A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
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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:
- Statistics and Probability
- 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
- Elite Data Science
- 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
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
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
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
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