daanz
Daan Zeeuwe

Data Science

#2

Practical Statistics for Data Scientists

2017 • 1 Reader • 395 pages

#3

Python for Data Analysis

2017 • 15 Readers • 676 pages • 4.3

#4

Python Data Science Handbook

2016 • 2 Readers • 548 pages • 4

#5

R for Data Science

2016 • 6 Readers • 492 pages • 5

#6

Introduction to Machine Learning with Python: A Guide for Data Scientists

2015 • 1 Reader • 376 pages

#7

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

2017 • 19 Readers • 819 pages • 5

#8

An Introduction to Statistical Learning

2013 • 17 Readers • 426 pages • 5

#9

Data Science From Scratch: First Principles with Python

2015 • 7 Readers • 500 pages • 3.8

#10

Build a Career in Data Science

2020 • 1 Reader • 352 pages

#11

Cover 6

1 Reader • 301 pages

#12

Computer Age Statistical Inference

#13

Cover 3

2011 • 1 Reader • 3

#14

Data Science for Business

22 Readers • 3

#15

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

2015 • 160 Readers • 611 pages • 4.6

#16

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

2016 • 264 Readers • 274 pages • 3.7

#17

Big Data

2013 • 29 Readers • 242 pages • 3.4

#18

The Data Science Handbook

2015 • 1 Reader • 285 pages

#19

Mining of Massive Datasets

2011 • 4 Readers • 480 pages

#20

Neural Networks and Deep Learning: A Textbook

2018 • 1 Reader • 496 pages

#21

Cover 0

1 Reader

#22

Bayesian Reasoning and Machine Learning

2011 • 1 Reader • 735 pages

#23

Introduction to Probability

2002 • 1 Reader • 416 pages • 4

#24

Cover 0

2012 • 3 Readers • 214 pages

#25

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

2001 • 6 Readers • 768 pages

#26

Data Mining and Machine Learning: Fundamental Concepts and Algorithms

2020 • 1 Reader • 779 pages

#27

Think Stats

2011 • 2 Readers

#28

Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics

2020 • 1 Reader • 347 pages • 4

#29

The Art of Data Science: A Guide for Anyone Who Works with Data

2015 • 1 Reader • 154 pages • 4

#30

Cover 4

2013 • 1 Reader • 178 pages

#31

Cover 0

10 Readers • 159 pages • 2

#32

Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

2019 • 1 Reader • 362 pages • 4

#33

Cover 0

3 Readers • 256 pages

#34

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

1 Reader

#35

The Stuff of Thought

2007 • 30 Readers • 499 pages • 3.3

#36

Markov Chains and Decision Processes for Engineers and Managers

2009 • 1 Reader • 478 pages

#37

Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python

1 Reader • 211 pages • 4

#38

Leadership by Algorithm: Who Leads and Who Follows in the AI Era?

2020 • 1 Reader • 210 pages

#39

Genius Makers

2021 • 16 Readers • 384 pages • 4

#40

Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs

2023 • 1 Reader • 4

#41

Crypto Technical Analysis: Your One-Stop Guide to Investing, Trading, and Profiting in Crypto with Technical Analysis.

2021 • 1 Reader • 266 pages • 4

#42

Minding the Machines: Building and Leading Data Science and Analytics Teams

2021 • 1 Reader • 240 pages