AI in data analysis 2600-ABdz1AIADf
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.
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Term 2025Z:
. Data analysis as an element of business analysis |
Type of course
Course coordinators
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
Methods: Interactive explanation of key concepts, practical examples, quizzes. The primary assessment criterion is the test score.
The final grade consists of two components: 80% of the grade is the test and 20% of the grade is participation in discussions and preparation for tutorials.
Rating scale: DST (51%-60%), DST Plus (61%-70%), DB (71%-80%), DB Plus (81%-90%), BDB (91%-100%).
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
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: