| Learning objectives |
Upon completion of this course, participants will
be able to:
- Articulate and apply core concepts in big data analytics.
- Develop proficiency in statistical methods and relevant machine
learning algorithms.
- Apply regression models, clustering, and predictive analytics
to various datasets.
- Balance theoretical understanding with practical application of
statistical methods and machine learning algorithms.
- Effectively communicate findings through data visualization to
diverse stakeholders.
- Master the art of storytelling through data visualization to
clearly convey complex insights.
- Discuss ethical issues in the use of Big Data in economics and
finance.
- Explore privacy concerns, data security, and regulatory
frameworks.
- Proactively address ethical issues and privacy concerns in Big
Data.
- Examine real-world applications in economic and
finance.
|
| Course prerequisites |
This course is offered as an elective to incoming
exchange students. It is a mandatory course for the MSc in Applied
Economics and Finance. It is assumed that students have knowledge
similar to the entry requirements for this program.
Students will be selected based on their application. Please send a
1-page motivational letter, a 1-page CV, and a grade transcript to
ily.stu@cbs.dk before the registration deadline for elective
courses. |
| Examination |
|
Big Data
Analytics for Economic and Financial
Decision-Making:
|
| Exam
ECTS |
7,5 |
| Examination form |
Home assignment - written product |
| Individual or group exam |
Individual exam |
| Size of written product |
Max. 10 pages |
| Assignment type |
Project |
| Release of assignment |
The Assignment is released in Digital Exam (DE)
at exam start |
| Duration |
Written product to be submitted on specified date
and time. |
| Grading scale |
7-point grading scale |
| Examiner(s) |
One internal examiner |
| Exam period |
Summer |
| Make-up exam/re-exam |
Same examination form as the ordinary
exam
|
|
| Course content, structure and pedagogical
approach |
|
Upon completion of this course, participants will be able
to:
Big Data Fundamentals:
- Articulate and apply core concepts in big data
analytics.
Statistical Techniques and Machine
Learning:
- Develop proficiency in statistical methods and relevant machine
learning algorithms.
- Apply regression models, clustering, and predictive analytics
to various datasets.
- Balance theoretical understanding with practical application of
statistical methods and machine learning algorithms.
Data Visualization and Interpretation:
- Effectively communicate findings through data visualization to
diverse stakeholders.
- Master the art of storytelling through data visualization to
clearly convey complex insights.
Ethical and Privacy Considerations:
- Discuss ethical issues in the use of Big Data in economics and
finance.
- Explore privacy concerns, data security, and regulatory
frameworks.
- Proactively address ethical issues and privacy concerns in Big
Data.
Applications in Finance and Business:
- Examine real-world applications in economic and
finance.
Hands-on Projects
- Examine real-world applications in economic and
finance.
Upon completion, participants will possess the skills to
leverage Big Data, enabling informed decisions and optimized
strategies in various economic contexts.
This course explores the intersection of Big Data and
Economics/Finance, highlighting the transformative role of data
analytics in decision-making. Participants will gain practical
insights into handling large-scale data sets, applying advanced
statistical techniques, and utilizing machine learning algorithms.
The course includes practical exercises ensuring hands-on
experience with real-world data. Examples will illustrate how big
data is used in economic analysis and applied business contexts,
providing a comprehensive understanding of its
applications.
|
|
| Research-based teaching |
|
CBS’ programmes and teaching are research-based. The following
types of research-based knowledge and research-like activities are
included in this course:
Research-based knowledge
Research-like activities
- Students conduct independent research-like activities under
supervision
|
| Description of the teaching methods |
| In-class lectures with PC-based
exercises. |
| Feedback during the teaching period |
| Feedback will be provided both as part of
discussions in the class and of exercises. |
| Student workload |
| Exam |
20 hours |
| Classes and Exercises |
68 hours |
| Preparation |
118 hours |
|
| Expected literature |
|
Selected chapters from
- James, Witten, Hastie, and Tibshirani (2023), “An Introduction
to Statistical Learning with Applications in R/Python”. Second
Edition, Springer Text in Statistics
- Knaflic (2015), “Storytelling with Data – a Data Visualization
Guide for Business Professionals”,
Wiley
|
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