Python is ideal for accessing, manipulating, and gaining insights from all kinds of data. Python for Data Science introduces you to the data analysis world of Python through a learn-by-doing approach rooted in real-world examples and hands-on activities. You'll learn how to write Python code to acquire, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.
You'll discover Python's rich set of built-in data structures for basic operations, as well as a robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more . These examples show how to load data in various formats, how to simplify, group, and aggregate datasets, and how to create charts, maps, and other visualizations. Subsequent chapters will delve into real-world data applications, including the use of location data to drive taxi services, market basket analysis to identify co-purchased items, and machine learning to predict stock prices.
https://nostararch.com/python-data-science
Introduction
Chapter 1: The Basics of Data
Chapter 2: Python Data Structures
Chapter 3: Python Data Science Libraries
Chapter 4: Accessing Data from Files and APIs
Chapter 5: Working with Databases
Chapter 6: Aggregating Data
Chapter 7: Combining Datasets
Chapter 8: Creating Visualizations
Chapter 9: Analyzing Location Data
Chapter 10: Analyzing Time Series Data
Chapter 11: Gaining Insights from Data
Chapter 12: Machine Learning for Data Analysis
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