developed 3 different neural network models that classify tweets on a crowdsourced dataset in Figure Eight.

• Data Analysis and Visualization

  • World Cup 2018 Team Analysis: Analysis and visualization of the FIFA 18 dataset to predict the best possible international squad lineups for 10 teams at the 2018 World Cup in Russia.

  • Spotify Artists Analysis: Analysis and visualization of musical styles from 50 different artists with a wide range of genres on Spotify.

  Tools: Pandas, NumPy, Matplotlib, Rspotify, httr, dplyr, tidyr, radarchart, ggplot2

Data Journalism Portfolio

This section contains portfolio of data journalism articles completed by me for freelance clients and self-learning purposes.

For a more visually pleasant experience for browsing the portfolio, check out jameskle.com/data-journalism

• Statistics

  • The 10 Statistical Techniques Data Scientists Need to Master

  • Logistic Regression Tutorial

  • Decision Trees Tutorial

  • Support Vector Machines Tutorial

  • A Friendly Introduction to Data-Driven Marketing for Business Leaders

• Machine Learning

  • The 10 Algorithms Machine Learning Engineers Need to Know

  • 12 Useful Things to Know About Machine Learning

  • A Tour of The Top 10 Algorithms for Machine Learning Newbie

  • The 10 Data Mining Techniques Data Scientists Need For Their Toolbox

  • Clustering and Classification in E-Commerce

  • The ABCs of Learning to Rank

  • 6 Ways to Debug a Machine Learning Model

• Deep Learning

  • The 10 Deep Learning Methods AI Practitioners Need to Apply

  • The 8 Neural Network Architectures ML Researchers Need to Learn

  • The 5 Deep Learning Frameworks Every Serious Machine Learner Should Be Familiar With

  • The 5 Computer Vision Techniques That Will Change How You See The World

  • Convolutional Neural Networks: The Biologically-Inspired Model

  • Recurrent Neural Networks: The Powerhouse of Language Modeling

  • The 7 NLP Techniques That Will Change How You Communicate in the Future

  • The 5 Trends Dominating Computer Vision in 2018

  • The 3 Deep Learning Frameworks For End-to-End Speech Recognition That Power Your Devices

  • The 5 Algorithms for Efficient Deep Learning Inference on Small Devices

  • The 4 Research Techniques to Train Deep Neural Network Models More Efficiently

  • The 2 Hardware Architectures for Efficient Training and Inference of Deep Nets

  • 10 Deep Learning Best Practices to Keep in Mind in 2020

Downloadable Cheatsheets

These PDF cheatsheets come from BecomingHuman.AI.

1 - Neural Network Basics

2 - Neural Network Graphs

3 - Machine Learning with Emojis

4 - Scikit-Learn With Python

5 - Python Basics

6 - NumPy Basics

7 - Pandas Basics

8 - Data Wrangling With Pandas

9 - SciPy Linear Algebra

10 - Matplotlib Basics

11 - Keras

12 - Big-O