Programming for Analytics (Paperback)

Kimberly Seefeld

ISBN 10: 1969233419 / ISBN 13: 9781969233418

Published by Datajoyai, 2026

About this Item

Paperback. Programming for Analytics introduces the fundamental programming concepts needed for modern data analytics, business intelligence, artificial intelligence, and data science. Designed for beginners with little or no coding experience, this textbook emphasizes analytical thinking, problem-solving, automation, and reproducible workflows rather than computer science theory.Students learn how programming supports data analysis, automation, reporting, and decision-making. The book introduces core concepts including variables, data types, functions, libraries, data structures, automation, debugging, and reproducibility. Readers explore both R and Python, gaining an understanding of how programming languages support analytics workflows and how skills transfer across platforms.Special attention is given to practical topics that analysts encounter in the workplace, including data manipulation, code organization, reusable functions, automation of repetitive tasks, documentation, debugging strategies, and evaluating code quality. The text also explores the growing role of artificial intelligence as a programming assistant and discusses responsible, ethical, and professional uses of AI-generated code.Hands-on labs, demonstrations, and practice exercises reinforce concepts through realistic analytics scenarios. By focusing on concepts rather than language-specific syntax, this book helps students develop a durable foundation that prepares them for future study in analytics, data science, machine learning, and AI.Ideal for introductory courses in programming for analytics, data analytics, business analytics, artificial intelligence, and data science or independent study. Resources for learning are available from the publisher. A beginner-friendly introduction to programming for analytics that teaches students how to use R, Python, automation, and AI tools to support data analysis. Includes hands-on labs, practical examples, and reproducible analytics workflows. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781969233418

Bibliographic Details

• Title: Programming for Analytics (Paperback)
• Publisher: Datajoyai
• Publication Date: 2026
• Language: English
• ISBN 10: 1969233419
• ISBN 13: 9781969233418
• Binding: Paperback
• Condition: new

About this title

Synopsis

Programming for Analytics introduces the fundamental programming concepts needed for modern data analytics, business intelligence, artificial intelligence, and data science. Designed for beginners with little or no coding experience, this textbook emphasizes analytical thinking, problem-solving, automation, and reproducible workflows rather than computer science theory.

Students learn how programming supports data analysis, automation, reporting, and decision-making. The book introduces core concepts including variables, data types, functions, libraries, data structures, automation, debugging, and reproducibility. Readers explore both R and Python, gaining an understanding of how programming languages support analytics workflows and how skills transfer across platforms.

Special attention is given to practical topics that analysts encounter in the workplace, including data manipulation, code organization, reusable functions, automation of repetitive tasks, documentation, debugging strategies, and evaluating code quality. The text also explores the growing role of artificial intelligence as a programming assistant and discusses responsible, ethical, and professional uses of AI-generated code.

Hands-on labs, demonstrations, and practice exercises reinforce concepts through realistic analytics scenarios. By focusing on concepts rather than language-specific syntax, this book helps students develop a durable foundation that prepares them for future study in analytics, data science, machine learning, and AI.

Ideal for introductory courses in programming for analytics, data analytics, business analytics, artificial intelligence, and data science or independent study. Resources for learning are available from the publisher.