| Learning objectives |
To achieve the grade 12, students should meet the
following learning objectives with no or only minor mistakes or
errors:
- Demonstrate an understanding of web APIs and their role in
supporting business ecosystems, data exchange, integration, and
AI-enabled applications
- Identify and compare standard data formats used to support data
exchange in business intelligence and API-based integration
processes.
- Evaluate different data storage and data modeling approaches
used for managing business data in operational and analytical
environments.
- Demonstrate understanding of relational database structures and
apply SQL to process, manage, transform, and retrieve business
data.
- Describe the end-to-end business data analysis process,
including data collection, transformation, storage, modeling,
analysis, and reporting.
- Demonstrate application of analytical skills to implement data
processing tasks and develop insights using business intelligence
and reporting tools.
- Show competence in using data analysis and reporting tools to
prepare, explore, and present business data.
|
| Course prerequisites |
| The are no pre-requisites however student should
be able to install software and setup configurations following step
by step guidelines |
| Examination |
|
Business Data
Processing and Business Intelligence:
|
| Exam
ECTS |
7,5 |
| Examination form |
Written sit-in exam on CBS'
computers |
| Individual or group exam |
Individual exam |
| Assignment type |
Written assignment |
| Duration |
4 hours |
| Grading scale |
7-point grading scale |
| Examiner(s) |
One internal examiner |
| Exam period |
Winter |
| Aids |
Limited aids, see the list below:
The student is allowed to bring - Any calculator
- In Paper format: Books (including translation dictionaries),
compendiums and notes
The student will have access to - Advanced IT application package
|
| Make-up exam/re-exam |
Same examination form as the ordinary exam
The number of registered candidates for the make-up
examination/re-take examination may warrant that it most
appropriately be held as an oral examination. The programme office
will inform the students if the make-up examination/re-take
examination instead is held as an oral examination including a
second examiner or external examiner.
|
Description of the exam
procedure
This is a four hours sit-in examination. Students will be
provided with one or more datasets to be used as context
for the examination.
The examination assesses students’ ability to apply
the conceptual and practical knowledge gained throughout
the course. Questions may require working directly with the
provided data as well as addressing broader concepts and techniques
covered in the course.
Students are expected to demonstrate their learning in alignment
with the course learning objectives through appropriate analysis,
reasoning, and
application.
|
|
| Course content, structure and pedagogical
approach |
|
Data-driven decision making is a core component of modern
business practice. As a result, it is increasingly important for
business students to understand the end-to-end process of business
data analytics.
This course provides foundational knowledge of the major stages of
business data analytics, including data collection, storage,
transformation, processing, modeling, and reporting. Students gain
hands-on experience with real datasets and learn the complete
analysis lifecycle through practical examples. Topics include:
APIs in Business and AI Ecosystems: How
APIs enable data access, integration, and sharing across business
systems, open web services, and AI-enabled applications, including
emerging integration patterns such as MCP.
Data Formats: Overview of common data formats
used for digital data exchange.
Data Storage Models: High-level understanding
of storage approaches for operational and analytical relational
databases.
Structured Query Language (SQL): Using SQL
for querying, aggregating, and filtering data, and as a quick
reporting tool.
Data Warehouse Concepts: Introduction to data
warehouse architectures and schema designs.
Tools: Tableau or Power BI for reporting;
PostgreSQL for relational databases and SQL; and a scripting
language or visual tool for interacting with Web
APIs.
|
| 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
- Classic and basic theory
- New theory
- Models
Research-like activities
- Data collection
- Analysis
- Discussion, critical reflection, modelling
|
| Description of the teaching methods |
The course consists of 30 hours of online
pre-recorded lectures. These are held as a mixture of theoretical
teaching, hands on demo and step by step tutorials. Students will
be provided code snippets and guides to supplement their technical
learning.
The course will have 10 hours of on campus workshop sessions. The
workshops will primarily be Q&A sessions to cover any queries
that arise from the teaching material covered during the lectures.
The on campus session will also provide in person support for any
installation issues regarding tools required to cover each module
in this course.
The students will be working with real-life data sources. The
students will be either provided with academic license access to
the tools or they will be asked to register for trial version of
same tools that are selected for data analysis learning in this
course.
Students will also experience how to register and gain access to
publicly available data sets using techniques like Web Apis and
storage of data will be demoed using relational database storage
systems.
The students will work on a mini project to demonstrate their
learning in the final exam. |
| Feedback during the teaching period |
There will be three ways to provide feedback. a)
Two online quizzes with multiple choice questions and implicit
feedback with survey results. b) On demand Individual feedback
about topics covered and in relation with exam project c) In person
feedback during in person QA sessions.
|
| Student workload |
| Lectures |
30 hours |
| Prepare to class |
56 hours |
| On Campus Q&A support sessions |
10 hours |
| Project work |
90 hours |
| Exam and prepare |
20 hours |
| Total |
206 hours |
|
| Expected literature |
|
, The literature can be changed before the semester
starts. Students are advised to check the syllabus on
Canvas before buying any material.
The literature consists of practical hands on guides for each
technology covered.
There are readings associated with each topic.
-
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business
intelligence and analytics: From big data to big impact. MIS
quarterly, pp.1165-1188.
-
Han, J., Pei, J. and Kamber, M., 2011. Data mining: concepts and
techniques. Elsevier. Chapter 1 & Chapter 4
-
Nurseitov, N., Paulson, M., Reynolds, R. and Izurieta, C., 2009.
Comparison of JSON and XML data interchange formats: a case study.
Caine, 9, pp.157- 162.
-
De, B., 2017. API Management. In API Management (pp. 15-28).
Apress, Berkeley, CA. Chapter 1 and Chapter 2
-
MongoDb official documentation
-
Silberschatz, A., Korth, H., Sudarshan, S. Database System
Concepts. 7th Edition. McGraw-Hill Education. ISBN13:
9780078022159
-
Fisher, D., DeLine, R., Czerwinski, M. and Drucker, S., 2012.
Interactions with big data analytics. interactions, 19(3),
pp.50-59.
-
Knaflic, C. (2015). Storytelling with data: A data visualization
guide for business professionals. John Wiley & Sons, Chapter 1
– 8
-
Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour
through the visualization zoo. Commun. ACM, 53(6), 59-67
-
Jukic N, Vrbsky S, Nestorov S & Sharma A (2020). Database
Systems: Introduction to Databases and Data Warehouse, ISBN:
978-1-943153-68-8. Chapter
7,8,9
|
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