1. Fundamentals 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 Foundations
(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 visualization using charts and dashboards, identifying trends, correlation vs. causation, storytelling with data)
5. Advanced Modeling and Prediction
(Introduction to predictive modeling, 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, optimization and simulation models, time series analysis, text and sentiment analysis, data ethics and security)

1. Introduction to Managerial Data Analysis (Overview of the course and objectives, Basic concepts and the importance of data analysis for managers, Introduction to data analysis tools (e.g., Excel, R, Python))
2. Statistical Foundations 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 for working with Big Data (e.g., Hadoop, Spark), Basic operations and functions)
5. Exploratory Data Analysis (EDA) (Data visualisation for EDA, Identifying patterns and trends, Correlation and causality)
6. Predictive Modelling (Introduction to predictive modelling, Regression analysis, Classification models)
7. Machine Learning for Managers (Basic concepts and techniques of machine learning, Supervised vs. unsupervised learning, Applications of machine learning in business)
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 Analysis and Text Analytics (Introduction to text analytics, Sentiment analysis techniques, Applications of text analytics in business)
11. Data-Driven Decision Making (Data-driven decision-making process, 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 Presentation (Working on the final project, Project presentations in class, Feedback and evaluation)

Professional Knowledge:
Students will understand the role of data in the decision-making process.
Professional Skills:
Students will learn to use tools and technologies for data transformation and analysis.
Professional Competence:
Students will be able to comprehend and interpret data.