Book Image

Become a Python Data Analyst

By : Alvaro Fuentes
Book Image

Become a Python Data Analyst

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)

The Anaconda Distribution and Jupyter Notebook

In this book, you will learn the basic concepts of data analysis using Python. In the first chapter, we will learn how to install the Anaconda distribution, which contains all the software needed for this book. We will also get to know Jupyter Notebook, which is the computing environment where we will do all of our work. Something nice about this book is that we take a hands-on practical approach that will help you to master the tools very effectively.

The following are the main topics that will be covered in this chapter:

  • The Anaconda distribution and the problems it solve
  • How to install it in our computer and get ready to start working
  • Jupyter Notebook, where we will perform our computations and analysis
  • Some useful commands and keyboard shortcuts used in Jupyter Notebook