Data Science and Artificial Intelligence

Business Analytics Tools

Seminar, 3.00 ECTS

 

Course content

The course will be used to practice the acquired skills in practical applications, which means students will get familiar with at least 3 different tools for data analysis and use them for different domains. From the current perspective, these products are the companies Microsoft, ATOS, SAS or IBM etc.

Learning outcomes

The graduate is able to put predictive analytics into practice for a variety of business applications. They master the practical use of all necessary tools and can weigh the advantages and weaknesses of the individual tools.

Recommended or required reading and other learning resources / tools

Books: Vijay Kotu: Predictive Analytics and Data Mining, Max Kuhn: Applied Predictive Modeling, Daniel Covington: Analytics: Data Sicence, Data Analysis and Predictive Analytics for Business, John D. Kelleher: Fundamentals of Machine Learning for Predicitive Data Analyitcs: Algorithms, Worked Examples and Case Studies, Steven Finlay: Predictive Analytics, Data mining and Big Data
Journals:

Mode of delivery

1 THW Lecture, 2 THW Tutorial

Prerequisites and co-requisites

Modules MAT 2 and AI 2

Assessment methods and criteria

Continuous appraisal