University of Cologne Faculty of Management, Economics and Social Sciences

CISU 2019 Course Syllabus

Business Analytics

Level: Bachelor Lecturer: Boryana Bogdanova, PhD Home institution: Sofia University “St. Kliment Ohridski”

Course dates: 5-15 August (daily 9:00-13:00, no class on Fri 9 Aug) Location: University of Cologne, Germany Workload: 180h Attendance: 30 h Independent studies: 150 h Credit points: 4 ECTS Course language: English Course content

The course provides a comprehensive introduction in the field of Business Analytics. The content is structured into three parts. The first part focuses on the process of data preparation. Students get acquainted with the most common issues when facing raw data and they learn how to resolve them. The second and the third part of the course are engaged with supervised learning techniques. Students are introduced to problems with both qualitative and quantitative response variables. Some of the most popular statistical models are presented and illustrated via textbook examples and real-life case studies. The process of model estimation, validation, interpretation, and implementation is explained step by step within a balanced exhibition of theory and applications in RStudio.

Learning goals

ü Develop skills for data understanding and data preparation; ü Develop skills for understanding the business problem and define properly the

corresponding analytical problem; ü Get acquainted with some of the common statistical methods for supervised

learning; ü Get experience in estimation, validation, interpretation, and implementation of

supervised learning models; ü Develop skills for delivering data-driven decisions; ü Develop skills for writing R-scripts.

University of Cologne Faculty of Management, Economics and Social Sciences

Learning method

The learning process includes in-class and out-of-class activities. The course content is communicated with students via presentations, illustrative examples, and case studies. The presentations provide the theoretical framework and outline core concepts, methods, and techniques. The related out-of-class activity is reading the recommended textbook pages. Illustrative examples aim to demonstrate how to apply certain methods and techniques in practice. For this purpose are used textbook examples. Students are advised to re-perform all the demonstrated empirical exercises by their own. Each topic ends with performance of applied analysis on a given case study. The main goal of this activity is to develop skills for understanding the business problem, define properly the analytical problem, carry on precise data preparation, make choice of theoretical model, and deliver solution at the end of the day. The related out-of-class activity is preparing a project. The grade is formed on the basis of a project presentations.

Course materials

All course materials are freely available online.

Textbooks:

[1] James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer Texts in Statistics. Download the textbook here: http://www-bcf.usc.edu/~gareth/ISL/

[2] De Jonge, E., Van der Loo, M. (2013). An Introduction to Data Cleaning with R. Statistics Netherlands. Download the textbook here: https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf

Case studies:

Case study 1: Predictive modelling: the case of Sofia air pollution. Case study 2: Classification modelling: the case of credit card churners.

Video tutorials:

A sequence of short video tutorials will be uploaded at the webpage of the course. Videos are intended to introduce students to RStudio thus during the classes they could focus on the course content rather than bother about technical details.

University of Cologne Faculty of Management, Economics and Social Sciences

Project Assignment:

A list of detailed instructions on how to write and present a course project will be uploaded at the webpage of the course.

Pre-course assignment

The following resources will be uploaded two weeks before the course opens:

• Short video tutorials introducing RStudio; • Case studies: description and related datasets; • Project assignment.

All the applied analysis is performed in R /RStudio/. Therefore, students are asked to get acquainted with the short video tutorials provided by the lecturer and re-perform the exercises presented there. Also, reading through the three case studies prior the start of the course is highly recommended.

Recommended prior knowledge

No prior knowledge is required. Yet, this course is particularly well-suited to students eager to find how we can turn data into knowledge thus adding value to the business.

Course structure and content details

Date Topic Readings

5-Aug-2019 Data Preparation – part 1

[2]: From raw data to technically correct data, pp. 12-30.

5-Aug-2019 Case study in-class discussion Case study 1

6-Aug-2019 Data Preparation – part 2

[2]: From technically correct data to consistent data, pp. 31-50.

6-Aug-2019 Case study in-class discussion Case study 1

7-Aug-2019 Linear Regression Analysis [1]: Simple Linear Regression; Multiple Linear Regression, pp. 61-82.

7-Aug-2019 An illustrative example discussion [1]: The Marketing Plan, pp. 102-104.

University of Cologne Faculty of Management, Economics and Social Sciences

8-Aug-2019 Case study in-class discussion Case study 1

8-Aug-2019 Regression trees [1]: Tree-Based Methods, pp. 303-311.

8-Aug-2019 An illustrative example discussion [1]: Housing Values in Suburbs of Boston, pp. 327-328.

12-Aug-2019 Classification via logistic regression.

[1]: Classification: Logistic Regression, pp. 127-138.

12-Aug-2019 An illustrative example discussion [1]: The Stock Market Data, pp. 154-161.

13-Aug-2019 Classification trees [1]: Tree-Based Methods, pp. 311-316.

13-Aug-2019 Case study in-class discussion Case study 2

14-Aug-2019 Bagging, Random Forests, Boosting

[1]: Tree-Based Methods, pp. 316-323.

14-Aug-2019 Case study in-class discussion Case study 2

15-Aug-2019 Presentation of the course projects.

Projects Instructions

Examination process

Project (due 15-Aug-2019)

Grading

Grading is performed following the evaluation schedule as outlined in the project assignment.

University of Cologne Faculty of Management, Economics and Social Sciences

Bio

Boryana Bogdanova, PhD

Associate Professor

Sofia University “St. Kliment Ohridski” Faculty of Economics and Business Administration Department of Statistics and Econometrics

Contact details: 1113 Sofia, 125 Tsarigradsko Shosse Blvd., bl.3

bpelova@feb.uni-sofia.bg; bpelova@gmail.com

Link to my webpage at Sofia University, FEBA

Teaching: Co-founder of the MSc Program in Business Analytics at FEBA, Sofia University.

Core courses: Data Mining, Econometrics, Quantitative Methods

Research interests: Empirical Finance, Behavioral Finance, Predictive Modelling, Time Series Analysis, Wavelet Analysis, etc.

Mentorship: Mentor at Datathons and Challenges organized by Data Science Society