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2022/2023  KAN-CDSCV1003U  Data Science for Business Applications

English Title
Data Science for Business Applications

Course information
Language English
Course ECTS 15 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Daniel Hain - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 01-02-2022

Relevant links
Learning objectives
After completing the course, students should be able to
  • Comprehend and participate in current professional and academic discussions in applied data science, big data analytics, and artificial intelligence.
  • Critically reflect possibilities and constraints related to the implementation of data-driven methods.
  • Identify business problems that can be solved by the use of machine learning and artificial intelligence techniques.
  • Apply a data-driven logic, structure, and workflow to problem-solving.
  • Understand, manipulate, analyze, and visualize a variety of structured and unstructured data, such as tabular, text, and relational data.
  • Apply machine learning and artificial intelligence techniques to solve business problems – also in Big Data contexts.
  • Describe and communicate the results of data analysis in a precise, understandable and informative manner, using appropriate data description and visualization techniques.
  • Expand their knowledge in various data science topics of interest and relevance via self-learning.
Course prerequisites
As a hands-on data-analytics course, the students are expected to continuously carry out analysis and data manipulation in the Python and/or R programming languages. As such the course requires an interest in and commitment to hands-on learning and acquiring the necessary coding skills. However, no prior coding knowledge and experience is required.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 2
Compulsory home assignments
Each student has to get 2 out of 3 home assignments approved in order to participate in the ordinary exam. The assignments are written individually and are max. 5 pages each.

There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot participate due to documented illness, or if a student does not get the activities approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (10 pages) which will cover 2 mandatory assignments.
Examination
Data Science for Business Applications:
Exam ECTS 15
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-4
Size of written product Max. 30 pages
Assignment type Project
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
If a student fails the ordinary exam, then the course coordinator chooses whether the student will have to hand in a revised product for the retake or a new project.
Description of the exam procedure

The final exam is based on a written product that should give students an opportunity to work on an end-to-end data science project. Examples of final projects include the development of recommender systems, algorithmic trading models, customer segmentation analyses, and many other business applications of Machine LEarning and Artificial Intelligence
Course content, structure and pedagogical approach

Recent developments in information and communication technology (ICT), growing data quantities (Big Data), and rapidly improving techniques to analyze it (Machine Learning, Artificial Intelligence, Deep Learning) are fundamentally changing the context and challenges that businesses, public organizations, and researchers are facing. Competencies to identify patterns and make sense of data as well as to inform the decisions of managers, policymakers and other actors are in high demand on the labor market. This course is developed as intensive hands-on training in Data Science: The combination of data sourcing, management, analytics, visualization, and communication. 

 

Data Scientists can apply their skills to problems in various areas. Within business, they can contribute to extracting and combining knowledge from existing Enterprise Resource Planning (ERP) systems, data warehouses, and external sources, and use them to support data-driven strategic decision making. They are able to use sophisticated visualization techniques such as dynamic dashboards to provide business intelligence and executive guidance.

 

The course is structured in three parts, providing the students with a full overview of methods, techniques, and workflows currently used in business analytics, machine learning, and artificial intelligence. Students are not expected to have programming experience.

 

  1. Applied Data Science and Machine Learning: This module will prove a condensed introduction to the “Data Science Pipeline”, introducing students to methods, techniques, and workflows in applied data analytics and machine learning, including data acquisition, preparation, analysis, visualization, and communication.
  2. Network Analysis and Natural Language Processing: Focuses on analyzing a variety of unstructured data sources. Particularly, students will learn how to explore, analyze, and visualize natural language (text) as well as relational (network) data.
  3. Deep Learning and Artificial Intelligence for Analytics: Introduces to the most recent developments in machine learning, which are deep learning and artificial intelligence applications. The module will provide a solid foundation for this exciting and rapidly developing field. Students will learn whether and how to apply deep learning techniques for business analytics and acquire proficiency in new methods autonomously.

 

The course is a data-analytics course with a lot of hands-on exercises using Python and R programming languages.
Description of the teaching methods
This course is a blended-learning course. Some of the lectures and exercises will be delivered online but there will be some activities, especially a few lectures, and hands-on exercise workshops will be conducted on campus. The hands-on exercises will be offered in both Python and R programming languages. The students are free to choose either one or both languages for the exercises and their projects.

To enhance the students' ability to engage in continuous self-guided learning, the efficient acquisition of diverse sets of knowledge, and the transfer of acquired knowledge into practical outcomes, the course will use online resources and blended learning techniques along with e-learning tools such as podcasting, online tutorials, and mini-assignments, as integral parts of the teaching methodology in order to enhance student engagement outside the classroom. Physical face-to-face time will be centered around the tacit and interactive components of the problem-solving processes, and the communication and demonstration of complex methods and larger problems.
Feedback during the teaching period
In-class exercises will be used systematically to test students’ understanding of the course content and increase their ability to reproduce acquired knowledge and skills autonomously. Students will receive continuous in-class feedback on them.

Individual between-classes assignments will be used to further solidify acquired knowledge and skills. Students will receive in-class feedback on these assignments, as well as anonymous written peer-feedback.

Additionally, feedback in the forms of question / answers and discussions during the class will be provided.
Student workload
Lectures and Exercises 60 hours
Self study 100 hours
Prepare for the class 60 hours
E-learning (eg. interactive online courses) 40 hours
Project work and report 140 hours
Exam and prepare 12 hours
Total 412 hours
Expected literature

Please check the updated literature on Canvas before buing any material.

 

Main teaching references:

  • VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. O'Reilly Media, Inc. Online available here
  • Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc. Online available here

 

 

The teaching material will include:

  • Scientific Articles

  • Lecture slides

  • Computational Notebooks

  • E-Learning resources

  • Hand-on coding and analytics exercises
Last updated on 01-02-2022