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

Business Data Science Basics: e-learning.tuhh.de/studip
Business Decisions with Machine Learning: e-learning.tuhh.de/studip
Building Business Data Products: e-learning.tuhh.de/studip

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

Course	Course notes & material
1. Business Data Science Basics	click here
2. Business Decisions with Machine Learning	click here
3. Building Business Data Products	click here

Preliminary Schedule