Analyze Fraud Using Data Analytics and R
About this course
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.
68/100
CourseAsk score
- What the provider tells you
- 32/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- analyze fraud patterns
- evaluate fraud detection techniques
- apply data-driven analytical approaches
- interpret real-world fraud scenarios
- assess risk using analytical reasoning
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Udemy
edX
Coursera
Compared on these lists
Where this course ranks against the alternatives.