Business Analytics and Data Science

This course is the substitute for the Core Course "Theory and Practice of Machine Learning" that cannot be offered this semester.

Description:
The module is concerned with theories, concepts, and practices to inform and support managerial decision making by means of formal, data oriented methods. Students have the opportunity to develop a variety of skills, including:

• Students are familiar with the three branches of descriptive, predictive and prescriptive analytics and appreciate the relationships between these streams.
• Given some data, students are able to select appropriate techniques to summarize and visualize the data so as to maximize managerial insight.
• Students understand the potential and also the limitations of predictive analytics to aid decision making. They comprehend when and how business applications can benefit from predictive analytics. Given some decision task, they are able to recommend suitable prediction methods.
• Students are familiar with statistical programming languages. Using standard tools, they can develop basic and advanced prediction models and assess their accuracy in a statistically sound manner.

Topics & Content:
Fundamentals of Business Analytics
Making data accessible: Tools for summarization, grouping, and visualization
The business case for predictive modeling
Prediction methods for regression and classification
Advanced data types: time series, text, survival, and network data Fundamentals of intelligent search
Further elaboration of lecturing material
Practical PC exercises

The lecture is accompanied by a tutorial session, in which lecture topics are further elaborated. The aim of the tutorial is to develop and assess empirical models using contemporary data science software. More specifically, the Python programming language is used in tutorial session. Students who are not familiar with Python are given an opportunity to learn Python/programming fundamentals in the first weeks of the tutorial sessions. In order to acquire the skills needed for the course in such short time frame, students must be prepared to invest ample time into self-study exercises.

Time & venue:
Lectures: Thursdays, 10:00-12:00; HU Berlin, Spandauer Str. 1, room 202
Tutorials: Thursdays, 14:00-16:00; HU Berlin, Spandauer Str. 1, room 202 or Tuesdays, 12:00-14:00; HU Berlin, Spandauer Str. 1, room 22

Exam:
Written exam

More information can be found on Moodle.