Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python

by Alvaro Fuentes

Overview

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.

Product Details

ISBN-13:	9781789534405
Publisher:	Packt Publishing
Publication date:	08/31/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	178
File size:	9 MB

About the Author

Alvaro Fuentes is a data scientist with an M.S. in quantitative economics and applied mathematics with more than 10 years of experience in analytical roles. He worked in the central bank of Guatemala as an economic analyst, building models for economic and financial data. He founded Quant to provide consulting and training services in data science topics and has been a consultant for many projects in fields such as business, education, psychology, and mass media. He has taught courses to students in topics such as data science, mathematics, statistics, R programming, and Python. He also has technical skills in R programming, Spark, PostgreSQL, Microsoft Excel, machine learning, statistical analysis, econometrics, and mathematical modeling.
Alvaro Fuentes is a data scientist with more than 12 years of experience in analytical roles. He holds an M.S. in applied mathematics and an M.S. in quantitative economics. He worked for many years in the Central Bank of Guatemala as an economic analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in data science topics and has been a consultant for many projects in fields such as business, education, medicine, and mass media, among others. He is a big Python fan and has been using it routinely for five years to analyze data, build models, produce reports, make predictions, and build interactive applications that transform data into intelligence.

Table of Contents

Table of Contents

1. The Anaconda Distribution and Jupyter Notebook
2. Vectorizing Operations with Numpy
3. Pandas: Everyone’s Favorite Data Analysis Library
4. Visualization and Exploratory Data Analysis
5. Statistical Computing with Python
6. Introduction to Predictive Analytics Models