Učni načrt predmeta

Predmet:
Poslovna inteligenca II
Course:
Business Inteligence II
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja Inteligentni sistemi in robotika 1 1
Information and Communication Technologies, 3rd cycle Intelligent Systems and Robotics 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-629
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Matjaž Gams
Sodelavci / Lecturers:
dr. Aleksander Pivk
Jeziki / Languages:
Predavanja / Lectures:
slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second-cycle studies in information or communication technologies or completed second-cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:
Content (Syllabus outline):

Znanstvena metoda: Struktura znanstvenega védenja, znanstvene aktivnosti in procesi.

Uvod: Definicija inteligence in poslovne inteligence (BI), osnovna shema BI, kriteriji, razlogi in področja za uvajanje, problemi in pasti pri uvajanju, najboljše poslovne prakse. Definicija poslovne analitike in primeri uporabe, pregled razlik med poslovno inteligenco in poslovno analitiko ter primeri iz prakse.

Upravljanje s podatki: Podatkovna skladišča, kakovost podatkov, priprava in oplemenitenje podatkov, migracija podatkov, posredovanje podatkov. Primeri največjih nevarnosti in napak.

Poslovna analitika: Odkrivanje, analiza in opredelitev poslovnih problemov, inteligentno analitično modeliranje za reševanje poslovnih/tržnih problemov, ovrednotenje rezultatov in njihov prenos v poslovno prakso. Pregled tipičnih poslovnih problemov.

Strategije trženja in neposredno trženje: Poslovne strategije, planiranje in razvoj strategij, strategije neposrednega trženja, poslovni modeli, analiza tržnih priložnosti in okolja. Analiza trga in strank, kontaktne strategije, tržni kanali, problemi integracije, personalizacija tržnih vsebin, spremljanje aktivnosti strank, upravljanje tržne učinkovitosti, trženje na osnovi dogodkov in trženje v realnem času.

Teorija iger in njena uporaba: Antagonistične igre s hkratnimi in zaporednimi potezami, Nashevo ravnovesje in njegovo iskanje, čiste in mešane strategije. Poslovna uporaba: barantanje, dražbe, pogajanja. Računalniška simulacija.

Izzivi pri razvoju programskih sistemov in implementacija projektov: Predstavitev celotnega procesa razvoja programskih projektov s poudarkom na reševanju težav, s katerimi se soočamo pri večjih projektih.

Uporaba generativne umetne inteligence v BI: Generativna umetna inteligenca (AI), kot so napredni jezikovni modeli (npr. GPT), prinaša nove možnosti za avtomatizacijo procesov analize podatkov, napovedovanja trendov in ustvarjanja poslovnih poročil. Uporaba generativne AI omogoča hitrejšo in bolj personalizirano analizo velikih količin podatkov ter kreiranje scenarijev, ki temeljijo na zgodovinskih podatkih. Poleg tega lahko sistemi generativne inteligence podpirajo poslovne odločitve z izdelavo več možnosti rešitev in predvidevanj, kar izboljšuje natančnost pri strateškem odločanju.

Orodja in rešitve: Pregled najboljših orodij in rešitev na trgu za BI/CI ter vpogled v prihajajoče tehnologije.

Scientific method: Structure of scientific knowledge, scientific activities, and processes.

Introduction: Definition of intelligence and business intelligence (BI), basic BI schema, criteria, reasons and areas for implementation, problems and pitfalls during implementation, and best business practices. Definition of business analytics and examples of its application, an overview of the differences between business intelligence and business analytics, along with real-world examples.

Data management: Data warehouses, data quality, data preparation and enrichment, data migration, data delivery. Examples of major risks and errors.

Business analytics: Identification, analysis, and definition of business problems, intelligent analytical modeling to solve business/market problems, evaluation of results and their transfer into business practice. Overview of typical business problems.

Marketing strategies and direct marketing: Business strategies, planning and development of strategies, direct marketing strategies, business models, analysis of marketing opportunities and environment. Market and customer analysis, contact strategies, marketing channels, integration challenges, personalization of marketing content, monitoring customer activities, managing marketing effectiveness, event-based marketing, real-time marketing.

Game theory and its application: Antagonistic games with simultaneous and sequential moves, Nash equilibrium and how to find it, pure and mixed strategies. Business applications: bargaining, auctions, negotiations. Computer simulation.

Challenges in software system development and project implementation: Presentation of the entire software project development process, with an emphasis on solving problems encountered in larger projects.

Use of generative artificial intelligence in BI: Generative artificial intelligence (AI), such as advanced language models (e.g., GPT), brings new opportunities for automating data analysis processes, predicting trends, and generating business reports. The use of generative AI enables faster and more personalized analysis of large datasets and the creation of scenarios based on historical data. Additionally, generative AI systems can support business decisions by offering multiple solution options and predictions, improving the accuracy of strategic decision-making.

Tools and solutions: Overview of the best tools and solutions available on the BI/CI market, as well as a glimpse into upcoming technologies.

Temeljna literatura in viri / Readings:

R. Sharda, D. Delen, E. Turban. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition. Pearson, 2021. ISBN-10 0134633288.
S. Russel, and P. Norvig. Artificial Intelligence: A Modern Approach, 4th Edition. Pearson Education Limited, 2021. ISBN-13 978-0-13-461099-3.
R. Hurley. Business Intelligence: The Ultimate Guide to BI, Artificial Intelligence, Machine Learning, Big Data, Cybersecurity, Data Science, and Predictive Analytics. ISBN-10 1659796954, 2020.
R. Akerkar. Artificial Intelligence for Business. Springer, 2019. ISBN 978-3-319-97435-4.
T. Zwingmann. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning, 1st Ed. O'Reilly Media, 2022. ISBN 978-1098111472.
D.L. Poole, and A.K. Mackworth. Artificial Intelligence: Foundations of Computational Agents. 3rd edition, Cambridge University Press, 2023. Online.
N. Bostrom. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2016. ISBN 978-0198739838.
G. S. Miñan Olivos, J. A. Estrada Espinoza, A. A. Cruz Aguilar, J. A. Moreno Ramos, and C. B. Cisneros Hilario. Business Intelligence as a Competitive Advantage in Organizations: A Systematic Review of the Literature Between 2012-2022. Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development”, 2023.
M. Hanqing Hu, and L. Jianling. Evolution of Business Intelligence: An Analysis from the Perspective of Social Network. Technical Gazette, 2022. .

Cilji in kompetence:
Objectives and competences:

Cilj predmeta je podati splošno in napredno znanje o poslovni inteligenci in poslovni analitiki, nadgrajenim z znanjem in potrebami strateškega (marketinškega) odločanja. Uvodoma so predstavljeni temelji področja poslovne inteligence in poslovne analitike, cilji, namen in ključni problemi vpeljave le-teh ter najboljše prakse.

Študenti, ki bodo uspešno končali ta predmet, bodo obvladali osnove in nadgradnjo poslovne inteligence in bodo usposobljeni za uporabo tovrstnih metod in algoritmov v reševanju zahtevnih poslovnih aplikacij in vrednotenje njihovih rezultatov.

The goal of the course is to provide general and advanced knowledge of business intelligence and business analytics extended with the knowledge and skills for strategic (marketing) decision-making. Firstly, the business intelligence and business analytics grounds will be presented, followed by the goals, objectives, and common problems of their adoption. Strong focus is given to best practices.

The students who will successfully complete this course will master the basics and some advanced areas of business intelligence and will be capable of applying these methods in solving demanding business problems and evaluating their results.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- osnove znanstvenega pristopa v poslovanju,
- osnovna znanja poslovanja v realnem svetu,
- pregled obstoječih nalog in metod poslovne inteligence,
- obvladovanje tehničnih in poslovnih vidikov metod poslovne inteligence,
- sposobnost uporabe obstoječih metod strojnega učenja na novih problemih poslovanja,
- sposobnost ugotavljanja uspešnosti metod strojnega učenja ali rudarjenja podatkov pri uporabi na konkretni nalogi poslovne inteligence,
- napredna znanja iz nekaterih poglavij poslovne inteligence,
- sposobnost samostojnega reševanja poslovnih odločitev in izdelave analiz.

Students successfully completing this course will acquire:
- Basic scientific approach in business intelligence.
- Basic BI knowledge in real world.
- Overview of existing tasks and methods in BI.
- Mastering technical and business aspects of business intelligence.
- The ability to apply existing ML methods to BI problems.
- The ability to identify whether ML or DM methods are successful given domain.
- Advanced knowledge about specific areas of business intelligence.
- An ability to perform advanced business decisions and business analyses.

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo.

Lectures, seminar, consultations, individual work.

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga
80 %
Seminar work
Ustni zagovor
20 %
Oral defense
Reference nosilca / Lecturer's references:
1. M. Gams, I. Yu-Hua Gu, A. Harma, A. Munos, V. Tam. Artificial intelligence and ambient intelligence. Journal of ambient intelligence and smart environments, ISSN 1876-1364, 2019, vol. 11, no. 1, str. 71-86, doi: 10.3233/AIS-180508.
2. M. Gams, T. Kolenik. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules, Electronics 2021, 10(4), MDPI, DOI 10.3390/electronics10040514
3. M. Shulajkovska, M. Smerkol, E. Dovgan, M. Gams. A machine-learning approach to a mobility policy proposal, Heliyon 9, 2023, DOI 10.1016/j.heliyon.2023.e20393
4. Shulajkovska, Miljana, Smerkol, Maj, Noveski, Gjorgji, Bohanec, Marko, and Gams, Matjaž. Artificial intelligence-based decision support system for sustainable urban mobility. Electronics, 2024, vol. 13, no. 18, 14 pages. ISSN 2079-9292. DOI: 10.3390/electronics13183655.
5. V. Janko, A. Vodopija, D. Susič, C. De Masi, T. Tušar, A. Gradišek, S. Vandepitte, D. De Smedt, J. Javornik, M. Gams, M. Luštrek. Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence, Frontiers 2023. C. 11, DOI 10.3389/fpubh.2023.1073581.