#2 | Practical Statistics for Data Scientists | 0 | 0 reads | |
#3 | | 4.33 | 6 reads | |
#4 | Python Data Science Handbook | 4 | 1 read | |
#5 | R for Data Science - Hadley Wickham
- Garrett Grolemund
| 5 | 4 reads | |
#6 | Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C. Müller
- Sarah Guido
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#7 | Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems | 5 | 2 reads | |
#8 | An Introduction to Statistical Learning - Daniela Witten
- Trevor Hastie
- Robert Tibshirani
- Gareth James
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#9 | Data Science From Scratch: First Principles with Python | 4 | 2 reads | |
#10 | Build a Career in Data Science - Emily Robinson
- Jacqueline Nolis
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#12 | Computer Age Statistical Inference | 0 | 0 reads | |
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#14 | Data Science for Business - Foster Provost
- Tom Fawcett
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#15 | Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems | 4.65 | 55 reads | |
#16 | Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy | 3.69 | 88 reads | |
#17 | Big Data - Viktor Mayer-Schönberger
- Kenneth Cukier
| 3.4 | 13 reads | |
#18 | The Data Science Handbook - Carl Shan
- Henry Wang
- William Chen
- Max Song
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#19 | Mining of Massive Datasets - Anand Rajaraman
- Jeffrey David Ullman
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#20 | Neural Networks and Deep Learning: A Textbook | 0 | 1 read | |
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#22 | Bayesian Reasoning and Machine Learning | 0 | 1 read | |
#23 | Introduction to Probability - Dimitri P. Bertsekas
- John N. Tsitsiklis
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#24 | | 0 | 1 read | |
#25 | The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie
- Robert Tibshirani
- Jerome Friedman
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#26 | Data Mining and Machine Learning: Fundamental Concepts and Algorithms - Mohammed J. Zaki
- Wagner Meira Jr.
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#27 | | 0 | 0 reads | |
#28 | Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics | 4 | 1 read | |
#29 | The Art of Data Science: A Guide for Anyone Who Works with Data - Roger D. Peng
- Elizabeth Matsui
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#32 | Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data | 4 | 1 read | |
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#34 | Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning | 0 | 0 reads | |
#35 | | 3.29 | 8 reads | |
#36 | Markov Chains and Decision Processes for Engineers and Managers | 0 | 0 reads | |
#37 | Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python | 4 | 1 read | |
#38 | Leadership by Algorithm: Who Leads and Who Follows in the AI Era? | 0 | 0 reads | |
#39 | | 4 | 5 reads | |
#40 | Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs | 4 | 1 read | |
#41 | Crypto Technical Analysis: Your One-Stop Guide to Investing, Trading, and Profiting in Crypto with Technical Analysis. | 4 | 1 read | |
#42 | Minding the Machines: Building and Leading Data Science and Analytics Teams | 0 | 0 reads | |