Data Analytics: Handbook of Formulas and Techniques

Data analytics depends on more than collecting numbers: sound measurement, useful models, clear visualization, and appropriate mathematical tools all shape what can be learned from the data. Adedeji B. Badiru’s Data Analytics: Handbook of Formulas and Techniques brings these elements together in a formula-focused reference for readers working with quantitative questions.

The eight-chapter progression starts with the essentials and builds toward statistical methods, regression models, and data integration. Its broad scope makes the book a practical point of reference for readers who want to connect mathematical techniques with decisions and applications in engineering, management, and business.

From measurements to models

The early chapters establish a systems view of analytics and examine data, measurement scales, mathematical equations, and empirical model building. The book also introduces predictive analytics and quantitative approaches to pandemic modeling, including COVID-19. This foundation helps place formulas in the wider process of turning observations into information.

See what the data can show

A dedicated chapter on data visualization considers data collection, processing, measurement scales, and the use of information. Later material develops the mathematical side of analysis through probability distributions, descriptive statistics, and other statistical methods—useful territory for readers who need to interpret and present quantitative results.

A broad mathematical reference

The handbook ranges from basic calculations and statistical methods to random-field regression models and data integration using the DEJI Systems Model. That breadth gives readers a way to look up techniques across connected areas rather than treating analytics as a single isolated procedure.

For study, research, and professional practice

Researchers, practitioners, educators, and students in data analytics and related fields—including industrial and production engineering, project management, civil and mechanical engineering, technology management, and business—may find this reference relevant. Its formula-centered approach is especially suited to mathematically inclined readers who want a compact companion to their work with data.

Keep Data Analytics: Handbook of Formulas and Techniques nearby when a quantitative problem calls for a model, a statistical method, or a clearer way to present the evidence.