Overview
- A practitioner’s guide for data science that can also be used effectively as a textbook for students. For example, the application of statistics and statistical methods are linked to an immediate real-world data science problem, not presented as a theoretical isolated field of investigation
- Presumes some familiarity with the basics of programming and purposefully guides the reader through a series of topics essential to data science, providing both the managerial and technical foundations of the discipline
- Chapters can be read independently of the others, a benefit for both the data science novice and professionals focused on learning a specific topic
- Most figures are created by code examples that may be downloaded and executed from https://github.com/robertball/Beginners-Guide-Data-Science
- For figures in this book of special interest to the reader, the code that generated the figure may be customized to align with the reader’s current needs
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Table of contents (11 chapters)
Keywords
- data science
- data mining
- data visualization
- data analytics
- practical
- pragmatic
- machine learning
- artificial intelligence
- statistics
- visualizations
- data exploration
- data management
- time series
- python
- code examples
- business insights
- business intelligence
- beginning to end
- computer science
- natural language processing
About this book
This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered.
Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification.
This book is organized as 11 chapters, structuredas independent treatments of the following crucial data science topics:
- Data gathering and acquisition techniques including data creation
- Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis
- Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements
- Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded
- Metrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observations
- Recommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some manner
- Machine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analytics
- Natural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate manner
- Time series analysis, techniques to examine and generate forecasts about the progress and evolution of data over time
Data science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book.
Authors and Affiliations
About the authors
Brian Rague joined the School of Computing faculty at Weber State University in 2003 after working on various data science and engineering research projects throughout his early career at MIT, Caltech, and NASA’s Jet Propulsion Laboratory. He has consulted with industry partners on how to effectively leverage the ongoing deluge of available data for both operations and research purposes. His areas ofinterest emphasize the platforms and technologies that wrangle and process data, such as machine learning, parallel computing, and distributed systems.
Bibliographic Information
Book Title: The Beginner's Guide to Data Science
Authors: Robert Ball, Brian Rague
DOI: https://doi.org/10.1007/978-3-031-07865-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-07864-4Published: 16 November 2022
Softcover ISBN: 978-3-031-07867-5Published: 16 November 2023
eBook ISBN: 978-3-031-07865-1Published: 15 November 2022
Edition Number: 1
Number of Pages: XI, 248
Number of Illustrations: 15 b/w illustrations, 88 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Machine Learning, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences