Course Details

INTRODUCTION TO DATA SCIENCE

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
1	DSBE510	INTRODUCTION TO DATA SCIENCE	3+0+0	3	7,5

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	DATA SCIENCE
Type of Program	Formal Education
Type of Course Unit	Compulsory
Course Delivery Method	Face To Face
Objectives of the Course	Increasing students’ knowledge about data and data types. Introducing working principles of Python and R. Explaining how to use data collection, data grouping and data management techniques. Practicing fast and efficient access to reliable data.
Course Content	The course introduces the fundamental programming languages of data science. Main aims of the course are to teach, at the basic level, subjects of data collection, data grouping, data management, fast and efficient access to reliable data, and Python and R programming languages. In this way, it is planned to introduce the programming languages for the first time to students who do not have prior programming knowledge, to teach the basics of coding, and to help students gain the ability of applying the course content in the sub-branches of business and economic disciplines.
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Associate Prof.Dr. Umut Türk
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Course Category

Mathematics and Basic Sciences	%20
Engineering	%10
Social Sciences	%50
Science	%10
Field	%10

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
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı	2	% 40
Proje/Çizim	2	% 50
Total	4	% 90

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Ev Ödevi	7	5	35
Sınıf İçi Aktivitesi	15	1	15
Proje	2	20	40
Kişisel Çalışma	15	1	15
Öğretici Sunum/Açıklama	2	15	30
Yüz Yüze Ders	15	3	45
Derse Devam	15	3	45
Total Work Load			Number of ECTS Credits 7,5 225

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

No	Learning Outcomes
1	Interpret basic descriptive and inferential statistical analyses.
2	Use Python and R programming languages effectively.
3	Import datasets from the internet.
4	Visualize data.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to Python:
2	IDEs
3	variables, expressions, statements
4	Strings, lists, dictionaries
5	tuples, conditional execution, loops and functions
6	tuples, conditional execution, loops and functions
7	Data Science Packages for Python: Numpy, Pandas, Matplotlib
8	Introduction to R
9	Data and data types
10	Descriptive statistics, summarizing data
11	Functions
12	Data visualisation: charts, figures, histograms, tables
13	Factor and correlation analyses
14	Inferential Statistics: regression types (I), regression types (II)
15	Inferential Statistics: regression types (I), regression types (II)

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=71466&lang=en