This book looks at you how to analyze data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, Stefanie Molin shows how to use the pandas library to perform data wrangling to reshape, clean, and aggregate data. Later chapters cover conducting exploratory data analysis by calculating summary statistics and visualizing the data to find patterns.
Author: Stefanie Molin Publisher: Packt Publishing Date: April 2021 Pages: 788 ISBN: 978-1800563452 Print: 1800563450 Kindle: B08R67H7F5 Audience: Panda users Level: Intermediate Category: Data Science
- Understand how data analysts and scientists gather and analyze data
- Perform data analysis and data wrangling using Python
- Combine, group, and aggregate data from multiple sources
- Create data visualizations with pandas, matplotlib, and seaborn
- Apply machine learning algorithms to identify patterns and make predictions
- Use Python data science libraries to analyze real-world datasets
- Solve common data representation and analysis problems using pandas
- Build Python scripts, modules, and packages for reusable analysis code
For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.
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Data Structures & Algorithms in Python
Author: Dr. John Canning, Alan Broder and Robert Lafore Publisher: Addison-Wesley Date: October 2022 Pages: 928 ISBN:978-0134855684 Print: 013485568X Kindle: B0B1WJF1K9 Audience: Python developers Rating: 4 Reviewer: Mike James Data structures in Python - a good idea!
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SQL Server 2022 Administration Inside Out
Author: Randolph West et al Publisher: Microsoft Press Pages: 992 Print: 0137899882 ISBN: 978-0137899883 Kindle: B0C4VKVP27 Audience: DBAs and developers Rating: 5.0 Reviewer: Ian Stirk
This book aims to update your DBA skills to cover SQL Server 2022, how does it fare?
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