363-1098-00L  Business Analytics

SemesterFrühjahrssemester 2020
DozierendeS. Feuerriegel
Periodizitätjährlich wiederkehrende Veranstaltung
KommentarStudents from the MAS MTEC are not applicable for this course and are kindly asked to enroll in the course "Executive Business Analytics (365-1120-00L)" instead.

KurzbeschreibungPrior to the start of the Information Age in the late 20th century, companies were forced to collect data from non-automated sources manually. Companies back then lacked the computing capabilities necessary for data to be analyzed, and as a result, decisions primarily originated not from knowledge but from intuition.
LernzielOverall learning goal

By the end of the course, students will be able to plan, implement and evaluate analytics in applied settings in order to generate value from data for society, corporations and individuals. This serves the pressing need of firms to improve their efficiency – such as customer satisfaction, competitive advantage –by leveraging the growing amounts of structured and unstructured data.

Detailed breakdown by objective

To achieve this overall goal, students should after participation being able to:

Objective 1 (Managerial aspects): Understand the processes and challenges of analytics-related projects
• Identify applications for analytics in corporations and organizations that create value
• List implications for management when undertaking a project involving business analytics
• Apply the data mining process CRISP-DM to their actual setting

Objective 2 (Methodological challenges): Understand common methods for performing business analytics
• Translate use cases of business analytics into a mathematical model formulation
• Name common methods for business analytics, as well as their underlying concepts
• Compare the properties of these models

Objective 3 (Practical implementation): Performing actual evaluations of business analytics based on real-word datasets
• Preprocess data in order to transform it into relational structures
• Apply statistical software (e.g. “R” or Python) to perform business analytics in practice
• Evaluate the results in order to choose the best-performing method
InhaltWith the emergence of ubiquitous computing technology, company decisions nowadays rely strongly on computer-aided “Business Analytics”.

Business analytics refers to technologies that target how business information (or sometimes information in general) is collected, analyzed and presented. Combining these features results in software serving the purpose of providing better decision support for individuals, businesses and organizations.

This course will teach what distinguishes the varying capabilities across business analytics – namely the underlying methods. Participants will learn different strategies for data collection, data analysis, and data visualization. Sample approaches include dimension reduction of big data, data visualization, model selection, clustering and forecasting.
In particular, the course will teach the following themes:
• Forecasting: How can historical values be used to make predictions of future developments ahead of time? How can firms utilize unstructured data to facilitate the predictive performance? What are metrics to evaluate the performance of predictions?
• Data analysis: How can one derive explanatory power in order to study the response to an input?
• Clustering: How can businesses group consumers into distinct categories according to their purchase behavior?
• Dimension reduction: How can businesses simplify a large amount of indicators into a smaller subset with similar characteristics?
During the exercise, individual assignments will consist of a specific problem from business analytics. Each participant will be provided with a dataset to which a certain method should be applied to using the statistics software R.

Note: the course is a block course teaching the theoretical elements. This provides then the basis for a project work where individual students or groups implement analytics to a business-relevant datasets. This project underlies eventually the grading.
1. Motivation and terminology
2. Business and data understanding
a. Data management and strategy
b. Data mining processes
3. Data preparation for big data
a. Software and tools
b. Knowledge representation and storage
c. Information preprocessing
4. Explanatory modeling
5. Predictive modeling
a. Classification
b. Variable selection
c. Handling non-linearities
d. Ensemble learning
e. Unsupervised learning
f. Working with unstructured data
6. Managerial implications
LiteraturJames, Witten, Hastie & Tibshirani (2013): An Introduction to Statistical Learning: With Applications in R. Springer.
Sharda, Delen & Turban (2014): Business Intelligence: A Managerial Perspective on Analytics. Pearson.
Voraussetzungen / BesonderesPlease note that we expect simple scripting skills (e.g. in Python), as students will apply their theoretical knowledge by implementing a machine learning application with given open-source packages.