Management Information Systems Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

YBS218

Course Name:

Data Science

Semester:

Spring

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Compulsory Courses

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Doç. Dr. ŞEBNEM ÖZDEMİR

Course Lecturer(s):

Şebnem Özdemir

Course Assistants:

Course Objective and Content

Course Objectives:	The goal of this course for each student is: To learn content of data and data analysis and applying them into real world problem
Course Content:	R, Python, data partition in data set – hold out and cv, decision tree, naïve bayes, k-nn, support vector machine, artificial neural network, multi-layered models

Learning Outcomes

The students who have succeeded in this course;
1) Knows the differences between data, information and knowledge and defines them by giving examples
2) Knows the structured, semi-structured and unstructured data
3) Applies the data collection and organizing it with the direction of steps in the process
4) Conduct the steps of data analysis by using a language
5) Knows the differences between algorithm and model
6) Selects the most proper model, after applying an algorithm on the data set

Course Flow Plan

Week	Subject	Related Preparation
1)	Data preprocessing with R
2)	Decision Tree with R
3)	Mathematical background of Naïve Bayes and its application on R
4)	Mathematical background of Naïve Bayes and its application on R
5)	Comparing models, fine tuning, and selection
6)	Mathematical background of support vector machine and its application on R
7)	Mathematical background of support vector machine and its application on R
8)	Mathematical background of artificial neural network and its application on R
9)	Mathematical background of multi-layered network
10)	Introduction to Python for data science
11)	Data preprocessing with Python
12)	Regression based models with Python
13)	Decision tree, naive bayes, knn, svm, ann with Python
14)	Decision tree, naive bayes, knn, svm, ann with Python
15)	Decision tree, naive bayes, knn, svm, ann with Python
16)	Final Exam Week

Sources

Course Notes / Textbooks:	Yalçın Özkan, R Dili Uygulamaları, Deniz Kılınç Python Uygulamaları, Haldun Akpınar, Data
References:	Mooc Ortamlarındakı Yardımcı Dersler,Öğretim Üyesinin Ders Notları Courses On The Moocs,Lecturer's Handouts

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5	6
Program Outcomes
1) It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering.
2) Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming.
3) Uses different information technologies and systems for understanding and solving various business problems.
4) Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods.
5) Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database.
6) Gains technical and managerial contributions to IT projects and takes responsibility.
7) Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively.
8) Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education.
9) Develops teamwork, negotiation, leadership and entrepreneurship skills.
10) Has universal ethical values, social responsibility awareness and sufficient legal knowledge.
11) Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them.
12) Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms.
13) It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering.	3
2)	Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming.	3
3)	Uses different information technologies and systems for understanding and solving various business problems.	3
4)	Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods.	3
5)	Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database.	1
6)	Gains technical and managerial contributions to IT projects and takes responsibility.	1
7)	Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively.	3
8)	Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education.	1
9)	Develops teamwork, negotiation, leadership and entrepreneurship skills.	1
10)	Has universal ethical values, social responsibility awareness and sufficient legal knowledge.	2
11)	Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them.	1
12)	Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms.	2
13)	It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.	3

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 20
Project	1	% 30
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	14	28
Application	14	28
Study Hours Out of Class	14	28
Homework Assignments	1	2
Midterms	6	15
Final	6	20
Total Workload		121