Alvaro Fuentes - Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python
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- Book:Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python
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Enhance your data analysis and predictive modeling skills using popular Python tools
Key Features- Cover all fundamental libraries for operation and manipulation of Python for data analysis
- Implement real-world datasets to perform predictive analytics with Python
- Access modern data analysis techniques and detailed code with scikit-learn and SciPy
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 Pythons 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.
What you will learn- Explore important Python libraries and learn to install Anaconda distribution
- Understand the basics of NumPy
- Produce informative and useful visualizations for analyzing data
- Perform common statistical calculations
- Build predictive models and understand the principles of predictive analytics
Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book
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Copyright 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
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First published: August 2018
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ISBN 978-1-78953-170-1
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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.
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
Python is one of the most common and popular languages used 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 that you need 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 the NumPy and pandas libraries. In the concluding chapters, you will gain experience in building simple predictive models, 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 of performing data analysis with Python.
Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python's tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book.
, The Anaconda Distribution andJupyter Notebook, covers t he most important libraries for data science with Python. This is a well-charted overview of the main objects, attributes, methods, and functions that we will use for doing predictive analytics with Python.
, Vectorizing Operations with NumPy, explores Numpythis is the library upon which almost all other scientific computing in Python projects are based. Learning how to handle NumPy arrays is crucial for doing anything related to data science in Python.
, Pandas - Everyone's Favorite Data Analysis Library, gives an overview of p andas which is a library that provides high performance, easy-to-use data structures, and data analysis tools for the Python programming language. We data scientists love it, and it is one of the key reasons behind Pythons popularity in the data science community. In this section, we show by example how to perform descriptive analysis with pandas.
, Visualization and Explanatory Data Analysis, explains that v isualization is a key topic for data science. Python provides a lot of options for doing visualizations for different purposes. In this volume, we learn about two of the most popular libraries, matplotlib and seaborn, and perform exploratory data analysis on real-world datasets.
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