Business Analytics with Data Science and Machine Learning

3 complementary elective courses in “Business & Management”

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Credits

3 complementary courses with 2 ECTS each:

  • Business Data Science Basics
  • Business Decisions with Machine Learning
  • Building Business Data Products

They can be taken independently, but it is highly recommended to enroll consecutively.


Target Audience

  • Master students with “Business & Management” courses in their curriculum
  • Students with a strong interest and motivation in acquiring the skills required for mastering the computational aspects of modern business analytics.

Instructor(s)


Overview

Business Analytics is an applied and interactive course program comprised of three different courses and designed to provide you with a sound understanding of the constantly growing opportunities that business analytics experiences through modern approaches in data science and machine learning. In this course you will learn methods of descriptive, predictive and prescriptive analytics in order to approach critical business decisions based on data and to derive recommendations for action. Participants learn how to collect, cleanse and transform large amounts of data using various techniques. The aim is to specifically examine, visualize and model the associated data using modern machine learning methods.

During the course program, the participants apply the tools they have learned to practical data science problems from various management areas, creating a comprehensive and multifaceted application portfolio that demonstrates their data analysis and modeling skills. The programming language used is R, whereby the integration of Python into the workflow is also practiced. Programming knowledge is not required, but is of course an advantage. Each session will involve a small amount of lecturing on R concepts, and a large amount of time for students to complete assigned coding and analysis problems.

Objectives

After completing this module, students will be able to:

  • Obtain large amounts of data via APIs or web scraping from the Internet
  • Clean and transform data
  • Explore and visualize data in a goal-oriented way
  • Model data using modern machine learning techniques
  • Communicate data and results in an actionable form of products, dashboards and applications

Grading

  • Students are evaluated based on their solutions of challenges assigned in each session, which they continuously document in their github lab journals.

Registration


Time & Location


Business Data Science Basics

  • Monday, April 25th, 09.00 - 17.00, Building Q, Room Q - 1.121
  • Tuesday, April 26th, 09.00 - 17.00, Building Q, Room Q - 1.121
  • Wednesday, April 27th, 09.00 - 17.00, Building Q, Room Q - 1.121

Business Decisions with Machine Learning

  • Monday, May 16th, 09.00 - 17.00, Building Q, Room Q - 1.121
  • Tuesday, May 17th, 09.00 - 17.00, Building Q, Room Q - 1.121
  • Wednesday, May 18th, 09.00 - 17.00, Building Q, Room Q - 1.121

Building Business Data Products

  • Monday, June 20th, 09.00 - 17.00, Building Q, Room Q - 1.121
  • Tuesday, June 21st, 09.00 - 17.00, Building Q, Room Q - 1.121
  • Wednesday, June 22nd, 09.00 - 17.00, Building Q, Room Q - 1.121

Course Notes & Materials

CourseCourse notes & material
1. Business Data Science Basicsclick here
2. Business Decisions with Machine Learningclick here
3. Building Business Data Productsclick here

Preliminary Schedule


Business Data Science Basics

SessionDateTopic
1April 25thIntroduction to R, RStudio IDE & GitHub
2April 25thIntroduction to the tidyverse
3April 26thData Acquisition
4April 26thData Wrangling
5April 27thData Visualization

Business Decisions with Machine Learning

SessionDateTopic
6May 16thFundamentals of Machine Learning
7May 16thSupervised ML: Regression (I)
8May 17thSupervised ML: Regression (II)
9May 17thAutomated ML with H20 (I)
10May 18thAutomated ML with H20 (II)
11May 18thML Performance Measures

Building Business Data Products

SessionDateTopic
12June 20Explainable ML with LIME
13June 21ML: Deep Learning
14June 22Reporting with RMarkdown, Shiny, Flexdashboard
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Joschka Schwarz
Research Assistant & Doctoral Student

My research interests include social network dynamics and computational social science with a focus on the computational analysis of language.

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Oliver Mork
Research Assistant & Doctoral Student

My research interests lie at the intersection of Econometrics & Machine Learning.

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Dr. Hannes W. Lampe
Research Associate & Post-Doc

My research interests lie at the intersection of Technologie, Innovation and Entrepreneurhip (TIE) and Public and Nonprofit Management (PNP).

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Prof. Dr. Christoph Ihl
Professor & Head of Institute

My research interests include cultural entrepreneurship, social networks and natural language processing.