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Practical Data Science with Python

You're reading from   Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801071970
Length 620 pages
Edition 1st Edition
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Author (1):
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Nathan George Nathan George
Author Profile Icon Nathan George
Nathan George
Nathan George is a data scientist at Tink in Stockholm, Sweden, and taught data science as a professor at Regis University in Denver, CO for over 4 years. Nathan has created online courses on Pythonic data science and uses Python data science tools for electroencephalography (EEG) research with the OpenBCI headset and public EEG data. His education includes the Galvanize data science immersive, a PhD from UCSB in Chemical Engineering, and a BS in Chemical Engineering from the Colorado School of Mines.
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Toc

Table of Contents (30) Chapters Close

Preface 1. Part I - An Introduction and the Basics
2. Introduction to Data Science FREE CHAPTER 3. Getting Started with Python 4. Part II - Dealing with Data
5. SQL and Built-in File Handling Modules in Python 6. Loading and Wrangling Data with Pandas and NumPy 7. Exploratory Data Analysis and Visualization 8. Data Wrangling Documents and Spreadsheets 9. Web Scraping 10. Part III - Statistics for Data Science
11. Probability, Distributions, and Sampling 12. Statistical Testing for Data Science 13. Part IV - Machine Learning
14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction 15. Machine Learning for Classification 16. Evaluating Machine Learning Classification Models and Sampling for Classification 17. Machine Learning with Regression 18. Optimizing Models and Using AutoML 19. Tree-Based Machine Learning Models 20. Support Vector Machine (SVM) Machine Learning Models 21. Part V - Text Analysis and Reporting
22. Clustering with Machine Learning 23. Working with Text 24. Part VI - Wrapping Up
25. Data Storytelling and Automated Reporting/Dashboarding 26. Ethics and Privacy 27. Staying Up to Date and the Future of Data Science 28. Other Books You May Enjoy
29. Index

Index

A

A/B testing 267, 268, 270

methods 270, 271

academic sources, data science 568

accuracy 357, 358

AdaBoost

working 442, 444

ADASYN sampling

adjusted R2 394

advanced search methods 418

Akaike Information Criterion (AIC) 334, 394

Alexa 550

Anaconda

installing 26

used, for installing Python 26

ANOVA 298, 299, 300

assumptions 274

used, for testing between several groups 272

Anvil 544

API wrappers

using 232, 233

Application Programming Interface (API) 74

using, to collect data 229, 230, 231

application programming interfaces (APIs) 16

apply function

using 129

artificial intelligence (AI) 18

Artificial Intelligence* Foundations of Computational Agents

reference link 20

AUC score 368, 372

autocorrelation 400

automated dashboarding 544, 545

automated reporting 544

options 544

automatic machine learning (AutoML) 6

AutoML

solutions 421...

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Authors (1)

Profile icon Nathan George
Nathan George
Nathan George is a data scientist at Tink in Stockholm, Sweden, and taught data science as a professor at Regis University in Denver, CO for over 4 years. Nathan has created online courses on Pythonic data science and uses Python data science tools for electroencephalography (EEG) research with the OpenBCI headset and public EEG data. His education includes the Galvanize data science immersive, a PhD from UCSB in Chemical Engineering, and a BS in Chemical Engineering from the Colorado School of Mines.
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