sarthack99 / cracking-the-data-science-interview

A Collection of Books/Resources That I Used to Prepare For Data Science Interviews

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 to incorporate transfer learning into the design of 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

• 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

• 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

  • 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