a semi-structured guide to pursuing data science formed after hours of googling

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Running code

1. Install conda or miniconda.
2. Create the environment: With GPU (Windows):

   conda env create -f environment.yml [TODO]
   

   With CPU (Windows):

   conda env create -f environment_cpu.yml [TODO]
   

3. Make sure you can activate the environment:

   conda activate [pytorch/tensorflow]-[gpu/cpu]
   

4. cd into the relevant directory

   cd [category]/[book]
   

5. And run the tests:

   python check_cuda.py
   python check_cpu.py
   

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1. Programming Fundamentals

• R for Data Science - 2nd Edition

• MATLAB Programming Fundamentals - R2023a

• Fluent Python - 2nd Edition

• T-SQL Fundamentals - 4th Edition

2. Math Fundamentals

• A First Course in Probability - 10th Edition

• Introduction to Probability - 1st Edition

• An Introduction to Mathematical Statistics and Its Applications - 6th Edition

• Mathematical Statistics with Applications - 7th Edition

• OpenIntro Statistics - 4th Edition

• Probability, Statistics, and Data: A Fresh Approach Using R - 1st Edition

• Calculus, Linear Algebra, ODEs/PDEs

3. Modeling

• Applied Predictive Modeling - 1st Edition

• The Elements of Statistical Learning - 2nd Edition

• An Introduction to Statistical Learning with R - 2nd Edition

• An Introduction to Statistical Learning with Python - 2nd Edition

4. Data Engineering

• Python for Data Analysis - 3rd Edition

• Python Data Science Handbook - 2nd Edition

• SQL for Data Scientists - 1st Edition

5. Advanced Modeling (Frameworks, Deep Learning, Systems)

• Machine Learning with PyTorch and Scikit-Learn - 1st Edition

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - 3rd Edition

• Designing Machine Learning Systems - 1st Edition

• Dive Into Deep Learning - 1st Edition

• Artificial Intelligence: A Modern Approach - 4th Edition

6. Advanced Data Engineering (Platforms, Systems)

• Designing Data-Intensive Applications - 1st Edition

• Apache Spark

• Apache Hadoop

• Apache Flink

• Cloud tool (AWS, GCP, Azure, etc...)

• [TODO] data engineering/warehousing

7. Advanced Math

• Statistical Rethinking - 2nd Edition

• Statistical Inference - 2nd Edition

• Probability: Theory and Examples - 5th Edition

• An Introduction to Stochastic Modeling - 4th Edition

• Bayesian Data Analysis - 3rd Edition

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Interview Practice

• DataLemur
• stratascratch
• Deep-ML
• NeetCode
• Kaggle
• TidyTuesday

Online Courses/Certificates

• IBM Data Science Professional Certificate
• Machine Learning Specialization - Andrew Ng
• Deep Learning Specialization - Andrew Ng

Public Datasets

• Registry of Open Data on AWS
• OpenML
• UC Irvine Machine Learning Repository
• The Open Science Framework
• DATA.NASA.GOV
• U.S. Government's Open Data
• Office for National Statistics
• Papers With Code
• Hugging Face
• Kaggle
• GitHub awesome-public-datasets
• Databar
• Reddit r/statistics
• Google Dataset Search

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Course Texts:

• K. Korb and A. Nicholson, Bayesian Artificial Intelligence. (UCSD CSE 150a)
• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. (UCSD CSE 150a) (UCSD CSE 150b) (UCM CSE176)
• R. Sutton and A. Barto, Reinforcement Learning: An Introduction. (UCSD CSE 150a)
• The Elements of Statistical Learning by Trevor Hastie, ‎Robert Tibshirani, and Jerome Friedman. (UCSD CSE 151a)
• Data Mining: Concepts and Techniques by Jiawei Han et al. (UCSD CSE 151a)
• Pattern Recognition and Machine Learning by Christopher M. Bishop. (UCSD CSE 151a)
• Dive into Deep Learning book by Aston Zhang et al. (UCSD CSE 151a)
• Deep Learning (UCSD CSE 151b)
• Machine Learning Crash Course (UCSD CSE 151b)
• Learning From Data (UCSD CSE 151b)
• G. James, D. Witten, T. Hastie and R. Tibshirani: An Introduction to Statistical Learning, with Applications in R, 2nd ed. Springer, 2021 (UCM CSE176)
• R. O. Duda, P. E. Hart and D. G. Stork: Pattern Classification, 2nd ed. Wiley, 2001. (UCM CSE176)
• P. A. Flach: Machine Learning. The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, 2012 (UCM CSE176)
• S. Marsland: Machine Learning: An Algorithmic Perspective, 2nd ed. Chapman and Hall/CRC Press, 2014 (UCM CSE176)
• T. M. Mitchell: Machine Learning. McGraw-Hill, 1997 (UCM CSE176)