Programming for Analytics | Student Edition

Kimberly Seefeld

ISBN 10: 1969233419 / ISBN 13: 9781969233418

Published by DataJoyAI, 2026

About this Item

Programming for Analytics | Student Edition | Kimberly Seefeld | Taschenbuch | Englisch | 2026 | DataJoyAI | EAN 9781969233418 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 135851081

Bibliographic Details

• Title: Programming for Analytics | Student Edition
• Publisher: DataJoyAI
• Publication Date: 2026
• Language: English
• ISBN 10: 1969233419
• ISBN 13: 9781969233418
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 18D millimeters 254 millimeters width by 178 millimeters height by 18 millimeters depth
• Weight: 619 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Programming for Analytics: Student Edition 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.