Apply R for Business Analytics Projects

EDUCBA via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

4 weeks, 10 hours a week

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Overview

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This Specialization provides a comprehensive, applied approach to business analytics using R across fraud detection, marketing performance analysis, and HR attrition modeling. Learners develop the ability to interpret complex datasets, apply statistical and machine learning techniques, and generate actionable business insights. Through structured, project-based learning, participants explore fraud lifecycle analytics, customer behavior modeling, churn prediction, workforce attrition analysis, and data-driven strategy evaluation. The program emphasizes analytical reasoning, feature selection, predictive modeling, and performance validation, equipping learners with practical skills required for data-driven roles in finance, marketing, HR, and business analytics.

Syllabus

Course 1: Analyze Fraud Using Data Analytics and R
Course 2: Analyze Marketing Performance Using R and Excel
Course 3: Analyze HR Attrition Using R Analytics

Courses

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5 hours 52 minutes
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Learners will analyze fraud patterns, evaluate fraud detection techniques, and apply data-driven analytical approaches to identify and mitigate fraudulent activities. This course builds a strong foundation in fraud concepts while progressively introducing modern fraud analytics methods, including Big Data approaches and machine learning techniques such as supervised and unsupervised learning. Learners will gain a structured understanding of the fraud lifecycle, high-level fraud analytics strategies, and the measurable business benefits of analytics-driven fraud prevention. By completing this course, learners will be able to interpret real-world fraud scenarios, assess risk using analytical reasoning, and support informed decision-making in fraud detection environments. The course emphasizes practical insight through detailed credit card fraud examples, enabling learners to connect theory with real operational challenges. What makes this course unique is its end-to-end perspective on fraud analytics—from foundational concepts to strategic implementation—combined with a project-oriented approach using R for analytical thinking. Rather than focusing solely on tools, the course develops analytical judgment, pattern recognition skills, and strategic awareness essential for roles in fraud risk, data analytics, and financial crime prevention.
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5 hours 56 minutes
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By the end of this course, learners will be able to analyze HR attrition data, evaluate key workforce factors, apply statistical techniques, select significant features, and build a predictive attrition model using R. This course provides a practical, end-to-end approach to HR analytics with a strong focus on employee attrition. Learners begin by preparing and validating real-world HR data, followed by in-depth exploratory data analysis to understand workforce demographics, job-related factors, and attrition patterns. The course then progresses to statistical analysis using correlation and Chi-Square tests, helping learners identify meaningful relationships between employee attributes and attrition outcomes. What makes this course unique is its structured, project-driven methodology that mirrors real HR analytics workflows. Learners apply Information Value (IV) techniques for feature selection, create a final modeling dataset, and build an attrition prediction model in R, concluding with performance evaluation on unseen data. By completing this course, learners gain hands-on experience in HR data analysis, develop job-ready analytical thinking, and build confidence in using R for data-driven HR decision-making, making it ideal for aspiring data analysts, HR professionals, and analytics learners seeking practical industry skills.
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7 hours 25 minutes
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By the end of this course, learners will be able to analyze marketing performance using data, apply key marketing metrics, evaluate customer behavior, and build data-driven strategies using R and Microsoft Excel. Learners will gain practical skills in interpreting marketing data, measuring campaign effectiveness, assessing customer satisfaction, and predicting customer churn across real-world business scenarios. This course is designed to help learners bridge the gap between marketing theory and analytics practice. Through structured modules and hands-on case studies, learners explore marketing planning metrics, sales channel management, Net Promoter Score (NPS), customer service evaluation, persuasion parameters, and advanced analytical techniques such as conjoint analysis, market segmentation, churn modeling, and algorithm-based optimization. What makes this course unique is its strong focus on applied analytics using widely adopted tools like R and Excel, combined with industry-specific case studies from banking, airlines, telecommunications, and product-service markets. Instead of focusing only on theory, the course emphasizes practical interpretation of data and actionable insights that support real marketing decisions. Upon completion, learners will be equipped with in-demand marketing analytics skills applicable to roles in marketing, business analysis, and customer insights.