Course code InfT5057

Credit points 3

Data Processing and Business Intelligence

Total Hours in Course120

Number of hours for lectures12

Number of hours for seminars and practical classes24

Independent study hours84

Date of course confirmation19.01.2022

Responsible UnitDepartment of Control Systems

Course developer

author prof.

Līga Paura

Dr. agr.

Course abstract

The study course covers data processing and business intelligence methods and tools that provide support for solving business issues. Master students learn about data mining, data visualization, data tools and infrastructure, and how to help organizations to make more data-driven decisions. Data processing and business intelligence programs will be used to solve the problems. Data analysis will be illustrated by real data examples.
The aim of the study course is to provide students with in-depth knowledge in the data mining, data visualization and analysis, and skills in data analysis and BI tools.

Learning outcomes and their assessment

• depth knowledge and critical understanding about data visualisation and data analysis methods - theory test, independent work ;
• choose and apply methods according to research task - completed practical works, independent work;
Professional skills:
• independently visualize and perform data processing in research work, using data visualization and processing tools - completed practical works, independent work;
Soft skills
• independently choose appropriate data visualisation and data analysis methods according to data analysis theory - completed practical works, independent work;
• able to discuss about principles of choice the methods and their application and implementation of the specific problem at study - completed practical works, independent work, a test with calculations;
• to realize data visualisation and data analysis in master work by using a data processing and BI soft wares - theory test, independent work, a test with calculations;
• to interpret the results and formulate conclusions and link them to the business sector; to make decisions and to analyse results - independent work, a test with calculations;.

Course Content(Calendar)

1. Introduction to the study course. Data classification. Data preparation for business analysis. Graphical presentation of data. Classification of data processing methods. Data processing tools. [Lectures - 2h, Practical works - 2h]
2. Data analysis with Business Intelligence (BI) tools. Introduction to BI tools. BI tools database and data sources. [Lectures - 1h, Practical works - 2h]
3. Basic functions of BI tools and their effective application. Graphical representation of the company's financial indicators and creation of reports with BI tools.
4. Interactive and Dynamic Reports with Power BI. Data grouping and Binning. Interactive graphs and data analysis. [Lectures - 1h, Practical works - 2h]
Independent work: Creating a report based on the company financial data with the BI tool and write the interpretation of the obtained results
5. Qualitative data visualisation and analysis. Contingency tables. Contingency analysis. Hypothesis testing. Chi-Square independence test: 2x2 contingency tables. Fisher Exact test: 2x2 contingency tables. Chi-Square independence Test: rxc contingency tables. [Lectures - 1h, Practical works - 2h]
6. Association between qualitative variables: contingency coefficient, Cramer coefficient, φ coefficient. Choice of association coefficients depending on the type of contingency table. [Lectures - 1h, Practical works - 2h]
7. Correlation analysis. Pearson correlation coefficient. Hypothesis testing. Spearmen’s rank correlation coefficient. [Lectures - 1h, Practical works - 2h]
8. Linear regression analysis: one factor linear regression model, hypothesis tests on the regression coefficients. [Lectures - 1h, Practical works - 2h]
9. Assumptions of linear regression. [Lectures - 1h, Practical works - 2h]
10. Linear regression analysis: multiple linear regression and some experimental design models, hypothesis tests on the regression coefficients, assumptions of multiple linear regression. [Lectures - 1h, Practical works - 2h]
11. Nonlinear regression models: formulation and application of a nonlinear regression models. [Lectures - 1h, Practical works - 2h]
Test: Contingency analysis, one factor and multiple regression analysis. [Practical works - 2h]

Requirements for awarding credit points

The course includes theory test and test with calculations - to be taken during practice work in classroom. Independent work: Creating a report based on the company financial data with the BI tool and write the interpretation of the obtained results.
Final assessment of the study course – final grade (mark) is given as an accumulative assessment of the study results.

Description of the organization and tasks of students’ independent work

Within the framework of the study course description for independent work is given 84 hours. Independent studies (work) are organized as follows: preparing for theory test (24 hours); preparing independent work with calculations (40 hours); learning and preparing for tests with calculations (20 hours).

Criteria for Evaluating Learning Outcomes

The final grade in the study course includes:
20% Theory test: methods classification and application for data analysis;
40 % independent work: creating a report based on the company financial data with the BI tool and write the interpretation of the obtained results.
35% Test with calculations: contingency analysis, correlation and regression analysis.
The evaluation of the works depend on the degree of completion.

Compulsory reading

1. Kirk A. Data visualisation: a handbook for data driven design. Los Angeles: SAGE, 2019. 312 p.
2. Corr L., Stagnitto J. Agile Data Warehouse Design: collaborative dimensional modeling, from Whiteboard to Star Schema. UK: Decision Press, 2014. 304 p.
3. Arhipova I., Balina S. Statistika ekonomikā un biznesā: risinājumi ar SPSS un MS Excel: mācību līdzeklis. Rīga: Datorzinību centrs, 2006. 359 lpp.
4. Kabacoff R. I. R in action: data analysis and graphics with R. Second edition. Shelter Island, NY: Manning, 2015. 579 p.

Further reading

1. Data science & big data analytics: discovering, analyzing, visualizing and presenting data. EMC Education Services. Indianapolis, IN: John Wiley and Sons, 2015. 410 p.
2. Advanced Analytics with Power BI: Microsoft. Pieejams:
3. Gujarati D. N. Basic econometrics. 3rd ed. New York [etc.]: McGraw-Hill, Inc., 1995. 838 p.

Periodicals and other sources

1. European Journal of Management and Business Economics: ISSN 2444-8451 Elsevier data base
2. Journal of Data Analysis and Information Processing: ISSN Online: 2327-7203. Pieejams:


Obligatory course for professional master’s study programme “Business Management”