School of Economics and Administrative Sciences \ International Entrepreneurship
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
This course aims to introduce the fundamental concepts of data science and analytics, equipping students with the ability to understand and apply data-driven decision-making processes. The course primarily focuses on data visualization techniques, aiming to facilitate the analysis and interpretation of large datasets. Within the scope of the course, topics such as data visualization, basic statistical analyses, data cleaning, modeling, the use of data in business, and decision-making processes will be covered. Rather than emphasizing artificial intelligence-based approaches, this course focuses on visualization and data analysis processes, aiming to enhance students' skills in effectively presenting and interpreting data.
Textbook and / or References
Hamilton, L. C. (2012). Statistics with Stata: Version 12. Cengage Learning.
Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press.
Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.
Schwabish, J. (2021). Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press.
1. To understand the fundamental principles of data science and analytics.
2. To learn the processes of data collection, cleaning, and analysis.
3. To create meaningful presentations using data visualization techniques.
1. The students can create effective charts and presentations using data visualization techniques.
2. The students can clean and analyze large datasets.
3. The students can evaluate data-driven decision-making processes.
4. The students can prepare data-driven reports and presentations by applying statistical analysis methods.
Week 1: Introduction to the Course
Week 2: Concepts of Data Science
Week 3: Data Collection and Cleaning Techniques
Week 4: Basic Statistical Analyses
Week 5: Data Visualization Techniques
Week 6: Data Analysis with Excel and Tableau
Week 7: Data Analysis with Stata (Introduction)
Week 8: Midterm Exam
Week 9: Exploration and Interpretation of Datasets
Week 10: Sample Applications: Use of Data in Business
Week 11: Data-Driven Decision-Making Processes
Week 12: Student Presentations
Tentative Assesment Methods
Midterm 40%
Participation 15%
Project and Presentations 45%
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