Faculty of Business and Management Sciences · International Trade and Finance · 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.
Required Resources
Altunışık, Remzi, Recai Coşkun, Serkan Bayraktaroğlu ve Engin Yıldırım. Sosyal Bilimlerde Araştırma Yöntemleri: SPSS Uygulamalı. 9. Baskı. Sakarya: Sakarya Yayıncılık, 2020.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2017.
Recommended Resources
Wasserman, Larry. All of Statistics: A Concise Course in Statistical Inference. 2nd ed. New York: Springer, 2010.
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.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 14 | 3 | 42 |
| Midterm | 1 | 8 | 8 |
| Quiz | 0 | 0 | 0 |
| Assignment | 12 | 2 | 24 |
| Practice | 1 | 8 | 8 |
| Final | 0 | 0 | 0 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to Data Analysis: Basic Concepts and Data Types | Basic reading, research, analysis |
| 2 | Data Collection Methods and Data Cleaning | Basic reading, research, analysis. |
| 3 | Descriptive Statistics: Mean, Median, Mode, Distribution | Basic reading, research, analysis. |
| 4 | Data Visualization Techniques: Histograms, Bar Charts, Box Plots | Basic reading, research, analysis. |
| 5 | Probability Theory and Probability Distributions | Basic reading, research, analysis. |
| 6 | Hypothesis Testing: Z-Test, T-Test, Chi-Square Test | Basic reading, research, analysis. |
| 7 | Regression Analysis: Linear and Logistic Regression | Basic reading, research, analysis. |
| 8 | Midterm Exam | - |
| 9 | Data Mining Techniques and Applications | Basic reading, research, analysis. |
| 10 | Fundamentals of Machine Learning: Supervised and Unsupervised Learning | Basic reading, research, analysis. |
| 11 | Big Data and Big Data Analytics | Basic reading, research, analysis. |
| 12 | Time Series Analysis and Forecasting | Basic reading, research, analysis. |
| 13 | Text Mining and Natural Language Processing (NLP) | Basic reading, research, analysis. |
| 14 | Data Ethics and Data Privacy | Basic reading, research, analysis. |
| 15 | Current Trends and Future Perspectives in Data Analysis | Basic reading, research, analysis. |
| 16 | Final Exam | - |