Data Science Roadmap from Google Data Scientist

I know how hard it is to search for a job. I personally had to apply to over 3000 jobs to get an internship and full time job. And this is exactly why, I provide all my resources and information for free. and I hope that even 1% of this can help you in your career. At the same time, I do this all by myself and don’t have anyone to help or any marketing budget to work with. So, if you find this article helpful, consider supporting me by making a donation through buymeacoffee , becoming a paid member of substack or susbcribing to my Youtube page.

DATA SCIENCE EVERYTHING

I have 3 of my friends who worked at Google as Data Scientist. The compensation for each of them is over $180K just as base salary. And this weekend , we spent time to devise a roadmap to help new comers also crack data science roles.

SQL

150+ SQL Interview Questions:

Here is the complete excel sheet of over 300+ questions for SQL which will help you ace any interview : Link.

Even practice one question a day will help you master your skills.

SQL Theory Questions

For SQL Theory Questions, I used this resource : Link which has 100+ SQL Theory questions and literally everything you need to prepare for your interview. Use this and you will be set for life in SQL Theory questions.

PYTHON

Python For Data Science : Link

The link contains everything you need to know for Python for Data Science.

CHEATSHEETS :

SQL Cheatsheet : Link

Cheatsheets from BecomingHuman.AI

Data Science Portfolio Projects

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

Collection of Data Science Projects : Link

6 MONTH DATA SCIENCE ROADMAP : Link