Veidlapa Nr. M-3 (8)
Study Course Description
Data Analysis and Strategy
Course Code
SZF_164
Branch of Science
Electrical engineering, Electronic engineering, Information engineering
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.Able to make recommendations and interpretations about the ability to explain an organization’s activities through data analysis and the value that such an analysis can provide.
Individual work and tests
Individual work • Analysis of correlations and causation • Explain business models with data • Development of recommendations for a data source • Final test
2.Able to formulate and define examples of data analysis, machine learning and AI applications.
Individual work and tests
Explain business models with data • Individual work • Final test
3.Capable of interpreting the approaches and elements of the creation of a data strategy.
Individual work and tests
Final test • Individual work • Development of recommendations for a data source • Customer journey analysis • Process mining analysis • Analysis of organization communication data
4.Able to interpret and explain the principles of the business and technical architecture of data solutions and able to name and characterise its elements.
Individual work and tests
Analysis of organization communication data • Development of recommendations for a data source • Process mining analysis • Customer journey analysis • Individual work • Final test
5.Able to identify and define analytical tasks, at individual and organizational level.
Individual work and tests
Final test • Customer journey analysis • Explain business models with data • Process mining analysis • Development of recommendations for a data source • Analysis of organization communication data
Skills
1.Able to create a solution design for analytical tasks.
Individual work and tests
Customer journey analysis • Final test
2.Able to perform data modeling and data visualization according to the business task.
Individual work and tests
Final test • Explain business models with data
3.Capable of selecting and applying modern quantitative data analysis methods.
Individual work and tests
Process mining analysis • Final test
4.Can assess and explain the business value of data analysis, control panels, and reports.
Individual work and tests
Final test • Analysis of organization communication data • Analysis of correlations and causation
5.Able to apply data analysis to explain business models.
Individual work and tests
Explain business models with data • Development of recommendations for a data source • Process mining analysis • Final test
6.Able to use appropriate data analytics techniques and approaches for the business task.
Individual work and tests
Final test • Customer journey analysis • Explain business models with data • Individual work
7.Have acquired practical skills in creating a data analysis task, loading data from different data sources, data modeling, and data visualization.
Individual work and tests
Individual work • Development of recommendations for a data source • Final test
Competences
1.Will be able to independently evaluate and create, according to the business problem, the design of the data analysis application or solution.
Individual work and tests
Final test • Development of recommendations for a data source
2.Will be able to select tools and analysis techniques appropriate to data analysis tasks.
Individual work and tests
Analysis of correlations and causation • Analysis of organization communication data • Final test
3.Will be able to assess the maturity of the organisation in the field of data analysis, to capture the current situation, to draw up recommendations for improvement.
Individual work and tests
Final test
4.Will be able to assess the relevance of organization analytics to business goals and the degree of automation.
Individual work and tests
Final test • Analysis of organization communication data
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
20.00% from total grade
|
10 points
|
|
In independent work, students should create group work on topics acquired in the course by analysing project data, visualizing, drawing up recommendations. |
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Analysis of correlations and causation |
10.00% from total grade
|
10 points
|
|
Perform correlation analysis on the proposed data sources. Visually display causal relationships. |
||
|
2.
Explain business models with data |
10.00% from total grade
|
10 points
|
|
Visualize data models for proposed data, interpret data models. |
||
|
3.
Development of recommendations for a data source |
10.00% from total grade
|
10 points
|
|
4.
Analysis of organization communication data |
10.00% from total grade
|
10 points
|
|
Analysis of the organisation’s communication schedule, identification and analysis of communication paterns. |
||
|
5.
Process mining analysis |
10.00% from total grade
|
10 points
|
|
Analyze process activity data and visualize the process using process mining visuals. |
||
|
6.
Customer journey analysis |
10.00% from total grade
|
10 points
|
|
Customer journey analysis, calculation and ubiquity of customer experience metrics (NPS, CSAT, others). |
||
|
7.
Final test |
20.00% from total grade
|
10 points
|
|
Perform data analysis and data visualization for sales data, including necessary data analysis elements and visualization techniques. |
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
The role and application of data analysis (BI) in the strategic management of the company
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
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
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data-driven business models
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Modern tools and techniques for data analysis
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Recommender systems
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
ONA - organization network analysis
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
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
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Knowledge management and the role of data analysis
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data stewards and data support for business process owners
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Proactive analytics, BI development scenarios
|
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