Introducing Data Science : Big Data, Machine Learning, and more, using Python tools (Edition 1) (Paperback)

9781633430037. New condition. Trade paperback. Language: English. Pages: 320. Trade paperback (US). Glued binding. 320 p. Contains: Illustrations. DESCRIPTION Data Science has become one of the hottest fields in technology. Firms worldwide are scrambling to find developers with data science skills to work on projects ranging from social media marketing to machine learning, but the prerequisite knowledge and experience for this career can seem bewildering. This book is designed to help anyone who wants to learn more about data science get started. Introducing Data Science teaches readers how to accomplish the fundamental tasks that occupy data scientists. They'll use the Python language and common Python libraries as they experience firsthand the challenges of dealing with data at scale. They'll discover how Python allows them to gain insights from huge data sets that need to be stored on multiple machines, or for data moving at such speed no single machine can handle it. After reading this book, readers will have a solid foundation to consider a career in data science. KEY SELLING POINTS Master big data with Python and become a data scientist How to use data science in a big data world Gain hands on experience with the most common Python data science libraries AUDIENCE This book assumes readers (software engineers, business intelligence reporters, database moderators, statisticians, web developers, anyone interested in Big Data) are comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. ABOUT THE TECHNOLOGY At its core, data science is a set of concepts and techniques for extracting meaning and clarity from enormous stored data sets or fast-moving data streams. Data scientists write programs to interpret these data. The Python programming language is a favorite tool of data scientists because it's easy to read and write, and it provides several high-value libraries that simplify core tasks like statistics, machine learning algorithms, and mathematics.