This repository aims at compiling comprehensive resources for career development in data science.
Data science is relativey a new discipline. It started only in around 2010, and soon after, it became extrememly popular and Harvard Business Review named data scientist as the sexiest job in the 21st century.
Despite its popularity, the career paths in data science are not well defined. The roles, responsibilities, and reporting lines of data scientists vary from one organization to another. Also data science programs at schools and companies offer training with different focuses. Therefore, it is hard for aspiring data scientists to plan for their career development.
To simplify, there are three main career paths in data science, (1) data analytics, (2) machine learning research, and (3) machine learning engineering. While all three paths are relevent to any data scientists, one can progress one's career by focusing on either one or a combination of these paths.
In this repository, we are planning to add resouces in three main career paths, such as career advices and interviews of senior data scientists/managers (if you are one of these people, we may reach out to you. ;)).
Any contribution and feedback will be appreciated.
- Stanford CS230: Autumn 2018 Lecture 8 - Career Advice & Reading Research Papers by Andrew Ng at Stanford
- Consistent efforts, e.g. reading 2 papers every week, matters more than intense burst of work.
- Team you directly work with matters more than the brand of the company.
- Learn the most. Do important work
- An Opinionated Guide to ML Research by John Schulman at OpenAI
Start with competitions at Kaggle.
- Deep RL for Supply Chain and Price Optimization by Ilya Katsov
- Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Deep Learning with Python by François Chollet
- Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow by Aurélien Géron
- Deep Learning for Coders with fastai & PyTorch by Jeremy Howard
- Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur
- Forecasting: Principles and Practice by Rob J Hyndman, George Athanasopoulos
- Kaggle Winner's Blog: Winners' interviews at Kaggle's official blog.
- Chai Time Data Science: Sanyam Bhutani's interviews with top Kagglers, ML researchers and practitioners.