I relised to create this repo about my way from 0 to data scientist. I'll be publish all name of books what I've read including assignment. I'll be publish on special chapter difficult example.
- Think Python: An Introduction to Software Design
- 1400 задач по программированию / Златопольский
- Data Science from Scratch second edition
- Python for data science Mckinney. Library Pandas most important library for cleaning data
- Software Engineering at Google
- The making of a Manager
- 1.The Linear Algebra Survival Guide. 2.Jim Hefferon Linear Algebra
- SQL for data analysis O'Reilly
- Pandas for Everyone Python Data Analysis by Daniel Y. Chen
- Statistics: Introductory statistics Douglas Shafer
- Active Calculus, David C. H. Austin, Matthew Boelkins, and Steven Schlicker - mathematical analysis
- Deep Learning for Coders with fastai and PyTorch O'Reilly
- TensorFlow here
- Pytorch: How Pytorch tensors’ backward() accumulates gradient
- The Unreasonable Effectiveness of Recurrent Neural Networks here
- Description here
- Basic Python + libraries
- SQL
- Statistics. Linear algebra. Mathematical analysis
- A/B testing
- Probability of theory
- Linear algorithms/search
- Cluster analysis
- Data Mining with Decision Trees
- Evaluation Metrics
- Bayesian belief network model
- Ensemble learning, stacking and blending
- NLP
- Machine Learning
- Deep Learning
- Model Retraning
Open-sourced software licensed under the MIT license.