Faculty of Business and Management Sciences · Economics (English) · Undergraduate
Course Objective
To teach students about data preprocessing, data analysis techniques such as classification, clustering, associatin analysis.
Course Content
This course covers data analysis processes, statistical methods, and data mining techniques. Students will develop skills in data collection, cleaning, visualization, and analysis using SPSS. The course includes topics such as descriptive statistics, hypothesis testing (t-test, ANOVA, chi-square), regression analysis, factor analysis, and time series analysis. Additionally, students will learn to interpret, report, and apply data analysis results in decision-making processes. Throughout the course, hands-on SPSS applications will be conducted to reinforce analytical skills.
Course Learning Outcomes
- Students will be able to understand and apply data analysis processes and basic statistical concepts.
- Students will be able to interpret large datasets using data cleaning, visualization, and analysis techniques.
- Students will be able to apply regression analysis, hypothesis testing, and data mining techniques.
- Students will be able to develop data-driven decision-making processes using machine learning and big data analytics.
- Students will be able to conduct analysis in compliance with data privacy, ethical principles, and sustainable data policies.