daanz
Daan Zeeuwe

Data Science

#2

Practical Statistics for Data Scientists

2017 • 8 Readers • 395 pages

#3

Python for Data Analysis

2017 • 29 Readers • 676 pages • 4.3

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#4

Python Data Science Handbook

2016 • 6 Readers • 548 pages • 4

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#5

R for Data Science

2016 • 6 Readers • 492 pages • 5

#6

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

2015 • 8 Readers • 376 pages • 4

#7

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

2017 • 26 Readers • 819 pages • 5

#8

An Introduction to Statistical Learning

2013 • 24 Readers • 426 pages • 3.7

#9

Data Science From Scratch: First Principles with Python

2015 • 10 Readers • 500 pages • 3.8

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#10

Build a Career in Data Science

2020 • 1 Reader • 352 pages

#11

Cover 6

1 Reader • 301 pages

#12

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

2 Institute of Mathematical Statistics Monographs

2016 • 3 Readers • 496 pages

#13

Cover 3

2011 • 1 Reader • 3

#14

Data Science for Business

24 Readers • 3

#15

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

2015 • 232 Readers • 611 pages • 4.7

#16

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

2016 • 346 Readers • 274 pages • 3.7

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#17

Big Data

2013 • 39 Readers • 242 pages • 3.4

#18

The Data Science Handbook

2015 • 1 Reader • 285 pages

#19

Mining of Massive Datasets

2011 • 8 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 • 8 Readers • 735 pages

#23

Introduction to Probability

2002 • 1 Reader • 416 pages • 4

#24

Cover 0

2012 • 7 Readers • 214 pages

#25

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

2001 • 25 Readers • 768 pages • 4.3

#26

Data Mining and Machine Learning: Fundamental Concepts and Algorithms

2020 • 1 Reader • 779 pages

#27

Think Stats

2011 • 8 Readers • 137 pages

#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

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#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

31 Readers • 159 pages • 4

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#32

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

2019 • 2 Readers • 362 pages • 4

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#33

Cover 0

3 Readers • 256 pages

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#34

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

2018 • 4 Readers • 285 pages • 4

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#35

The Stuff of Thought: Language as a Window into Human Nature

#4 of 4 in Language and Human Nature Tetralogy

2007 • 61 Readers • 524 pages • 3.6

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#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

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#38

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

2020 • 1 Reader • 210 pages

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#39

Genius Makers

2021 • 28 Readers • 384 pages • 4.3

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#40

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

2023 • 1 Reader • 4

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#41

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

2021 • 1 Reader • 266 pages • 4

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#42

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

2021 • 1 Reader • 240 pages

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