Course Details

DATA ANALYSIS AND BIG DATA USAGE

ECON401

Return to Classes
Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
7ECON401DATA ANALYSIS AND BIG DATA USAGE3+0+035

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ECONOMICS
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course 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.
Course Content 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.
Course Methods and Techniques
Prerequisites and co-requisities ( ECON301 )
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Umut Türk
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources

Course Category
Mathematics and Basic Sciences %10
Engineering %10
Social Sciences %50
Science %20

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
Veri yok

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Ev Ödevi 7 3 21
Sınıf İçi Aktivitesi 15 1 15
Final Teslimi ve Jüri 1 20 20
Sunum 1 10 10
Yüz Yüze Ders 15 3 45
Derse Devam 15 3 45
Total Work Load   Number of ECTS Credits 5 156

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning 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
WeekTopicsStudy MaterialsMaterials
1 Software Introduction
2 Introduction to R studio
3 Variables, Vector Operations
4 Functions: Correlations
5 More on Functions
6 Matrices and arrays
7 Merging Datasets
8 Factor, Conditional Selection
9 Packages in R
10 Advanced Visualization
11 Advanced Visualization
12 Data Frames and Data Types
13 Data Scraping
14 Data Scraping
15 Machine Learning


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
C1 1 3 1 4 1 4 1 1 1 4
C2 1 4 1 3 4 4 3 3 1 4
C3 2 3 2 4 2 4 2 2 2 4
C4 3 2 3 4 2 4 1 1 3 4
C5 3 2 3 4 2 4 3 3 3 4
C6 2 2 2 4 2 4 2 2 2 3

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
Return to Classes