Predictive Analytics in Finance - (F) - Q4

Participation Prerequisites

For students unfamiliar with R, we recommend to complete the free online course "Introduction to R" on datacamp.com (should only take about 4 hours or so); any other way to brush up basic programming skills in R is also fine. As part of the course, students will have the option to take any courses on DataCamp for free - this is fully optional and DataCamp courses will not be part of the assessments.

Course Content

This course is dedicated to conveying a sense of how to structure analytic projects systematically in the context of predictive models. The course introduces such a structure with an applied, step-by-step introduction to predictive analytics that mixes theory and practical, hands-on implementation tasks (using programming in R). Fundamental types of predictive data science models are introduced, including decision trees, logistic regression, support vector machines, neural networks, and naïve Bayes. In addition to those supervised models, we also look into unsupervised models for clustering.  The aim is to enable students to work more efficiently alongside data scientists, and to empower students to conduct predictive modelling themselves using common machine learning approaches.

The course features hands-on exercises and case studies in Finance, such as fraud detection, default/return prediction in a peer-to-peer investment case (based on real data from Lending Club) and customized credit product pricing (based on real data from a car loan provider).

Discover what the course has to offer—watch the introductory video for a comprehensive overview.

Intended Learning Outcomes and Competencies

• Ability to apply the cross-industry standard process for data mining to business problems in finance related to prediction
• Ability to apply a range of machine learning models to a range of supervised and unsupervised learning problems
• Ability to link typical business problems in finance to predictive analytics models

Instruction Type

Form of Examination

Literature

F. Provost and T. Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking . O’Reilly, 2013