Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy
Key Features
Covers a wide range of data science tasks using SciPy, NumPy, pandas, and matplotlib
Effective recipes on advanced scientific computations, statistics, data wrangling, data visualization, and more
A must-have book if you're looking to solve your data-related problems using SciPy, on-the-go
Book Description With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease.
This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.
What you will learn
Get a solid foundation in scientific computing using Python
Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib
Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy
Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack
Implement data wrangling tasks efficiently using pandas
Visualize your data through various graphs and charts using matplotlib
Who this book is for Python developers, aspiring data scientists, and analysts who want to get started with scientific computing using Python will find this book an indispensable resource. If you want to learn how to manipulate and visualize your data using the SciPy Stack, this book will also help you. A basic understanding of Python programming is all you need to get started.
Author Biography:
L. Felipe Martins has a PhD in applied mathematics from Brown University and is currently an associate professor in the Department of Mathematics at Cleveland State University. His main research areas are applied probability and scientific computing. He has taught applied mathematics courses at all levels, including linear algebra, differential equations, probability, and optimization, and uses Python as an instructional tool in all courses. He is the author of two books, IPython Notebook Essentials and Mastering Python Data Analysis. Ruben Oliva Ramos is a computer systems engineer from Tecnologico de Leon Institute, with a master's degree in computer and electronic systems engineering and a specialization in teleinformatics and networking from the University of Salle Bajio in Leon, Guanajuato, Mexico. He has more than 5 years of experience of developing web applications to control and monitor devices connected with Arduino and Raspberry Pi, using web frameworks and cloud services to build the Internet of Things applications. He is a mechatronics teacher at the University of Salle Bajio and teaches students of the master's degree in design and engineering of mechatronics systems. Ruben also works at Centro de Bachillerato Tecnologico Industrial 225 in Leon, Guanajuato, Mexico, teaching subjects such as electronics, robotics and control, automation, and microcontrollers on the Mechatronics Technician career course; he is a consultant and developer for projects in areas such as monitoring systems and datalogger data using technologies (such as Android, iOS, Windows Phone, HTML5, PHP, CSS, Ajax, JavaScript, Angular, and ASP.NET), databases (such as SQlite, MongoDB, and MySQL), web servers (such as Node.js and IIS), hardware programming (such as Arduino, Raspberry Pi, Ethernet Shield, GPS, GSM/GPRS, and ESP8266), and control and monitor systems for data acquisition and programming. Ruben is the author of the following books by Packt: Internet of Things Programming with JavaScript, Advanced Analytics with R and Tableau, and Raspberry Pi 3 Home Automation Projects. He is also involved in monitoring, controlling, and acquiring of data with Arduino and Visual Basic .NET for Alfaomega. V Kishore Ayyadevara has over 9 years of experience of using analytics to solve business problems and setting up analytical work streams through his work at American Express, Amazon, and more recently a retail analytics consulting start-up. He is an MBA graduate from IIM Calcutta and also an electronics and communications engineer. He worked in the fields of credit risk analytics, supply chain analytics, and consulting for multiple FMCG companies to identify ways to improve their profitability. His interests lie in translating a business problem into a data-related problem by demystifying complexity in data science and identifying ways to further embed analytics in business.
