Data science bookcamp : five real-world Python projects / Leonard Apeltsin.

Format:

Book

Author/Creator:

Apeltsin, Leonard, author.

Language:

English

Subjects (All):

Data mining.

Data sets.

Python (Computer program language).

Physical Description:

1 online resource (576 pages)

Edition:

[First edition].

Place of Publication:

Shelter Island, New York : Manning Publications Company, [2021]

Summary:

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you'll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you've learned, building your confidence and making you ready for an exciting new data science career.

Contents:

1. Computing probabilities using Python

2. Plotting probabilities using Matplotlib

3. Running random simulations in NumPy

4. Case study 1 solution

5. Basic probability and statistical analysis using SciPy

6. Making predictions using the central limit theorem and SciPy

7. Statistical hypothesis testing

8. Analyzing tables using Pandas

9. Case study 2 solution

10. Clustering data into groups

11. Geographic location visualization and analysis

12. Case study 3 solution

13. Measuring text similarities

14. Dimension reduction of matrix data

15. NLP analysis of large text datasets

16. Extracting text from web pages

17. Case study 4 solution

18. An introduction to graph theory and network analysis

19. Dynamic graph theory techniques for node ranking and social network analysis

20. Network-driven supervised machine learning

21. Training linear classifiers with logistic regression

22. Training nonlinear classifiers with decision tree techniques

23. Case study 5 solution.

Notes:

Description based on print version record.

Subtitle on cover: five real-world Python projects.

Includes bibliographical references and index.

ISBN:

9781638352303

1638352305

9781617296253

1617296252

OCLC:

1287131619

Publisher Number:

9781617296253AU (electronic audio bk.)