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Information
| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMICS (MASTER) (WITH THESIS) | |
| Code | İKT710 |
| Name | Data Science in Applied Economics |
| Term | 2026-2027 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. CENGİZ AYTUN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to provide economics students with the competence to solve economic problems using data mining, machine learning and big data analytics tools. Students are expected to clean and analyse real-world data using programming languages such as Python or R and translate the results into economic policy recommendations.
Course Content
Introduction to Economics and Data Science, Data Science Programming Tools (R/Python), Data Manipulation and Cleaning (Data Wrangling), Exploratory Data Analysis and Visualisation, Machine Learning Approach in Regression Models (Lasso, Ridge, Elastic Net), Classification Algorithms (Logistic Regression, KNN, Decision Trees and Random Forest, Support Vector Machines (SVM), Dimensionality Reduction Techniques (PCA), Cluster Analysis, Text Mining and Sentiment Analysis (on Economic News), Machine Learning in Time Series Prediction, Big Data Applications and Data Ethics.
Course Precondition
There are no course prerequisites. Basic knowledge of statistics and econometrics is recommended.
Resources
1. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R. Springer. 2. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
Notes
Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the basic concepts of data science and its integration with the discipline of economics. |
| LO02 | Prepares large-scale economic data sets for analysis with programming languages. |
| LO03 | Describe the relationships between economic variables using advanced data visualization techniques. |
| LO04 | Develop economic forecasting models using supervised learning algorithms. |
| LO05 | Unsupervised learning methods group and downsize economic indicators. |
| LO06 | Evaluate the performance and validity of models using model selection criteria. |
| LO07 | Generate quantitative indicators from qualitative data (MB minutes, news) using text mining techniques. |
| LO08 | Formulate and present economic policy scenarios using the results of data-driven analysis. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gains expertise in economics. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Specifies the role of the market economy, the price advantage of the event. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explains basic macroeconomic problems such as unemployment, inflation, current account deficit and policies to solve these problems based on scientific sources. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Faces with challenges, produces a numerical and policy options. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Builds, decodes and interprets models by using quantitative and qualitative techniques | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Learning the application of programme packages like SAS, RATS, SPSS, TSP, Win BUGS, EViews, etc to do analyses of empirical data. | 5 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Uses the theory of economics in analyzing the economic events | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Acquires the ability of analyzing in the conceptual level ,compares interprets, evaluates and synthesizes ideas in order to develop solutions to the problems. | 2 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Takes responsibility individually and / or in a team,leads a team and works effectively | |
| PLO10 | Bilgi - Kuramsal, Olgusal | With the awareness of the necessity of life-long learning, follows the latest developments about his field and improves himself | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Speaks Turkish, and at least one other foreign language in accordance with the requirements of academic and business life. | 2 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Questions traditional approaches, implementations, and methods ;develops and practises alternative study programs when required | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Knows where and how to find all scientific resources and data to do her research. | 5 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Understands the necessity and importance of research in social sciences. | |
| PLO15 | Beceriler - Bilişsel, Uygulamalı | Understands the relationship between economics and other branches of science. | 2 |
| PLO16 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Expresses himself/herself accurately, both orally and in writing. | |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Recognize and apply social, scientific and professional ethical values. | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Economics and Data Science: Basic Concepts and Approaches | Related article reading | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Programming for Data Science (Python/R) Installation and Introduction | Software installations | Öğretim Yöntemleri: Alıştırma ve Uygulama, Anlatım, Deney / Laboratuvar |
| 3 | Data Structures and Access to Economic Data Sources (API, Scraping) | Data source review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Data Cleaning and Manipulation (Tidying Data) | Sample data set preparation | Öğretim Yöntemleri: Alıştırma ve Uygulama, Soru-Cevap |
| 5 | Exploratory Data Analysis and Economic Data Visualization | Graphics libraries review | Öğretim Yöntemleri: Alıştırma ve Uygulama, Tartışma |
| 6 | Moving from Traditional Econometrics to Machine Learning: Lasso/Ridge | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Classification Problems: Logistic Regression and K-Nearest Neighbor | Case study | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Decision Trees and Ensemble Methods: Random Forest | Literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | 10 Gradient Boosting Machines and XGBoost Applications Reading the textbook Practice, Discussion | Reading the textbook | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Dimensionality Reduction (PCA) and Clustering | Applied data set analysis | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Introduction to ML and Deep Learning in Time Series Forecasting | Literature reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Deney / Laboratuvar |
| 13 | Text Mining: Economic News and Sentiment Analysis | Preparation for data extraction | Öğretim Yöntemleri: Alıştırma ve Uygulama, Anlatım |
| 14 | 14 Big Data Analytics, Data Ethics and Economic Policy Design Case studies Discussion, Lecture | Case studies | Öğretim Yöntemleri: Alıştırma ve Uygulama, Örnek Olay, Anlatım |
| 15 | Term Project Presentations and General Evaluation | Project presentation preparation | Öğretim Yöntemleri: Bireysel Çalışma, Alıştırma ve Uygulama |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 142 | ||
| Total Workload / 25 (h) | 5,68 | ||
| ECTS | 6 ECTS | ||