Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
2	ECON 536	Applied Data Analysis	3+0+0	3	8	01.11.2025

Course Details

Language of Instruction	Turkish
Level of Course Unit	Master's Degree
Department / Program	MA Program in Economics (Thesis) (English)
Type of Program	Formal Education
Type of Course Unit	Compulsory
Course Delivery Method	Face To Face
Objectives of the Course	To teach: how to identify relevant data sources for economic research questions how to store, handle, convert, and combine datasets from different sources how to assess data scope and data quality how to conduct basic econometric analyses
Course Content	This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	Prof.Dr. Hasan Vergil
Name of Lecturers	Other Murat Yerlikaya
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Data Science for Business Provost, Foster and Tom Fawcet O’Reilly: Sebastopol, CA 78-1-449-36132-7
Course Notes	Thanks to modern software tools that allow to easily process and analyze data at scale, we are now able to extract invaluable insights from the vast amount of data generated daily. As a result, both the business and scientific world are undergoing a revolution which is fueled by one of the most sought after job profiles: the data scientist.

Course Category

Mathematics and Basic Sciences	%20
Engineering	%30
Social Sciences	%30
Field	%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

In-Term Studies	Quantity	Percentage
Mid-terms	1	% 30
Assignment	10	% 20
Attendance	1	% 10
Final examination	1	% 40
Total	13	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Course Duration	14	3	42
Hours for off-the-c.r.stud	14	3	42
Assignments	10	1	10
Mid-terms	1	15	15
Final examination	1	30	30
Total Work Load			Number of ECTS Credits 5 139

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	how to identify relevant data sources for economic research questions
2	how to store, handle, convert, and combine datasets from different sources
3	how to assess data scope and data quality
4	how to conduct basic econometric analyses
5	handle datasets using a common programming language
6	summarize data in meaningful ways

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to Applied Data Analytics. Data-analytic thinking and data-driven decision making. Data science process. Data sources and types of data. Current trends and applications of big data.
2	Introduction to Applied Data Analytics. Data-analytic thinking and data-driven decision making. Data science process. Data sources and types of data. Current trends and applications of big data.
3	Data management, data cleaning and data mannipulation with statistical software. Writing and executing a program in statistical software.
4	Data management, data cleaning and data mannipulation with statistical software. Writing and executing a program in statistical software.
5	Exploratory data analysis. Data visualization techniques.
6	Exploratory data analysis. Data visualization techniques.
7	Classification and prediction problems. Basic machine-learning algorithms. Similarity matching, k-means clustering, linear regression, logistic regression (binary classification problem).
8	Classification and prediction problems. Basic machine-learning algorithms. Similarity matching, k-means clustering, linear regression, logistic regression (binary classification problem).
9	Classification and prediction problems. Basic machine-learning algorithms. Similarity matching, k-means clustering, linear regression, logistic regression (binary classification problem).
10	Geographic Information Systems (GIS). Geo-mapping.
11	Geographic Information Systems (GIS). Geo-mapping.
12	Geographic Information Systems (GIS). Geo-mapping.
13	Text mining. Text as Data. Bag of words. N-grams. Mining the social web (e.g., Twitter).
14	Text mining. Text as Data. Bag of words. N-grams. Mining the social web (e.g., Twitter).

Contribution of Learning Outcomes to Programme Outcomes

	P1	P2	P3	P4	P5	P6	P7	P8
All	5	5	5	5	4	5	4	5
C1
C2
C3
C4
C5
C6

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://obs.ihu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=248769&lang=en