Veidlapa Nr. M-3 (8)
Study Course Description
Data Analysis and Strategy
Course Code
SZF_164
Branch of Science
-
ECTS
3.00
Target Audience
Business Management; Management Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Aldis Ērglis
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts
Dzirciema street 16, Rīga, szf@rsu.lv
About Study Course
Objective
Learn and understand quantitative and qualitative analysis options for strategic elements of the organization (value chain, business capabilities, business processes, goals and organizational structure) using data analysis and business intelligence (BI) techniques.
Preliminary Knowledge
Prior knowledge of data analysis fundamentals, data sources, data structures, and visualization is required. Prior knowledge of organization management principles is desirable.
Learning Outcomes
Knowledge
1.At the end of the study course, the students have gained in-depth knowledge about the possibilities of explaining the organization's activity with the help of data analysis and the value that such analysis can provide; - able to apply data analysis techniques and approaches appropriate to the business task; - have acquired practical skills in creating a data analysis task, loading data from various data sources, data modelling and data visualization; - able to formulate and define examples of the application of data analysis, machine learning and artificial intelligence; have learned the principles of building the business and technical architecture of data solutions and can name and describe its elements; - familiarized with the approaches to creating a data strategy and its elements.
Skills
1.At the end of the study course, students: - can identify and define analytical tasks at the individual and organizational level; - able to create a solution design for analytical tasks; - able to perform data modeling and data visualization according to the business task; - able to choose and apply modern quantitative data analysis methods; - able to assess and explain the business value of data analysis, dashboards and reports; - able to use data analysis to define business models.
Competences
1.At the end of the study course, the students will: - be able to independently evaluate and create a data analysis application or solution design according to the business problem; - will be able to choose appropriate tools and analysis techniques for data analysis tasks; - will be able to assess the maturity of the organization in the field of data analysis, record the current situation, develop recommendations for improvements; - will be able to determine the compliance of the organization's analytics with business goals and the degree of automation.
Assessment
Individual work
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Individual work |
-
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-
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The student's knowledge will be tested in two ways: in this way, practical tasks will be assigned during the lessons, the full performance of which will have to be done outside contact hours. Six such tests are planned, the purpose of which is to test the acquired knowledge of the presented topics and the ability to apply data analysis and visualization techniques practically. Generally, getting up to 60% of the grade for these practical works is possible. In addition, there will be an analysis of the situation after the 6th lesson, which will include solving a business task in the field of process automation and their evaluation and categorization with the help of the process mining method. This type of test will account for up to 20% of the total grade. At the end of the course, there will be a final exam on all topics covered, making up to 20% of the total grade.
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Examination
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Title
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% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
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The student's final assessment consists of: - Practical work no. 1 result - 10% - practical work no. 2 result - 10% - practical work no. 3 result - 10% - practical work no. 4 result - 10% - practical work no. 5 result - 10% - practical work no. 6 result - 10% - Case study result - 20%, final exam - 20%.
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Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
|
2
|
Topics
|
The role and application of data analysis (BI) in the strategic management of the company
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-
Lecture
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Intelligent decision making
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Correlations and causation
|
-
Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data-driven business models
|
-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Modern tools and techniques for data analysis
|
-
Lecture
|
Modality
|
Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Recommender systems
|
-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
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ONA - organization network analysis
|
-
Lecture
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Modality
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Location
|
Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Quantitative business process analysis with Process Mining
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data analysis for digital transformation
|
-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Auditorium
|
2
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Topics
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Knowledge management and the role of data analysis
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data stewards and data support for business process owners
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Auditorium
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2
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Topics
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Proactive analytics, BI development scenarios
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Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Competing on Analytics: The New Science of Winning. 2017Suitable for English stream
2.
Smart Business: What Alibaba's Success Reveals about the Future of Strategy. 2018Suitable for English stream
3.
The Book of Why: The New Science of Cause and Effect – Most Thought-Provoking. 2020Suitable for English stream
4.
INTELLIGENT AUTOMATION: Learn how to harness Artificial Intelligence to boost business & make our world more human. 2021Suitable for English stream
Additional Reading
1.
Key Management Models: The 75+ Models Every Manager Needs to KnowSuitable for English stream