Become a Python Data Analyst: Building Predictive Models with Pandas, Scikit-learn, NumPy and more

Alvaro Fuentes

€103.99

Video May 2017 4hrs 30mins 1st Edition

Alvaro Fuentes

€103.99

Video May 2017 4hrs 30mins 1st Edition

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Key benefits

Aimed for the beginner, this course contains in one place all you need to start analyzing data with Python
Learn the foundations for doing Data Science and Predictive Analytics with Python through real-world examples
Learn how ask questions and answer them effectively with the most widely used visualization and data analysis techniques

Description

The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Python’s most important tools and libraries for doing Data Science; they are known in the community as “Python’s Data Science Stack”. This is a practical course where the viewer will learn through real-world examples how to use the most popular tools for doing Data Science and Analytics with Python. All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Become-a-python-data-analyst

Who is this book for?

Data analysts or data scientists interested in learning Python’s tools for doing Data Science. Business Analysts and Business Intelligence experts who would like to learn how to use Python for doing their data own analysis tasks will also find this tutorial very helpful. Software engineers and developers interested in Python’s capabilities for analyzing data gain a lot from this course. A basic (beginner’s level) familiarity with Python language is assumed.

What you will learn

Learn about the most important libraries for doing Data Science with Python and how they can be easily installed with the Anaconda distribution.
Understand the basics of Numpy which is the foundation of all the other analytical tools in Python.
Produce informative, useful and beautiful visualizations for analyzing data.
Analyze, answer questions and derive conclusions from real world data sets using the Pandas library.
Perform common statistical calculations and use the results to reach conclusions about the data.

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