Business Data Analysis with R
Learn multivariate analysis techniques using R and RStudio to make data-driven business decisions by analyzing complex variables simultaneously.
Learn multivariate analysis techniques using R and RStudio to make data-driven business decisions by analyzing complex variables simultaneously.
This course is designed for decision-makers and aspiring data scientists who want to leverage computational technologies for business insights. You'll learn how to apply multivariate analysis to business data using R and RStudio, powerful open-source tools for statistical analysis and data visualization. Multivariate analysis allows you to simultaneously consider multiple variables affecting a business entity, whether numerical or categorical, enabling more comprehensive decision-making. The course emphasizes how this analytical approach can process any type of business information to make better decisions by considering all relevant factors. Just as the human brain continuously processes multiple variables when driving—assessing speed, weather conditions, road conditions, and potential distractions—multivariate analysis enables you to consider, discriminate, group, relate, and cluster data variables. Throughout the four-week program, you'll master key multivariate analysis techniques including principal component analysis, cluster analysis, and discriminant analysis, applying them to solve real business problems and cases. No prior programming experience is required—only basic statistical knowledge—as the course provides step-by-step guidance on using these tools and their business applications.
Instructors:
Víctor Cruz Morales
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What you'll learn
Use R and RStudio as tools for programming and statistical analysis Apply multivariate data treatment models with R Deduce behavior patterns from surveys and product properties Generate results dashboards for multivariate data Implement cluster analysis for business segmentation Perform principal component analysis to simplify complex datasets Apply discriminant analysis for categorical business data Create data visualizations for multivariate business analysis
Skills you'll gain
This course includes:
PreRecorded video
Graded assessments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This comprehensive course teaches multivariate data analysis techniques using R and RStudio specifically focused on business applications. The curriculum is structured across five modules that progressively build analytical skills for complex business data. Beginning with an introduction to multivariate data characteristics, you'll learn about covariances, correlations, and multivariate normal distributions with practical business applications. The second module focuses on visual analysis techniques, teaching you how to read external data, prepare data frames, and use specialized graphing libraries to identify patterns and predict trends. The third module covers cluster analysis, including hierarchical and non-hierarchical methods with k-means techniques for customer or product segmentation. In module four, you'll master principal component analysis to reduce data dimensionality while preserving key information, learning how to calculate components and determine their explanatory power. The final module explores discriminant analysis for categorical data classification in business contexts. Throughout the course, you'll apply these techniques to real business scenarios, learning to create results dashboards that transform complex multivariate data into actionable business insights.
Módulo 1: Introducción al análisis multivariado
Module 1
Módulo 2: Análisis visual de Datos Multivariados
Module 2
Módulo 3. Análisis de clusters
Module 3
Módulo 4. Análisis de componentes principales
Module 4
Módulo 5. Análisis discriminante
Module 5
Fee Structure
Payment options
Financial Aid
Instructor
Víctor Cruz Morales
2 Courses
PhD in Industrial Processes and Energy at Universidad Anáhuac Mayab
Full Professor at Anáhuac Mayab University in the Department of Industrial Engineering, specializing in Simulation, Multivariate Analysis, Mathematical Models, and Numerical Methods, all using R and Python.
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