Python Data Analysis

Dive deeper into data analysis with the flexibility of Python and learn how its extensive range of scientific and mathematical libraries can be used to solve some of the toughest challenges in data analysis. Build your confidence and expertise and develop valuable skills in high demand in a world driven by Big Data with this expert data analysis book.

This data science tutorial will help you learn how to effectively retrieve, clean, manipulate, and visualize data and establish a successful data analysis workflow. Apply the impressive functionality of Python’s data mining tools and scientific and numerical libraries to a range of the most important tasks within data analysis and data science, and develop strategies and ideas to take control your own data analysis projects. Get to grips with statistical analysis using NumPy and SciPy, visualize data with Matplotlib, and uncover sophisticated insights through predictive analytics and machine learning with SciKit-Learn. You will also learn how to use the tools needed to work with databases and find out how Python can be used to analyze textual and social media data, as you work through this essential data science tutorial.

Python Data Analysis makes the difficult challenges of data analysis easier, offering you practical guidance that doesn’t reduce the complexity of the tasks and technology at hand but instead makes them much more manageable.

• Learn how to find, manipulate, and analyze data using Python
• Perform advanced, high performance linear algebra and mathematical calculations with clean and efficient Python code
• Explore predictive analytics and machine learning using SciKit-Learn with this Python machine learning tutorial
• Learn cluster and regression analysis
• Gain insights into textual data and social media with NLTK
• Create effective visualizations to present your data using Matplotlib
• Do more with your databases and explore the relationship between MongoDB and PyMongo
• Discover techniques and tricks for performance tuning and concurrency

Table of Contents

Chapter 1: Getting Started with Python Libraries
Chapter 2: NumPy Arrays
Chapter 3: Statistics and Linear Algebra
Chapter 4: pandas Primer
Chapter 5: Retrieving, Processing, and Storing Data
Chapter 6: Data Visualization
Chapter 7: Signal Processing and Time Series
Chapter 8: Working with Databases
Chapter 9: Analyzing Textual Data and Social Media
Chapter 10: Predictive Analytics and Machine Learning
Chapter 11: Environments Outside the Python Ecosystem and Cloud Computing
Chapter 12: Performance Tuning, Profiling, and Concurrency
Appendix A: Key Concepts
Appendix B: Useful Functions
Appendix C: Online Resources