DATA ANALYTICS II PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
YBS316	DATA ANALYTICS II	6	3	3	8

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. DİDEM TETİK KÜÇÜKELÇİ
Instructor(s) of the Course Unit	Assist.Prof. SÜREYYA İMRE BIYIKLI
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	This course aims to enable students to develop basic algorithms in order to solve different types of problems and to teach the basic structures of programming and programming in a computerized environment; thus, it aims to make students think like a data scientist.
Contents of the Course Unit:	The content of the course is the concepts of algorithms and programming, variables, data types, input and output statements, arrays, lists, dictionaries, functions, file operations and the use of Python as a data analytics tool.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Develop algorithm for solving problems encountered in daily life. {develop}
Evaluate the functions of the Python programming language.{evaluate}
Design algorithms with Python programming language. {design}
Test the usability of algorithms.{testing}
Apply machine learning design to create solutions that meet the needs identified taking into account global, cultural, social, environmental and economic factors, as well as public health, safety and well-being.{Application}

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	-	Course Information (Syllabus) and Introduction to Python	Lecture & Studying on Sample & Practice
2	Studying on Sample, Assignment	Basic Data Structures in Python	Lecture & Studying on Sample & Practice
3	Studying on Sample, Assignment	Loops and Functions in Python	Lecture & Studying on Sample & Practice
4	Studying on Sample, Assignment	Data Preprocessing with Python	Lecture & Studying on Sample & Practice
5	Studying on Sample, Assignment	Data Visualization with Python	Lecture & Studying on Sample & Practice
6	Studying on Sample, Assignment	Polynomial Regression	Lecture & Studying on Sample & Practice
7	Studying on Sample, Assignment	Logistic Regression	Lecture & Studying on Sample & Practice
8	Studying on Sample, Assignment	Ridge Regression	Lecture & Studying on Sample & Practice
9	Studying on Sample, Assignment	Lasso Regression,Elastic Net Regression	Lecture & Studying on Sample & Practice
10	-	MID-TERM EXAM	-
11	Studying on Sample, Assignment	Decision Tree Algorithms	Lecture & Studying on Sample & Practice
12	Studying on Sample, Assignment	Support Vector Regression	Lecture & Studying on Sample & Practice
13	Studying on Sample, Assignment	Support Vector Machine	Lecture & Studying on Sample & Practice
14	Studying on Sample, Assignment	Q&A Session on Final Projects	Studying on Sample & Practice
15	Studying on Sample, Assignment	FINAL	Studying on Sample & Practice
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Gursakal, N. (2014). Istatistikte R ile Programlama.
Arslan, I. (2015). R ile Istatistiksel Programlama. Pusula Publications, Istanbul.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	50		Classical Exam
Final Exam	1	50		Classical Exam
TOTAL	2	100

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Define concepts such as management, manager and leader.					4
2	Analyze the accuracy, reliability and validity of the new information obtained from the data.						5

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Report the obtained data.						5
2	Prepare software and projects related with the field.						5

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Use the appropriate resources for data analysis related with the field.						5
2	Analyze the work processes.						5

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Manage projects as part of a team.						5
2	Apply the material, techniques and analyzes in relation with the subject for project and work flows.						5

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Fulfill responsibility with a focus on result in individual and team studies.						5

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Recognizes what he/she knows about his/her field or not.						5
2	Act the theoretical knowledge in real life with learning to learn approach.					4
3	Apply different methods and techniques with an innovative approach in his/her research.						5

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Apply the results obtained in accordance with voluntarism and social responsibility projects.						5
2	Establish a healthy contact with colleagues				3
3	Share the analyzes and obtained results with colleagues.				3
4	Cooperate with colleagues at international level with the help of foreign language competency.					4

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Behave in accordance with ethical values regarding the collection, analysis and reporting of data.					4
2	Participate the design of work processes and systems with full quality.						5
3	Cooperate with other employees for the continuation of sustainability in the profession.				3

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	13	3	39
Land Surveying	0	0	0
Group Work	7	4	28
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	14	2	28
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	7	5	35
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	7	4	28
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	64	0	202