Business Intelligence

obligatory courses

Blended learning

The classes present Business Intelligence technology with particular emphasis on the use of the Microsoft Power BI system. BI technology is designed to support decision-making at the operational, but also strategic level. After introductory considerations (information society, ubiquity of data, Big Data, a short review of management information systems), the identification of BI technologies is proposed, as well as the explanation and presentation of such issues as: types of data sources, data warehouses, ETL operations, Power Query, Power Pivot, Power View, OLAP, basics of DAX notation, data mining. As part of the course, students have the opportunity to independently create data models, process data and visualize results in order to create management dashboards. In addition to providing theoretical knowledge, emphasis is placed on software operation practice and team project work.

Lecture:

The lecture offers theoretical foundations that will be useful when using BI systems. Topics:

1. Introduction to BI - a framework for considerations (The times of the information society, Ubiquity of data and Big Data, Review of management information systems)

2. The concept and architecture of BI systems

3. Characteristics of well- and poorly structured problems,

4. Characteristics of the Microsoft Power BI tool

5. Data sources for Business Intelligence

6. Data warehouses

7. ETL operations

8. Power Query

9. Data modeling

10. OLAP

11. Data mining

12. Visualization of results (Reporting and data visualization tools, Dashboards)

13. Applications and development directions of BI systems (BI in management, Business Intelligence policy goals in the organization, Organizing data management processes for BI purposes, BI applications, Development directions of BI systems)

Exercises:

The exercises cover issues related to the use of the Microsoft Power BI tool and the use of BI methods in business analyses. Topics:

1. Introduction to Microsoft Power BI (built-in tools, program interface, overview of possibilities),

2. Data preparation (Power Query, basic data preparation operations, data from different sources, incompatible tables, table decomposition, queries, M language, text analysis)

3. Data modeling (basics of data modeling, Power Pivot, star models, snowflake models, fact constellations, models with staging tables)

4. ETL operations in practice

5. Data visualization (reporting tools, creating dashboards using Power View)

6. Elements of the DAX language

7. Review of semestral projects

Literature:

- Microsoft Power BI. Jak modelować i wizualizować dane oraz budować narracje cyfrowe, praca zbiorowa, wyd. III. Wydawnictwo Helion Gliwice 2023.

- Jerzy Surma - Business Intelligence. Systemy wspierania decyzji biznesowych. Wydawnictwo Naukowe PWN, Warszawa 2009.

- Arkadiusz Januszewski – Funkcjonalność informatycznych systemów zarządzania. Tom 2. Systemy Business Intelligence. Wydawnictwo Naukowe PWN, Warszawa 2008.

Additional literature:

- Marco Russo, Alberto Ferrari - Kompletny przewodnik po DAX. Analiza biznesowa przy użyciu Microsoft Power BI, SQL Server Analysis Services i Excel, Wydawnictwo Helion Gliwice 2019.

- Gil Raviv - Power Query w Excelu i Power BI. Zbieranie i przekształcanie danych. Wydawnictwo Helion Gliwice 2020.

- Marco Russo, Alberto Ferrari – Power BI I Power Pivot dla Excela. Analiza danych. Wydawnictwo Helion Gliwice 2020.

Student after completing the course:

In terms of knowledge:

• Knows and understands terminology and basic theoretical models in the field of BI and related technologies (K_W01)

• Knows and understands complex processes and phenomena taking place in society, in the entire economy and in various types of organizations under the influence of technology development and data overload (K_W02, K_W05).

In terms of skills:

• Is able to independently and collectively prepare analyses, diagnoses and reports on complex and unusual problems using Business Intelligence technology (K_U03)

• Is able to participate in teamwork (K_U05)

• Has the ability to self-educate and improve acquired qualifications (K_U06)

In terms of attitudes:

• Is ready to assess and critically approach various economic and social phenomena thanks to the use of BI technology (K_K01)

Learning outcomes will be verified on an ongoing basis through tasks performed by participants during exercises, during a theoretical test and during semester-long project work.

There is no exam - uniform grade for lecture and exercises:

- Semester project (50% of the grade)

- Score from exercises (30% of the grade)

- Score obtained during the lecture (20% of the grade)