Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 25/26.P.1.00

Description Period: 2025./26 academic year pavasara semestris

Study Course Accepted: -

Study Course Description

Introduction to Data Processing, Analysis and Visualisation

Main Study Course Information

Course Code
SZF_270
Branch of Science
Other engineering and technologies
ECTS
3.00
Target Audience
Biology; Business Management; Civil and Military Defense; Clinical Pharmacy; Communication Science; Dentistry; Digital Health; Health Management; Information and Communication Science; Juridical Science; Law; Life Science; Management Science; Marketing and Advertising; Medical Services; Medical Technologies; Medicine; Midwifery; Nursing Science; Pedagogy; Person and Property Defence; Pharmacy; Political Science; Psychology; Public Health; Rehabilitation; Social Anthropology; Social Welfare and Social Work; Sociology; Sports Science; Sports Trainer
LQF
Level 6
Study Type And Form
Full-Time

Study Course Implementer

Course Supervisor
Dina Barute
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts

SZF, Kuldigas Street 9C, szf@rsu.lv

About Study Course

Objective

The course aims to develop basic student skills in data preparation, descriptive analysis, and visualization to create correct summaries, clear visualizations, and simple interactive reporting (Dashboard) from the output data in Excel and BI tools (Power BI/tableau). During the course, students develop the skills to ensure data quality, correctness of calculations, reproducibility and responsible use of data and AI tools, including validation of results.

Preliminary Knowledge

High school math course. Basic skills for working in MS Excel are desirable.

Learning Outcomes

Knowledge

1.Describes the importance of data, data breakdowns, types of data sources and data quality criteria

Individual work and tests

Project job: from data to interactive report

2.Defines basic concepts of descriptive statistics and their interpretation

Individual work and tests

Project job: from data to interactive report • Excel practice: data processing, summaries, and visualization

3.Explains data visualization goals and best practice principles (schedule selection, scale, readability, availability)

Individual work and tests

Excel practice: data processing, summaries, and visualization • Project job: from data to interactive report

4.Explains the basics and key concepts of the Power BI (tableau) workflow (data loading, transformation, analysis, visualization, interactivity, sharing)

Individual work and tests

Project job: from data to interactive report

5.Describes the principles of responsible use of data and AI (confidentiality, validation, reproducibility, risks, source indication)

Skills

1.Imports data in Excel, structures it in a table, performs data cleaning and transformations, and creates summaries using PivotTables and PivotCharts

Individual work and tests

Excel practice: data processing, summaries, and visualization

2.Calculate and interpret descriptive statistics (measures of central tendency, dispersion, quartiles/percentiles) and use them for initial data exploration

Individual work and tests

Excel practice: data processing, summaries, and visualization • Project job: from data to interactive report

3.Create and design charts in Excel based on best practices (axes, units, headings, comparability, color usage, abstracts)

Individual work and tests

Excel practice: data processing, summaries, and visualization

4.Loads data to Power BI, performs basic transformations with Power Query, creates a simple data model (relationship between tables), creates visualizations, and creates an interactive report

Individual work and tests

Project job: from data to interactive report

5.Loads data into tableau public, creates basic visualizations, and creates a simple dashboard, subject to disclosure restrictions and data security principles

Individual work and tests

Project job: from data to interactive report

6.Use AI tools (e.g. Julius AI, ChatGPT) as support for data preparation, analysis and visualisation, formulate a task in a structured prompt, and perform cross-checks

Competences

1.Organise work independently from formulating a goal to presenting results by choosing appropriate tools and techniques

Individual work and tests

Project job: from data to interactive report

2.Assess data quality, calculation correctness and interpretation of visualizations by identifying misleading solutions and error risks

Individual work and tests

Project job: from data to interactive report • Excel practice: data processing, summaries, and visualization

3.Develop a simple, usable dashboard with clear structure, reasonable metrics and interactivity

Individual work and tests

Project job: from data to interactive report

4.Record and describe in a structured manner the steps of data processing and analysis (data preparation, choice of indicators, justification of visualizations), ensuring the transparency and reproducibility of the work

Individual work and tests

Project job: from data to interactive report

5.Adheres to the principles of responsible use of data and AI (confidentiality, ethics, sourcing, validation)

Individual work and tests

Project job: from data to interactive report

Assessment

Individual work

Title
% from total grade
Grade
1.

Excel practice: data processing, summaries, and visualization
40.00% from total grade
10 points

Students solve practical tasks in an Excel environment in data processing and summarizing (including structuring, sorting/filtering, calculations, and PivotTables) and create appropriate visualizations. Student chooses solving techniques independently (e.g. Excel functions and calculations or PivotTables/PivotCharts). Evaluates the correctness of solutions, the design of data and visualizations, and the interpretation of results. A submittable Excel file with completed tasks.

Examination

Title
% from total grade
Grade
1.

Project job: from data to interactive report
60.00% from total grade
10 points

Student develops’ data to Interactive report ’solution by choosing appropriate tools (Excel, Power BI, and/or tableau; AI tools as needed). Within the framework of the project, the student prepares data for further work (e.g. cleaning/transformation and basic validation), chooses reasonable indicators, creates qualitative visualizations and summarizes results in an interactive report according to the purpose. As a result, a project file is submitted and key findings, assumptions and limitations are presented, respecting the principles of data quality, reproducibility and responsible use of data/AI (where AI is used, indicate usage and validate results).

Study Course Theme Plan

FULL-TIME
Part 1

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Introduction to data processing, analysis and visualisation
Description
  • The meaning, use of the data
  • Data breakdowns and data sources
  • Data quality criteria and typical problems
  • Data analysis steps: question definition, data understanding, preparation, analysis, visualization, and conclusions

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Data mining and data introduction (Excel)
Description
  • Data import (CSV, XLSX), data type recognition (numbers, dates, text)
  • Principles of a structured table
  • Excel Table (Excel Table)
  • sort and filter data
  • Conditional Formatting

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Calculations and functions (Excel)
Description
  • Relative, absolute, and mixed references
  • Applying different functions in calculations
  • Building nested  functions
  • Dynamic array functions

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Calculations and functions (Excel)
Description
  • Relative, absolute, and mixed references
  • Applying different functions in calculations
  • Building nested  functions
  • Dynamic array functions

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Data Cleanup and Tansformation (Excel)
Description
  • Missing values, duplicates, inconsistencies; data validation
  • Cleaning up text/data formats; consistency of units
  • Splitting records into columns
  • Documenting transformations and ensuring reproducibility

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Descriptive statistics for data characterization and summaries (Excel)
Description
  • Data types and measurement scales (nominal, ordinal, interval, ratio) and their impact on the choice of metrics
  • Frequency tables, proportions, and group-wise summaries
  • Measures of central tendency and dispersion; quartiles and percentiles; distribution shape (introduction to skewed distributions)
  • Outliers
  • Basic charts for exploratory data analysis (histogram, box plot, bar chart, scatter plot)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Descriptive statistics for data characterization and summaries (Excel)
Description
  • Data types and measurement scales (nominal, ordinal, interval, ratio) and their impact on the choice of metrics
  • Frequency tables, proportions, and group-wise summaries
  • Measures of central tendency and dispersion; quartiles and percentiles; distribution shape (introduction to skewed distributions)
  • Outliers
  • Basic charts for exploratory data analysis (histogram, box plot, bar chart, scatter plot)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Data visualization: Choose, Create, and Design (Excel)
Description
  • Choosing the chart type based on the data type and the analytical question
  • Key elements: data series, axes and scale, title, legend, data labels, reference lines/annotations
  • Chart formatting and best practices: simplicity, readability, comparability, and use of color

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

PivotTables and PivotCharts (Excel)
Description
  • Create, group, sort, filter, “show value as”
  • PivotChart Create and format
  • Dynamic filters (slicers, timeline), summary interpretation

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

PivotTables and PivotCharts (Excel)
Description
  • Create, group, sort, filter, “show value as”
  • PivotChart Create and format
  • Dynamic filters (slicers, timeline), summary interpretation

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Practical workshop

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Introduction to Power BI and data preparation (Power BI)
Description
  • Power BI overview and workspace
  • Importing data and connecting to data sources
  • Power Query: data cleaning and transformation, documenting transformation steps
  • Data modeling: tables, keys, and relationships

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Visualization and DAX basics (Power BI)
Description
  • Creating and customizing basic visualizations
  • Calculated columns vs. measures
  • Basic measures: SUM, COUNT/DISTINCTCOUNT, AVERAGE; simple KPIs

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Power BI visualizations and interactive overview (Power BI)
Description
  • Visualization types and their suitability for the task
  • Filters and slicers
  • Usability and design: structure, hierarchy, consistent styling

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Tableau: visualizations and interactive overview
Description
  • Environment and action sequence (data source - worksheet - dashboard)
  • Create visualizations and main settings
  • Dashboard layout, interactivity, usability
  • publish restrictions, and data security (recent/anonymymised data only)

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Artificial intelligence in data preparation, analysis and visualisation
Description
  • Rules for using AI tools and data security in the course
  • AI as an assistant: typical use cases and limitations
  • Fundamentals of prompt writing and examples
  • Risks (hallucinations, incorrect calculations, data leakage) and validation of results
  • AI tools used (Julius AI, ChatGPT)

  1. Test

Modality
Location
Contact hours
On site
Auditorium
2

Topics

Presentation of final project work
Total ECTS (Creditpoints):
3.00
Contact hours:
32 Academic Hours
Final Examination:
Exam

Bibliography

Required Reading

1.

Greg H. G. (2019), Excel® 2019 all-in-one for dummies. Hoboken, New Jersey : John Wiley & Sons Komentārs: Atbilst kursa tēmām par Excel (2.-7.)Suitable for English stream

2.

Excel palīdzība un mācības

3.

Kusleika D.(2021) Data Visualization with Excel Dashboards and Reports. John Wiley & Sons, Inc. Komentāri: Atbilst tēmām par datu vizualizāciju un informācijas paneļiem.Suitable for English stream

4.

Hyman J.A. (2022) Microsoft Power BI for Dummies. Newark: Wiley Komentārs: Atbilst kursa tēmām par Power Bi (9.-11.)Suitable for English stream

5.

Gillet JC., Gupta S., Pinto S., Cherven S.S.K.M. (2022) Tableau Workshop Komentārs: Atbilst tēmai par datu vizualizācija TableauSuitable for English stream

Additional Reading

1.

ExceljetSuitable for English stream

2.

Datacamp. Materiāls iesācējiem par Power BiSuitable for English stream