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Become a Python Data Analyst

Become a Python Data Analyst

By : Alvaro Fuentes
4.3 (510)
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Become a Python Data Analyst

Become a Python Data Analyst

4.3 (510)
By: Alvaro Fuentes

Overview of this book

Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python.
Table of Contents (8 chapters)
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Statistical Computing with Python

In this chapter, we will briefly talk about Scientific Library for Python (SciPy), which is the scientific toolbox for Python. We will get a brief overview of the statistics subpackage and we will use it to perform many statistical calculations, including calculations of probabilities, probability distributions, and confidence intervals. We will also perform hypothesis testing on a real-world dataset, and we will be able to state conclusions that go beyond the sample that we have.

In this chapter, we will cover the following topics:

  • SciPy
  • Statistics
  • Probabilities
  • Hypothesis testing
  • Performing statistical tests
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