Book description
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python.
Publisher resources
Table of contents
- Acknowledgments
- Preface
- 1. What Is Data Science?
-
2. Core Python for Data Science
- Unit 4. Understanding Basic String Functions
- Unit 5. Choosing the Right Data Structure
- Unit 6. Comprehending Lists Through List Comprehension
- Unit 7. Counting with Counters
- Unit 8. Working with Files
- Unit 9. Reaching the Web
- Unit 10. Pattern Matching with Regular Expressions
- Unit 11. Globbing File Names and Other Strings
- Unit 12. Pickling and Unpickling Data
- Your Turn
- 3. Working with Text Data
- 4. Working with Databases
-
5. Working with Tabular Numeric Data
- Unit 21. Creating Arrays
- Unit 22. Transposing and Reshaping
- Unit 23. Indexing and Slicing
- Unit 24. Broadcasting
- Unit 25. Demystifying Universal Functions
- Unit 26. Understanding Conditional Functions
- Unit 27. Aggregating and Ordering Arrays
- Unit 28. Treating Arrays as Sets
- Unit 29. Saving and Reading Arrays
- Unit 30. Generating a Synthetic Sine Wave
- Your Turn
- 6. Working with Data Series and Frames
- 7. Working with Network Data
- 8. Plotting
- 9. Probability and Statistics
- 10. Machine Learning
- A1. Further Reading
- A2. Solutions to Single-Star Projects
- Bibliography
Product information
- Title: Data Science Essentials in Python
- Author(s):
- Release date: August 2016
- Publisher(s): Pragmatic Bookshelf
- ISBN: 9781680501841
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