Data science essentials in Python : collect - organize - explore - predict - value / Dmitry Zinoviev.

Format:

Book

Author/Creator:

Zinoviev, Dmitry, author.

Contributor:

Dvorak, Katharine, editor.

Series:

Pragmatic programmers.

Pragmatic Programmers

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (208 pages) : illustrations (some color).

Edition:

1st edition

Other Title:

Data science essentials in Python

Place of Publication:

Raleigh, North Carolina : The Pragmatic Bookself, 2016.

System Details:

text file

Summary:

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.

Contents:

Cover

Table of Contents

Acknowledgments

Preface

About This Book

About the Audience

About the Software

Notes on Quotes

The Book Forum

Your Turn

1. What Is Data Science?

Unit 1. Data Analysis Sequence

Unit 2. Data Acquisition Pipeline

Unit 3. Report Structure

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

3. Working with Text Data

Unit 13. Processing HTML Files

Unit 14. Handling CSV Files

Unit 15. Reading JSON Files

Unit 16. Processing Texts in Natural Languages

4. Working with Databases

Unit 17. Setting Up a MySQL Database

Unit 18. Using a MySQL Database: Command Line

Unit 19. Using a MySQL Database: pymysql

Unit 20. Taming Document Stores: MongoDB

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

6. Working with Data Series and Frames

Unit 31. Getting Used to Pandas Data Structures

Unit 32. Reshaping Data

Unit 33. Handling Missing Data

Unit 34. Combining Data

Unit 35. Ordering and Describing Data

Unit 36. Transforming Data

Unit 37. Taming Pandas File I/O.

Your Turn

7. Working with Network Data

Unit 38. Dissecting Graphs

Unit 39. Network Analysis Sequence

Unit 40. Harnessing Networkx

8. Plotting

Unit 41. Basic Plotting with PyPlot

Unit 42. Getting to Know Other Plot Types

Unit 43. Mastering Embellishments

Unit 44. Plotting with Pandas

9. Probability and Statistics

Unit 45. Reviewing Probability Distributions

Unit 46. Recollecting Statistical Measures

Unit 47. Doing Stats the Python Way

10. Machine Learning

Unit 48. Designing a Predictive Experiment

Unit 49. Fitting a Linear Regression

Unit 50. Grouping Data with K-Means Clustering

Unit 51. Surviving in Random Decision Forests

A1. Further Reading

A2. Solutions to Single-Star Projects

Bibliography

Index

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- Z -.

Notes:

Place of publication from publisher's website.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (EBC, viewed March 14, 2018).

ISBN:

9781680502237

1680502239

9781680503395

1680503391

9781680503388

1680503383

9781680501841

1680501844

OCLC:

960471156