AI in data analysis 2600-ABdzAIADkf

1. Data analysis as an element of business analysis
• Purpose and scope of business analysis
• Defining the business problem
• Modelling business reality
2. Data in the organization
• Definitions, types of data, data sources
• Data management in the organization
3. Working with data
• Statistics and probability
• The process of data exploration and analysis
• Pitfalls of working with data
4. AI technologies
• Definitions and approaches to artificial intelligence
• Machine learning, deep learning
• Algorithms used by AI (supervised, unsupervised, reinforcement)
• Agents for data analysis
• Building AI models – MLOps
5. AI in data analysis across various business areas (marketing and trade, finance and banking, health and medicine, logistics, manufacturing and others)
6. Ethics, transparency, and risks in data analysis using AI
7. Delivering data analysis results – reporting and visualization as a means of formulating responses to identified business challenges.

Term 2025Z:

The foundations of artificial intelligence and its place in business
Block 1: History and Basic Concepts of AI
• The origin of artificial intelligence : Alan Turing, The Turing Test , Dartmouth Conference.
• Main stages of development: expert systems, machine learning, deep learning.
• Basic concepts: algorithm, model, training data, neural network.
• Differences between AI, ML, DL, Generative AI.
Block 2: Key AI Technologies
• Machine learning – supervised and unsupervised learning.
• Deep learning – multi-layer neural networks.
• Natural Language Processing – language analysis and generation.
• Generative AI – models that create content (text, images, sounds, videos).
Block 3: AI Application Areas in Business
• Marketing and sales – recommendations, predictions, personalization.
• Finance – credit scoring , fraud detection.
• HR – recruitment and competency analysis.
• Logistics – demand forecasting, route optimization.
• Customer service – chatbots , voicebots .
Block 4: AI and the labor market and business
• AI as support vs AI as threat.
• The concept of augmented intelligence .
• Changing employee competencies in the AI era.
AI in Marketing and Customer Experience
Block 1: Recommendation algorithms
• Mechanisms recommendations : collaborative filtering, content-based filtering.
• Examples: Amazon, Netflix , Spotify .
• The impact of recommendations on sales and customer loyalty.
Block 2: Personalization and Dynamic Pricing
• Content personalization: mailing, websites, online advertising.
• Dynamic pricing – operating logic, examples (Uber, airlines).
• Challenges and controversies of dynamic pricing .
Block 3: Communication automation – chatbots and voicebots
• Role in customer service – 24/7 availability, cost reduction.
• NLP technologies in practice.
• Advantages and limitations of chatbots in customer communication.
Block 4: AI in marketing campaigns
• Programmatic advertising – automation of media buying.
• Predictive targeting – predicting customer behavior .
• Automatic optimization of advertising campaigns.
Data and machine learning in business practice
Block 1: The role of data in artificial intelligence
• Data sources in enterprises (CRM, e-commerce, social media).
• Big data vs smart data.
• Data quality as a condition for the effectiveness of AI models.
Block 2: Machine learning mechanisms
• Supervised learning: classification, regression.
• Unsupervised learning: clustering , segmentation.
• workflow : data → training → model → evaluation.
Block 3: Customer Segmentation and Predictive Analytics
• Demographic segmentation vs behavioral segmentation.
• churn and value prediction ( Customer Lifetime Value).
• Sentiment analysis in social media.
Block 4: Application Case Studies
• E-commerce: recommendations and personalization (Amazon, Allegro).
• Banking : fraud detection, credit scoring .
• Retail and FMCG: demand forecasting, inventory management.

Regulation, Ethics, and the Future of AI in Business
Block 1: Legal regulations regarding AI
• EU AI Act – risk assessment system.
• GDPR and restrictions on customer profiling.
• Copyright and intellectual property in the context of AI.
• Regulations in the US and Asia – differences in approaches.
Block 2: Ethical Challenges of AI
• Bias and discrimination in algorithms.
• Transparency and accountability of AI systems.
• Deepfake , fake news and their impact on business and society.
Block 3: Business threats and risks
• Risk of incorrect forecasts and decisions.
• Cybersecurity and attacks on AI models.
• Reputational risks associated with the use of AI in marketing.
Block 4: The Future of AI in Business
• AI as a tool supporting creativity.
• Trend: AI agents and autonomous decision-making systems.
• AI in the concept of Marketing 5.0, 6.0 ( Kotler et al.).
• Directions of further development and implications for company strategies.

Term 2026Z:

Course coordinators

Aneta Biernikowicz

Type of course

elective courses

Learning outcomes

K_W05: The student knows and understands complex technological, social, legal, economic, ethical, and ecological processes and phenomena related to the use of numerical data and their impact on the functioning of the organization and the entire economy.
K_U01. The student is able to apply the theory of management and quality science, particularly in the field of numerical data analysis, to recognize, diagnose, and solve complex and unusual problems related to key functions within the organization, including inference, strategy development, and business decision-making.
K_U03. The student is able to adapt existing data analysis methods and tools or propose new ones based on them, using advanced information and communication techniques and the appropriate selection of sources to recognize, diagnose, and solve problems related to data analysis in the internal and external environments of the organization.
K_U08. The student is able to lead and take a leading role in team activities and collaborate within teams to use data to solve business problems.

Assessment criteria

Metody: Interaktywne wyjaśnienie kluczowych pojęć, przykłady z praktyki, quizy. Podstawowym kryterium oceny jest ocena z testu.
Ostateczna ocena składa się z dwóch elementów: 80% oceny stanowi test, a 20% oceny to udział w dyskusjach i przygotowanie do seminariów.
Do uzyskania zaliczenia przedmiotu należy uzyskać minimum 51% z kolokwium.
Skala ocen: DST (51%-60%), DST Plus (61%-70%), DB (71%-80%), DB Plus (81%-90%), BDB (91%-100%)

Practical placement

Bibliography

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th Edition). Pearson
Foster Provost F., Fawcett T., (2023), Analiza danych w biznesie. Sztuka podejmowania skutecznych decyzji, Helion
Gutman A., Goldmeier J., (2023), Analityk danych.Przewodnik po data science, statystyce i uczeniu maszynowym, Helion
Żyżyński J. (2019), Podstawy metod wnioskowania statystycznego dla zarządzania, Wydawnictwa Uniwersytetu Warszawskiego

Term 2025Z:

Basic literature
1. Russell, S., Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
2. Marr, B. (2021). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems . Wiley .
3. Kotler , P., Kartajaya , H., Setiawan , I. (2021). Marketing 5.0: Technology for Humanity . Wiley.
4. Mazurek, G. (2019). Digital transformation – marketing, business, consumers . Poltext .
Additional literature
1. Chaffey, D., Ellis-Chadwick, F. (2022). Digital Marketing (8th ed.). Pearson.
2. Brynjolfsson, E., McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future . W. W. Norton & Company.
3. McKinsey Global Institute (2022). The State of AI in 2022 . Industry report.
4. Błażewicz, J., Perek, Ł., Szczęch, A. (2022). Artificial intelligence in business and management [in:] E-mentor , no. 2(94), SGH

Term 2026Z: