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: Strukture 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 uvajanja, najboljše poslovne prakse, definicija poslovne analitike in primeri uporabe, pregled razlik med poslovno inteligenco in poslovno analitiko, 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 definiranje poslovnih problemov, inteligentno analitično modeliranje za reševanje poslovnih/tržnih problemov, ovrednotenje in prenos rezultatov v poslovno prakso, pregled tipičnih poslovnih problemov.

Strategije trženja in neposredno trženje: Poslovne strategije, planiranje in razvoj strategij, strategije neposrednega trženja, poslovni modeli, analiza trženjskih 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,
trženje v realnem času.

Teorija iger in njena uporaba: Antagonistične igre s hkratnimi in zaporednimi potezami, Nashevo ravnovesje in kako ga najti, 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 problemov, na katere naletimo pri večjih projektih.

Uporaba generativne umetne inteligence v BI.

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

Scientific Method: Scientific knowledge structures, scientific activities/processes.

Introduction: Definition of intelligence and business intelligence (BI), basic BI schema, criteria, reasons and areas for adoption, common problems and pitfalls, best practices, definition of business analytics (BA) and some use-cases, review of differences among BI and BA, best practices.

Data handling: Data warehousing, data quality, data preparation/enhancement, data migration, data mediation, examples of major pitfalls.

Predictive business analytics: Business problem detection, analysis, and definition, analytical modeling for solving business/marketing problems, evaluation and business adoption of modeling results, overview of various industry.

Marketing strategies and direct marketing: Business strategies, strategy planning and
development, direct marketing strategies (product, offer, media, distribution and creative
strategies), business models, analysis of marketing opportunities and environment. Customer/market analysis and research, contact
strategies, marketing channels, integration aspects, creative tactics, content personalization,
response tracking, marketing performance management, event-driven marketing, real-time marketing, best practice examples in various
industries.

Game theory and its applications: Simultaneous-move (static) and sequential-move (dynamic) non-cooperative games, Nash equilibrium and how to find it, pure and mixed strategies. Business applications: bargaining, auctions, negotiations.

Challenges in software engineering and project
implementation: A detailed overview of development of software project with the emphasis on understanding problems that are specific to big software projects.

Applications of generative AI in BI.

Tools and Solutions: Overview of best-of-breed BI/CI tools and solutions in the marketplace, insight into emerging technologies.

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
R. Sharda, D. Delen, E. Turban. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, ISBN-10 0134633288, 2021.
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, ISBN 978-3-319-97435-4, 2019.
T. Zwingmann. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Lear. 1st Ed., O'Reilly Media, ISBN 2022, 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

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. M. Gams, Ž. Kolar, Z. Vuk. C. Samuelsson, B. Jäger, E. Dovgan. Similarities and Differences between EU Platforms in the AHA and AAL Domains from a Software Viewpoint. DOI, Healthcare 2022, 10(2). 10.3390/healthcare10020401,
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.