Učni načrt predmeta

Predmet:
Poslovna inteligenca I
Course:
Business Inteligence I
Študijski program in stopnja /
Study programme and level		 Študijska smer /
Study field		 Letnik /
Academic year		 Semester /
Semester
Informacijske in komunikacijske tehnologije, 2. stopnja Inteligentni sistemi in robotika 1 2
Information and Communication Technologies, 2nd cycle Intelligent Sytems and Robotics 1 2
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT2-619
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 študijski program prve stopnje s področja naravoslovja, tehnike ali računalništva.

Student must complete first-cycle study programmes in natural sciences, technical disciplines or computer science.

Vsebina:
Content (Syllabus outline):

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

Uvod: Definicija poslovne inteligence (BI), definicija upravljanja s strankami (CI), arhitektura BI/CI za podjetja.

Osnove trženja: Poslovno informiranje, odločanje, strategije, planiranje in razvoj strategij, strategije neposrednega in posrednega trženja (strategije izdelkov, ponudbe, medijev, distribucije), poslovni modeli, analiza trženjskih priložnosti in okolja.

Orodja za delo s podatki: Preglednice. Podatkovne baze. Druga orodja. Primeri, osnovna in napredna praktična uporaba. Podatkovna skladišča, kakovost podatkov, priprava in oplemenitenje podatkov, migracija podatkov, posredovanje podatkov.

Poslovna analitika: Definiranje in analiza poslovnih problemov, inteligentno analitično modeliranje (kvalitativno/kvantitativno modeliranje, metrike, profiliranje, opredeljevanje strank) za reševanje poslovnih/tržnih problemov,
ovrednotenje in prenos rezultatov v poslovno prakso, pregled tipičnih poslovnih problemov, kot so kreditno tveganje, napovedovanje prekinitve poslovnih odnosov strank, zadrževanje strank, napovedovanje prodajnih možnosti, odkrivanje poneverb itd.

Avtomatizacija trženja: 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, primeri iz različnih industrij (bančništvo, telekomunikacije, maloprodaja, zavarovalništvo, proizvodnja), etični in pravni vidiki.

Izzivi pri razvoju programskih sistemov in
implementacija projektov: Predstavitev celotnega procesa razvoja programskih projektov.

Orodja in rešitve: Pregled najboljših orodij in rešitev na trgu za BI/CI. Uporaba AgI za poslovanje.

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

Introduction: Definition of business intelligence (BI), definition of customer intelligence (CI), enterprise BI/CI architectures.

Basic marketing approaches: Business informing, decision making, strategies, strategy planning and development, direct and indirect marketing strategies (product, offer, media, distribution and creative strategies), business models, analysis of marketing opportunities and environment.

Basic business software tools: Data handling tools: Spreadsheets. Databases. Other tools.
Examples, practical use at basic and advanced
level. Data warehousing, data quality, data
preparation/enhancement, data migration, data
mediation.

Predictive business analytics: Business problem definition, analysis, analytical modeling (descriptive/predictive modeling, metrics, customer centric profiling, customer scoring) for solving business/marketing problems, evaluation and business adoption of modeling results, overview of various industry examples such as credit scoring, risk scoring, churn prediction, customer retention, cross/upsell, fraud detection etc.

Marketing automation: 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 (banking,
telecommunications, retail, insurance, and manufacturing), ethics and legal aspects.

Challenges in software engineering and project
implementation: A detailed overview of development of software project.

Tools and Solutions: Overview of best-of-breed BI/CI tools and solutions in the marketplace. Use of AgI for BI.

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.

Cilji in kompetence:
Objectives and competences:

Cilj predmeta je podati osnovno znanje o poslovni inteligenci in o strateškem marketinškem odločanju. Uvodoma so predstavljeni temelji področja poslovne inteligence, nato pa so obdelane poslovne in tržne strategije, njihovo načrtovanje in razvoj ter možnosti in načini prenosa v prakso.
Cilj je tudi naučiti študente uporabljati osnovna poslovna orodja, kot so preglednice in podatkovne baze.
Osredotočili se bomo na pripravo in migracijo podatkov ter reševanje tipičnih problemov in nevarnosti, katerim se poskušamo izogniti.

V naslednjem sklopu je glavni cilj zagotoviti poglobljeno razumevanje narave in obsega tržne analize in njene vloge pri strateškem marketingu. Ta obsega analizo poslovnih problemov in priložnosti, odkrivanje nezadoščenih potreb strank, odkrivanje konkurenčne prednosti in napovedovanje vedenjskih vzorcev strank, kar omogoča organizaciji proaktivno nastopanje na trgu. V ta namen si bomo podrobno ogledali primerne analitične metode za posamezne probleme in proces vpeljevanja v prakso, kjer bodo nakazane tudi težave, ki se tipično pojavljajo.

The goal of the course is to provide basic knowledge of business intelligence and extend it with the knowledge and skills for strategic marketing decision-making. Firstly, the business and customer intelligence areas will be presented. Afterwards, we will focus on business and marketing strategies, their planning, development and practical application.
Students should get acquainted with basic business tools like spreadsheets and databases.
Multiple data sources, data quality and integration of data into data warehouses are the first major obstacle when implementing business intelligence solutions.
We will focus on preparation and integration of data, how to resolve possible problems and typical pitfalls that we want to avoid.

The main objective of the next part is to provide a deeper understanding of the nature and scope of marketing analysis and its role in strategic marketing. This includes investigating product-market opportunities, discovering unmet consumer needs, determining competitive advantage and forecasting customer behavior patterns so as to be proactive in the marketplace. Therefore, we will cover in detail problem-specific analytical methods and how they are applied in practice. The problems arising during this phase will also be indicated.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- Sposobnost analize, sinteze in predvidevanja rešitev ter posledic.
- Obvladanje raziskovalnih metod, postopkov in procesov, razvoj kritične in samokritične presoje.
- Sposobnost uporabe znanja v praksi.
- Avtonomnost v strokovnem delu.
- Razvoj komunikacijskih sposobnosti in spretnosti, posebej komunikacije v mednarodnem okolju.
- Etična refleksija in zavezanost profesionalni etiki.
- Kooperativnost, delo v skupini (in v mednarodnem okolju).
- Poznavanje področja poslovne inteligence in upravljanja s strankami.
- Poznavanje tržnih strategij, planiranja in razvoja strategij za boljše poslovno odločanje.
- Poznavanje upravljanja s podatki za potrebe poslovne inteligence.
- Poznavanje analitičnih metod primernih za poslovno uporabo ter njihove uporabe v realnih situacijah.
- Poznavanje strategij neposrednega trženja, upravljanja in analize učinkovitosti, taktikah kontaktiranja, etiki in pravnih vidikih.
- Poznavanje teorije iger.
- Zmožnost predstavitve poslovnih situacij z vidika teorije iger.
- Poznavanje razvoja in vodenja softverskih projektov.
- Poznavanje tržnih orodij in rešitev za BI.

Students successfully completing this course will acquire:
- An ability to analyse, synthesise and anticipate solutions and consequences.
- To gain the mastery over research methods, procedures and processes, a development of the critical judgement.
- An ability to apply the theory in to a practice.
- An autonomy in the professional work.
- Communicational-skills development; particularly in international environment.
- Ethical reflexion and obligation to a professional ethics.
- Cooperativity, team work (in international environment).
- Knowledge of business intelligence and customer intelligence area.
- Knowledge of marketing strategies, strategy planning and development for strategic decision- making.
- Knowledge of data handling issues for BI purposes.
- Knowledge of predictive modeling for business purposes and its real-life applicability.
- Knowledge of direct marketing strategies, marketing performance management, creative and contact tactics, ethics and legal aspects.
- Knowledge of game theory.
- The ability to view business situations in game- theoretic terms.
- Knowledge of design and management of software projects.
- Awareness of BI solutions in the marketplace.

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
50 %
Seminar work
Ustni zagovor seminarske naloge
50 %
Oral defense of seminar work
Reference nosilca / Lecturer's references:
1. 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
2. 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.
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. J. Zupančič, B. Filipič, M. Gams. Genetic-programming-based multi-objective optimization of strategies for home energy-management systems. Energy, ISSN 0360-5442, 2020, vol. 203, str. 117769-1-117769-15, doi: 10.1016/j.energy.2020.117769.
5. T. Kompara, J. Perš, D. Susič, M. Gams. A one-dimensional non-intrusive and privacy-preserving identification system for households. Electronics, ISSN 2079-9292, 2021, vol. 10, no. 5, str. 559-1- 559-21, doi: 10.3390/electronics10050559.