Information Package / Course Catalogue - Adnan Menderes University

General Information Unit Staff Academic Information Programme Outcomes Course Structure Diagram Contact Information

Person Responsible for Information Package

Prof. Aynur UÇKAÇ

Prof. Şansel ÖZPINAR

Assoc. Prof. Sercan YAVAN

Lec. Demet AKDENİZ

Lec. Erkam SARI

Lec. Gülizar Seda YILMAZ

Lec. Tarık ILİMAN

Financial Data Analysis

Course Code	: MLY310
Course Type	: Area Elective
Couse Group	: First Cycle (Bachelor's Degree)
Education Language	: Turkish
Work Placement	: N/A

Theory	: 3
Prt.	: 0
Credit	: 3
Lab	: 0
ECTS	: 5

Objectives of the Course

The aim of this course is to equip students with the fundamental skills for analyzing financial data and to teach how this data is used in making economic decisions. Students will learn the processes of collecting, analyzing, and interpreting financial data, while gaining the ability to make data-driven decisions using econometric tools and statistical methods. Additionally, the course aims to reinforce both theoretical and practical knowledge related to financial data analysis.

Course Content

This course provides students with the essential knowledge for analyzing financial data. Students will learn to analyze financial data using econometric and statistical methods. Topics covered include data collection techniques, data cleaning, hypothesis testing, regression analysis, and time series analysis. Additionally, practical knowledge of the software and tools used in financial analysis will be provided. Students will work with real-world data, gaining the ability to conduct analyses and apply this knowledge in economic decision-making processes.

Name of Lecturer(s)

Learning Outcomes

1.	The student will be able to apply basic methods and techniques for financial data analysis.
2.	The student will be able to analyze data sets using statistical methods such as regression analysis and multiple regression.
3.	The student will be able to perform time series analysis and identify trends and cyclical changes in financial data.
4.	The student will be able to ensure the accuracy of data by applying appropriate data cleaning and preparation techniques.
5.	The student will be able to make data-driven recommendations for financial decisions by performing analyses on real-world data.

Recommended or Required Reading

1.	Yılmaz, A. (2019). Statistical Methods and Econometric Analyses. Nobel Publishing.
2.	Asteriou, D., & Hall, S. G. (2015). Applied Econometrics: A Modern Approach. Palgrave Macmillan.

Weekly Detailed Course Contents

Week 1 - Theoretical

Introduction and Overview of Data Analysis; Concept of data analysis, importance of financial data, data collection methods.

Week 2 - Theoretical

Data Cleaning and Preparation; Preparing data sets, dealing with missing data, and anomaly detection.

Week 3 - Theoretical

Basic Statistical Methods; Basic statistical analysis of data sets (mean, median, variance, etc.).

Week 4 - Theoretical

Regression Analysis and Applications; Simple linear regression analysis and applications.

Week 5 - Theoretical

Multiple Regression and Modeling; Multiple regression analysis and modeling techniques.

Week 6 - Theoretical

Time Series Analysis and Applications; Analyzing time series data, trends, and cyclical changes.

Week 7 - Theoretical

Correlation and Variance Analysis; Analyzing correlations and variance in data sets.

Week 8 - Theoretical

Econometric Modeling and Advanced Methods; Econometric modeling techniques, GARCH, VAR models.

Week 9 - Theoretical

Data Mining and Applications; Data mining techniques, pattern recognition, and classification.

Week 10 - Theoretical

Analysis of Financial Data; Special methods and tools for analyzing financial data.

Week 11 - Theoretical

Analysis of Economic Data; Analyzing economic data and evaluating economic indicators.

Week 12 - Theoretical

Financial Reporting and Interpretation; Preparing financial reports, analyzing balance sheets and income statements.

Week 13 - Theoretical

Applied Data Analyses; Hands-on analysis using real-world data and case studies.

Week 14 - Theoretical

General Review and Project Work; Overall course review, student projects, and in-class discussions.

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Midterm Examination	1	%40
Final Examination	1	%60

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	3	3	84
Midterm Examination	1	12	1	13
Final Examination	1	24	1	25
TOTAL WORKLOAD (hours)				122

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7	PÇ-8	PÇ-9	PÇ-10
OÇ-1	4	3	2	2	3	2	3	3	3	2
OÇ-2	3	4	3	2	4	3	2	3	4	3
OÇ-3	3	3	4	3	4	3	4	3	4	3
OÇ-4	2	3	3	3	4	3	5	4	4	3
OÇ-5	4	4	4	5	4	3	4	3	5	3