Data Science Foundations Tools and Techniques: Core Skills for Quantitative Analysis with R and Git

Programming Skills for Data Science is a comprehensive resource for learning foundational programming skills for data science, with step-by-step instructions and real-world examples.

Format: Paperback / softback
Length: 384 pages
Publication date: 16 November 2018
Publisher: Pearson Education (US)

“Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.” –From the foreword by Jared Lander,series editor Using data science techniques,you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started,even if you have no programming or data science experience.   Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems,including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used,analyzed,and visualized so others can see the patterns youve uncovered. Step by step,youll master powerful R programming techniques and troubleshooting skills for probing data in new ways,and at larger scales.   Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everythings focused on real-world application,so you can quickly start analyzing your own data and getting answers you can act upon. Learn to Install your complete data science environment,including R and RStudio Manage projects efficiently,from version tracking to documentation Host,manage,and collaborate on data science projects with GitHub Master R language fundamentals: syntax,programming concepts,and data structures Load,format,explore,and restructure data for successful analysis Interact with databases and web APIs .

Freeman and Ross have created the ultimate guide for aspiring data scientists to acquire essential programming skills. This comprehensive resource is designed to help individuals with no prior programming or data science experience embark on their journey into the world of data science.

The book, Programming Skills for Data Science, is divided into several chapters, each focusing on a specific aspect of programming and data science. The authors, Michael Freeman and Joel Ross, are renowned experts in their respective fields and bring a wealth of knowledge and experience to the table.

In the first chapter, the authors introduce the reader to the basics of programming, covering topics such as variables, data types, loops, and functions. They explain these concepts in a simple and straightforward manner, making it easy for beginners to grasp. The chapter also includes hands-on exercises to reinforce the learning process.

The second chapter delves into the R language, which is widely used in data science. The authors provide a comprehensive introduction to the R programming language, covering its syntax, programming concepts, and data structures. They also explain how to install and configure R, which is essential for working with data science projects.

The third chapter focuses on data wrangling, a crucial step in data science. The authors explain how to clean and organize data, ensuring that it is in a suitable format for analysis. They also introduce techniques such as data visualization and exploratory data analysis, which are essential for uncovering patterns and insights from data.

The fourth chapter explores the use of Git, a version-control system, in data science projects. The authors explain how to set up and use Git, which is essential for collaborating with others on data science projects and maintaining a record of changes made to your code.

The fifth chapter introduces the reader to the world of machine learning, a powerful tool for analyzing data and making predictions. The authors provide a comprehensive introduction to machine learning algorithms, covering topics such as supervised learning, unsupervised learning, and neural networks. They also include hands-on exercises to help the reader apply these algorithms to real-world data.

The sixth chapter focuses on deploying data science projects, which involves sharing your findings with others and integrating them into real-world applications. The authors provide guidance on how to present your data in a clear and concise manner, as well as how to integrate data science projects into existing systems and applications.

Throughout the book, the authors use practical examples and exercises to illustrate the concepts and techniques discussed. These examples are drawn from a wide range of domains, including urban planning, healthcare, and finance, making the book relevant to a wide audience.

In addition to its comprehensive coverage of programming and data science skills, Programming Skills for Data Science also includes valuable resources such as a glossary of programming terms, a list of online resources, and a bibliography for further reading.

Whether you are a recent graduate looking to break into the data science field or an experienced professional seeking to enhance your skills, Programming Skills for Data Science is an essential resource for anyone interested in data science. With its clear and concise writing style, practical examples, and valuable resources, this book is sure to help you achieve your goals and succeed in the world of data science.

Weight: 492g
Dimension: 179 x 232 x 18 (mm)
ISBN-13: 9780135133101