| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits | Last Updated Date |
| 7 | ECON401 | DATA ANALYSIS AND BIG DATA USAGE | 3+0+0 | 3 | 5 | 14.05.2025 |
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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
|
ECONOMICS
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Type of Program
|
Formal Education
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Type of Course Unit
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Compulsory
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Introducing fundamental concepts of data analysis and big data usage.
Developing skills of data gathering and analysis.
Providing basic knowledge on conducting research in the field of data analytics.
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Course Content
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Course is designed to provide necessary tools for conducting research projects and it includes computer applications. Subjects such as data collection, data processing, data analysis and interpretation, transformation of data into information, and regression are examined. In the big data usage section, definition, scope and three important examples of big data in the science of economics (open data, real-time data, managerial data) are introduced. Applications of these big data types are related with the applications of economics in fields such as finance or health economics. In addition, use of big data in empirical studies and estimation processes are examined.
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Course Methods and Techniques
|
-
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Prerequisites and co-requisities
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( ECON301 )
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Course Coordinator
|
None
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Name of Lecturers
|
Associate Prof.Dr. Umut Türk
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Assistants
|
Research Assist. Mustafa Semih Peker
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
|
-
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Course Notes
|
-
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Course Category
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Mathematics and Basic Sciences
|
%10
|
|
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Engineering
|
%10
|
|
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Social Sciences
|
%50
|
|
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Science
|
%20
|
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
|
In-Term Studies
|
|
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı
|
1
|
%
30
|
|
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı
|
1
|
%
40
|
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Ödev
|
5
|
%
30
|
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Total
|
7
|
%
100
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ECTS Allocated Based on Student Workload
|
Activities
|
Total Work Load
|
|
Ev Ödevi
|
7
|
3
|
21
|
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Sınıf İçi Aktivitesi
|
15
|
1
|
15
|
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Final Teslimi ve Jüri
|
1
|
20
|
20
|
|
Sunum
|
1
|
10
|
10
|
|
Yüz Yüze Ders
|
14
|
3
|
42
|
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Derse Devam
|
14
|
3
|
42
|
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Total Work Load
| |
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Number of ECTS Credits 5
150
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Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
|
1
| Recognize the key aspects of data analysis and big data usage. |
|
2
| Distinguish the differences between big data types. |
|
3
| Extract data from existing data sources and form databases. |
|
4
| Implement statistical, mathematical and econometric methods in data analysis with a focus on global issues. |
|
5
| Use programs such as Phyton, R, Stata for solving statistical, mathematical and econometric problems. |
|
6
| Construct models and interpret statistical findings. |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Software Introduction
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| 2 |
Introduction to R studio
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| 3 |
Variables, Vector Operations
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| 4 |
Functions: Correlations
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| 5 |
More on Functions
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| 6 |
Matrices and arrays
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|
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| 7 |
Merging Datasets
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|
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| 8 |
Factor, Conditional Selection
|
|
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| 9 |
Packages in R
|
|
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| 10 |
Advanced Visualization
|
|
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| 11 |
Advanced Visualization
|
|
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| 12 |
Data Frames and Data Types
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|
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| 13 |
Data Scraping
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|
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| 14 |
Data Scraping
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Recommended Optional Programme Components
Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=70504&lang=en