Content

Data Science and Business Intelligence & Analytics (BI&A) systems go hand in hand and are a core enabler of decision-making in today’s organizations.  Where Data science rather represents a broad and generic concept of studying data to extract meaningful insights, BI&A systems provide actionable support along the entire analytical lifecycle. The aim of this course is to introduce key concepts, design principles, and building blocks of contemporary BI&A systems as well as foundational methods and central roles in organizations. A specific emphasis is set on introducing key concepts and activities from the perspectives of a data engineer, a data analyst, and a business analyst.

The course is complemented with an engineering capstone project, where students work in a team with real-world use cases and data in order to create a prototypical Business intelligence & Analytics system using state-of-the-art technologies (e.g., scikit-learn in Python or Microsoft Power BI)

Learning objectives

• Explain what business intelligence & analytics (BI&A) systems are and how they can be conceptualized.
• Describe important design principles and building blocks of contemporary BI&A systems
• Understand important methods and key roles along the entire BI&A systems lifecycle.
• Understand central BI&A concepts and key activities from the perspective of the data engineer, the data analyst, and the business analyst.
• Collect hands-on experience in a business intelligence & analytics project and deliver a prototypical solution for a real-world use case in a team effort.

Prerequisites

This course is limited to a capacity of 50 places. The capacity limitation is due to the attractive format of the accompanying engineering capstone project. Strong analytical abilities and profound skills in SQL as wells as Python are required.  Students have to apply with their CV and transcript of records. All organizational details and the underlying registration process of the lecture and the capstone project will be presented in the first lecture. The teaching language is English.

Die Erfolgskontrolle erfolgt in Form einer Prüfungsleistung anderer Art (Form) nach § 4 Abs. 2 Nr. 3 SPO. Die Leistungskontrolle erfolgt in Form einer einstündigen Klausur und durch Durchführung eines Capstone Projektes. Details zur Ausgestaltung der Erfolgskontrolle werden im Rahmen der Vorlesung bekannt gegeben.

Contact:

Jonas Gunklach (jonas.gunklach@kit.edu)