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Data Science and Analytics (DLMBDSA)

Module Title: Data Science and Analytics

Module No.:

DLMBDSA

Semester / Term:

--

Duration:

Minimum 1 Semester

Module Type(s):

Wahlpflicht

Regularly offered in:

Workload: 300 h

Credit Points: 10

Admission Requirements:

None

Language of Instruction:

Englisch

Contributing Courses to Module:

Workload:

Self-study: 200 h
Self-examination: 50 h
Tutorials: 50 h

Course Coordinator(s) / Tutor(s):

Please see the current list of tutors on the Learning Management System.

Module Director:

Prof. Dr. Markus C. Hemmer

References to Other Programs:

  • Master of Business Administration

References to Other Modules in the Program:

  • Corporate Finance

Qualification and Educational Objectives of the Module:

Data Science:
Upon successful completion, students are able

  • to understand the techniques to store data.
  • to understand how data are pre-processed to prepare them for analysis.
  • to develop typologies for data and ontologies for knowledge representation.
  • to decide for appropriate mathematical algorithms to utilize data analysis for a given task.
  • to understand the value, applicability, and limitations of artificial intelligence for data analysis.

Analytical Software and Frameworks:
Upon successful completion, students are able

  • to understand how cloud computing and distributed computing support the field of data analytics.
  • to understand in-memory database technologies for real-time analytics.
  • to apply advanced statistics and machine learning solutions to solve data analysis problems.
  • to compare the capabilities and limitations of the presented software solutions.
  • to assist in decision-making for the most appropriate solution for a given business case.

Course Content of the Module:

Data Science:

  • Introduction to Data Science
  • Data Storage
  • Pre-processing of Data
  • Processing of Data
  • Selected Mathematical Techniques
  • Selected Artificial Intelligence Techniques

Analytical Software and Frameworks:

  • Introduction
  • Statistical Modelling
  • Machine Learning
  • Cloud Computing Platforms
  • Distributed Computing
  • Database Technologies

Teaching Methods:

See the contributing course outlines

Literature:

See the contributing course outlines

Percentage of the Module Grade Relative to the Final Grade for the Program:

--

Prerequisites to Qualify for Assessment:

Assessment:

See the contributing course outlines

DLMBBD01:
Exam, 90 min. (50 %)
DLMBBD02:
Written Assessment: Written Assignment (50 %)