Kats Philipp - Learn Python by building data science applications: a fun, project-based guide to learning Python 3 while building real-world apps
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- Book:Learn Python by building data science applications: a fun, project-based guide to learning Python 3 while building real-world apps
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Understand the constructs of the Python programming language and use them to build data science projects
Key Features- Learn the basics of developing applications with Python and deploy your first data application
- Take your first steps in Python programming by understanding and using data structures, variables, and loops
- Delve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in Python
Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The secret sauce of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production.
This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. Youll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. Youll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, youll be able to perform data analysis, train models, and interpret and communicate the results. Finally, youll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice.
By the end of the book, youll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
What you will learn- Code in Python using Jupyter and VS Code
- Explore the basics of coding - loops, variables, functions, and classes
- Deploy continuous integration with Git, Bash, and DVC
- Get to grips with Pandas, NumPy, and scikit-learn
- Perform data visualization with Matplotlib, Altair, and Datashader
- Create a package out of your code using poetry and test it with PyTest
- Make your machine learning model accessible to anyone with the web API
If you want to learn Python or data science in a fun and engaging way, this book is for you. Youll also find this book useful if youre a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.
Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/PacktPublishing/Python-Programming-Projects-Learn-Python-3.7-by-building-applications. If you require support please email: customercare@packt.com
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Copyright 2019 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.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this ...
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Philipp Kats is a researcher at the Urban Complexity Lab, NYU CUSP, a research fellow at Kazan Federal University, and a data scientist at StreetEasy, with many years of experience in software development. His interests include data analysis, urban studies, data journalism, and visualization. Having a bachelor's degree in architectural design and a having followed the rocky path (at first) of being a self-taught developer, Philipp knows the pain points of learning programming and is eager to share his experience.
David Katz is a researcher and holds a Ph.D. in mathematics. As a mathematician at heart, he sees code as a tool ...
Sri Manikanta is an undergraduate student pursuing his bachelor's degree in computer science and engineering at SICET under JNTUH. He is a founder of the Open Stack Developer Community at his college. He started his journey as a competitive programmer and he always loves to solve problems that are related to the filed of data science. He has worked on a couple of projects on deep learning and machine learning. He has published many articles regarding data science, machine learning, programming and cyber security in top publications such as Hacker Noon, freeCodeCamp, Towards Data Science, and DDI. He completed his Python specialization at the University of Michigan, through Coursera.
A special thanks to my friends and well-wishers for supporting me, and to Packt Publishing for giving me the opportunity to review this book.
Richard Marsden has 25 years of professional software development experience. After starting in the field of geophysical surveying for the oil industry, he has spent the last 15 years running the Winwaed Software Technology LLC, an independent software vendor. Winwaed specializes in geospatial tools and applications including web applications and operates the Mapping-Tools website for tools and add-ins for geospatial applications such as Caliper Maptitude, Microsoft MapPoint, Android, and Ultra Mileage.
Richard has been a technical reviewer for a number of Packt publications, including Python Geospatial Development and Python Geospatial Analysis Essentials, both by Erik Westra; and Python Geospatial Analysis Cookbook, by Michael Diener.
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.
This book consists of three main sections. The first one is focused on language fundamentals, the second introduces data analysis in Python, and the final section covers different ways to deliver the results of your work. The last chapter of each section is focused on non-Python tools and topics related to the section subject.
, Getting Started with Python, introduces the Python programming language and explains how to install Python and all of the packages and tools we'll be using.
, Preparing the Workspace, covers all the tools we'll need throughout the bookwhat they are, how to install them, and how to use their interfaces. This includes the installation process for Python 3.7, all of the packages we'll require throughout the book, how to install all of them at once in a separate environment, as well as two code development tools we'll usethe Jupyter Notebook and VS Code. Finally, we'll run our first script to ensure everything works fine! By the end of this chapter, you will have everything you need to execute the book's code, ready to go.
, First Steps in Coding Variables and Data Types, gives an introduction to fundamental programming concepts, such as variables and data types. You'll start writing code in Jupyter, and will even solve a simple problem using the knowledge you've just acquired.
, Functions, introduces yet another concept fundamental to programmingfunctions. This chapter covers the most important built-in functions and teaches you about writing new ones. Finally, you will revisit the problem from the previous chapter, and write an alternative solution, using functions.
, Data Structures, covers different types of data structures in Pythonlists, sets, dictionaries, and many others. You will learn about the properties of each structure, their interfaces, how to operate them, and when to use them.
, Loops and Other Compound Statements
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