Apply R for Predictive Analytics and Machine Learning

By the end of this course, learners will be able to analyze data using R, apply statistical methods, build predictive models, and interpret analytical results for real-world decision-making. Learners will gain hands-on experience with R programming fundamentals, data manipulation, visualization techniques, and advanced analytics such as regression, decision trees, and time series analysis. This course is designed to guide learners from the basics of R—its origin, architecture, syntax, and data structures—to practical data analysis and business applications. Through structured modules, learners will work with vectors, data frames, loops, functions, and charts, and then progress to statistical analytics, distribution functions, and predictive modeling techniques. Real-world scenarios, including insurance industry case studies, help learners understand how analytics is applied in professional environments. What makes this course unique is its balanced focus on both programming and analytics, making it suitable for beginners as well as professionals transitioning into data analytics roles. With clearly aligned learning objectives, graded assessments, and practice quizzes, learners will build job-ready skills in R that can be applied across industries such as finance, insurance, and data science. Completing this course equips learners with a strong analytical mindset and practical R skills to confidently explore data, generate insights, and support data-driven decisions.

By the end of this course, learners will be able to analyze transactional datasets, calculate and adjust support thresholds, generate and interpret association rules, clean real-world grocery data, and apply advanced algorithms such as Eclat to uncover meaningful purchasing patterns using R. This hands-on project-based course guides learners step by step through the complete Market Basket Analysis workflow. Starting with loading and understanding transactional data, learners progress to calculating minimum support, training association rule models, visualizing rules, and optimizing results through parameter tuning. The course then shifts to practical data preparation using a real grocery dataset, emphasizing duplicate removal, co-purchase analysis, and efficient frequent itemset mining. What makes this course unique is its strong focus on applied learning using authentic datasets and industry-relevant techniques. Rather than emphasizing theory alone, learners gain practical experience implementing Market Basket Analysis end to end in R, mirroring real analytical tasks performed in retail analytics, recommendation systems, and customer behavior analysis. By completing this course, learners build job-ready skills in association rule mining, data preprocessing, and exploratory analysis—capabilities directly applicable to data analytics, data science, and business intelligence roles.

By the end of this course, learners will be able to analyze customer data, evaluate predictive features, build and optimize classification models, and assess model performance to accurately predict card purchase behavior using R. Learners will develop practical skills in logistic regression and decision tree modeling while applying industry-relevant evaluation techniques. This hands-on, project-based course guides learners through a complete predictive modeling workflow using a real-world card purchase use case. Starting with data import and feature assessment using Information Value, learners progress through visualization, data preparation, and model development. The course emphasizes model evaluation through lift charts, ROC analysis, and testing on unseen data, ensuring learners understand not just how to build models, but how to validate and trust them. Learners also gain experience saving and reusing trained models, a critical skill for real-world deployment. What makes this course unique is its strong focus on practical decision-making, model interpretability, and end-to-end implementation in R. By completing this course, learners strengthen their analytical thinking and gain job-ready skills applicable to roles such as data analyst, marketing analyst, and risk analyst.