Aarhus University Seal / Aarhus Universitets segl

Business Analytics - Tools for Analyzing Big Data

The course is divided into the following four main modules under two topic areas of Descriptive Analytics (using Tableau as software) and Predictive Analytics (using R and Rattle as software):

  1. Visualization (Descriptive Analytics)
  2. Unsupervised Learning: Clustering (Descriptive Analytics)
  3. Supervised Learning: Prediction (Predictive Analytics)
  4. Supervised Learning: Classification (Predictive Analytics)

Our course goals are the following:

  1. Students should be able to think critically about data analysis, which includes selecting the right type of analysis for a given task 
  2. Students should be able to identify opportunities of applying data analytics, in real business settings

Students should be well equipped to become data-savvy managers

Exam info and full course description

Exam info and full course description can be found in the course catalogue.

Admission Requirements

Course specific:

To apply for the course you must either be enrolled in a bachelor's degree, have a bachelor's degree or have passed a qualifying entry examination. 

Basic understanding of Excel.

General:

Exchange students: nomination from your home university

Freemovers: documentation for English Language proficiency

You can read more about admission here.

Lecturer

Hamed Mamani

hmamani@uw.edu

Academic profile

 

Hamed Mamani is Professor of Operations Management at Foster School of Business, University of Washington, USA. He is specialized in data analytics and operation management. Among current research topics are healthcare operations, public health policy, supply chain coordination, and incentives mechanisms.