Přístupnostní navigace
E-application
Search Search Close
Course detail
FP-BDAAcad. year: 2026/2027
The course Data Analysis focuses on providing both basic and advanced knowledge and skills in data analysis essential for effective managerial decision-making. Students will become familiar with various tools and techniques for data collection, preparation, analysis, and interpretation. Emphasis is placed on practical applications in a managerial environment, including the use of software tools such as Excel, R, and Python. The course covers topics such as an introduction to managerial data analysis, statistical foundations, data collection and preparation, working with big data, exploratory data analysis, predictive modelling, machine learning, optimisation and decision models, time series analysis, sentiment analysis and text analytics, data-driven decision making, data ethics and security, and a final project and presentation.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
To successfully complete the course Data Analysis, the following prerequisites are expected: basic knowledge of statistics and probability, basic knowledge of working with spreadsheets (e.g., Excel), and basic programming knowledge (advantageous but not required).
Rules for evaluation and completion of the course
Credit Requirements:* Mid-semester test: Completion of a practical assignment according to the instructions (40 points). The minimum score is 20.* Project: One project based on the assignment with the required documentation (60 points). The minimum score is 30. (The assignment is introduced during the third lecture.)* Plagiarism or unauthorised collaboration on projects or tests will result in failing to obtain the credit and may lead to disciplinary proceedings.* During the semester, the student must obtain at least 50% of the points, i.e. 50 out of 100.Exam:* Completion of a practical example and an oral examination.* (No points from the credit are transferred to the exam.)* The exam grading follows the ECTS classification.Course Completion for Students with an Individual Study Plan (ISP):* Credit: Completion of a project based on the assignment with the required documentation. (The assignment is introduced during the third lecture and is available in e‑learning. Minimum of 50 points out of 100.)* Exam: Oral examination. The exam grading follows the ECTS classification.* For ISP students, the conditions are identical, except for potential mandatory participation in classes.* The dates for course completion are arranged individually according to the conditions approved in the ISP.
Aims
Upon completing the course Data Analysis, students will be able to understand basic concepts and the importance of data analysis for managerial decision-making, apply statistical methods and techniques for data analysis, effectively collect, clean, and prepare data for analysis, use tools and technologies for working with big data, conduct exploratory data analysis and visualize results, create and interpret predictive models, apply machine learning techniques in a managerial context, use optimization and decision models for managerial tasks, analyze time series and make forecasts, perform sentiment analysis and text analytics, understand the data-driven decision-making process and create dashboards, adhere to ethical principles and security measures when working with data, and present data analysis results and defend the final project.
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans
Lecture
Teacher / Lecturer
Syllabus
1. Foundations of Managerial Data Analysis(Introduction to the course and its objectives, the importance of data analysis in managerial practice, key concepts, overview of tools such as Excel, R, Python, data access and its role in decision‑making.)2. Statistical and Analytical Fundamentals(Descriptive statistics, probability distributions, basic statistical tests, interpretation of results, working with confidence intervals and p-values.)3. Data Preparation and Management(Data sources and acquisition, data cleaning and transformation, handling missing values, data types and formats, basic principles of data quality.)4. Exploratory and Visual Data Analysis(Exploratory Data Analysis – EDA, data visualisation using charts and dashboards, identification of trends, correlation vs. causation, data storytelling.)5. Advanced Modelling and Prediction(Introduction to predictive modelling, linear and logistic regression, classification models, basics of machine learning, supervised vs. unsupervised learning, business applications.)6. Decision-Making, Ethics and Practical Applications(Data-driven decision making, KPIs and performance metrics, optimisation and simulation models, time series analysis, text and sentiment analysis, data ethics and security.)
Exercise
1. Introduction to Managerial Data Analysis(Course overview and objectives, key concepts and the importance of data analysis for managers, introduction to data analysis tools such as Excel, R, Python.)2. Statistical Essentials for Data Analysis(Descriptive statistics, probability and distributions, basic statistical tests.)3. Data Collection and Preparation for Analysis(Data sources and acquisition, data cleaning and transformation, handling missing values.)4. Working with Big Data(Introduction to Big Data, tools and technologies such as Hadoop and Spark, basic operations and functions.)5. Exploratory Data Analysis (EDA)(Visualisation for EDA, identifying patterns and trends, correlation and causation.)6. Predictive Modelling(Introduction to predictive modelling, regression analysis, classification models.)7. Machine Learning for Managers(Key concepts and techniques of machine learning, supervised vs. unsupervised learning, business applications of ML.)8. Optimisation and Decision Models(Linear programming, simulation models, applications of optimisation techniques.)9. Time Series Analysis(Introduction to time series, smoothing and decomposition, time series forecasting.)10. Sentiment and Text Analysis(Introduction to text analytics, sentiment analysis techniques, business applications of text analysis.)11. Data-Driven Decision Making(Decision-making processes based on data, KPIs and performance metrics, dashboards for managers.)12. Data Ethics and Security(Ethical aspects of data analysis, personal data protection, security measures.)13. Final Project and Presentations(Work on the final project, project presentations, feedback and evaluation.)
Professional KnowledgeStudents will understand the role of data in the decision-making process.Professional CompetenciesStudents will be able to understand and interpret data.
Professional SkillsStudents will learn to use tools and technologies for data transformation and analysis.
Self-study
Individual preparation for an ending of the course