Data Analytics Curriculum, LLC, based in Sheridan, Wyoming, specializes in textbooks, lab workbooks, datasets, and fully developed curriculum materials for learning foundations of modern data analytics. Our resources are designed to support teachers, professors, and independent learners seeking clear, hands-on, and accessible pathways into analytics, data science, and AI.
Our curriculum centers on a set of core textbooks which can be used alone or paired with complementary lab workbooks for hands-on practice. These lab books allow instructors to select the technology level best suited to their students: programming-focused R labs for advanced learners and data majors, and GUI-based Orange labs for introductory or mixed-skill classrooms. Books can be ordered through most bookstores (including Amazon) and can be purchased directly below and are available through the Ingram Spark distribution network for academic purchasing, including college bookstores.
All books include extensive student resources and ready-to-use teaching materials. Please follow the links below to access these materials and enhance your classroom experience.
Core Books
Our four foundational books can be used as text or for self-study. Each provides a structured conceptual basis including learning objectives, exercises, examples, and clear explanations. Suitable for high school, college, and adult learners. The TPT store link above contains all materials needed for teaching.
Introduction to Data Analytics
A beginner-friendly introduction covering data literacy, statistics, data visualization, and an introduction to AI, with foundational exposure to data wrangling and mining.
View on AmazonData Wrangling
Introduces data wrangling, guiding readers through efficient techniques to clean, transform, reshape, and prepare datasets, including SQL. Builds a strong conceptual foundation for key tasks and workflows in modern data analytics.
View on AmazonData Mining
Introduces techniques for exploring and analyzing data, including clustering, classification, association rules, and predictive modeling. Emphasizes clear concepts and practical applications, providing a foundation for lab exercises.
View on AmazonNatural Language Processing
Introduces text processing, tokenization, sentiment analysis, embeddings, and machine learning for language data. NLP is foundational for AI and large language models (like ChatGPT) and is increasingly essential for analytics study.
View on AmazonLab Workbooks
Each lab workbook provides complete step-by-step exercises in R (programming-based) and/or Orange software (GUI, beginner-friendly). Student resources link above includes code and data; the TPT store link contains all materials needed for teaching high school or college courses using these books.