Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets

Data Science in Theory and Practice is a comprehensive resource that explores the foundations of data science, including mathematical and statistical models, data transformation techniques, and predictive modeling. It is ideal for students and practitioners without a strong background in data science, and provides examples of code algorithms in R and Python.

Format: Hardback
Length: 400 pages
Publication date: 05 November 2021
Publisher: John Wiley and Sons Ltd

Data Science in Theory and Practice is a comprehensive resource that delves into the foundations of data science, offering a thorough exploration of the mathematical and statistical models employed to analyze data sets across diverse disciplines. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors then analyze various data transformation techniques, such as extracting information from raw data, examining long memory behavior, and implementing predictive modeling.

The book provides readers with a wealth of topics relevant to the analysis of complex data sets. In addition to a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also includes examples of code algorithms in R and Python, along with pseudo-algorithms to facilitate porting the code to other languages.

Ideal for students and practitioners without a strong background in data science, the book offers valuable insights into foundational theoretical subjects, such as the history of data science, matrix algebra, random vectors, and multivariate analysis. It also provides a comprehensive examination of time series forecasting, encompassing the different components of time series and transformations to achieve stationarity. Introductions to both the R and Python programming languages are provided, covering basic data types and sample manipulations for both languages. Algorithms are explored, including the process of writing algorithms and conducting asymptotic analysis.

A comprehensive discussion of several techniques is also included, covering topics such as machine learning, deep learning, and natural language processing. Data Science in Theory and Practice serves as a valuable resource for anyone seeking to gain a deeper understanding of data science and its applications in various fields.

Weight: 686g
Dimension: 233 x 161 x 27 (mm)
ISBN-13: 9781119674689