Introduction to Advanced Analytics

The lecture will provide a profound, yet practice-oriented overview on current approaches to realize value from vast amount of data in the enterprise context. This will include approaches on how to adapt an organizational system to realize value from analytics. The content will be illustrated based on complex case studies as well as demonstrations of selective approaches of data science and data engineering.

The lecture will cover mainly the following aspects of Advanced Analytics.

1. Introduction

  • Definition of Key Terms/Concepts
  • Advanced Analytics and Business Intelligence
  • Advanced Analytics in Practice

2. Theoretical Perspectives on Advanced Analytics

  • Socio-Technical Systems Theory & Affordance Theory
  • Dynamic Capabilities and Resource-based View

3. Organizational Implications of Advanced Analytics

  • Barriers to Advanced Analytics
  • Data Strategy and Culture
  • Organizational Implementation of Advanced Analytics (Structural and Procedural)
  • Management of Advanced Analytics

4. Data Science

  • Statistical Data Analysis
  • Artificial Intelligence and Machine Learning

5. Data Engineering, Architecture, and Infrastructure

  • Data Science Environment
  • Data Architecture and Infrastructure
  • Data Pipelines and Production of Machine Learning Models

6. Summary & Outlook

The language of instruction in this course is German. However, all course materials (lecture slides and
tutorial notes) are available in English.

Teaching objectives of this lecture is the acquisition of the following knowledge and capabilities:

  • Overview on prerequisites to realize value from vast amount of data in the enterprise context in form of an analytics competence
  • Capability to systematically use approaches to data engineering and data analysis
  • Knowledge on technologies and tools in the area of advanced analytics

Empfohlene Vorkenntnisse:
Basic knowledge in the area of business intelligence and data warehousing. For instance, through lectures, such as “Innerbetriebliche Systeme (IIS-IBS-M)”

Die Veranstaltung hat einen Umfang von 180 Stunden Arbeitsaufwand und wird mit 6 ECTS-Punkten bewertet, die im Rahmen einer schriftlichen, 90 minütigen Klausur erworben werden können.

Vorlesung:

The lecture will provide knowledge on how to realize value from advanced analytics in the enterprise context. In detail, the lecture will focus on:

  • Overview on prerequisites to realize value from vast amount of data in the enterprise context in form of an analytics competence
  • Capability to systematically use approaches to data engineering and data analysis
  • Knowledge on technologies and tools in the area of advanced analytics

Dozenten: Dr. Christian Dremel

WS, jährlich

SWS: 2

Literatur:
Will be announced within the first lecture.

Übung:

The exercise systematically deepens the knowledge conveyed in the lecture by means of exercises, which are worked on by the students in small groups and then discussed in plenary sessions. The focus of the exercise is on the following tasks:

  • Transfer tasks for the application of the conveyed knowledge
  • Complex case studies for in-depth study of the lecture content

Dozenten: Dr. Christian Dremel

WS, jährlich

SWS: 2