Course Introduction and Application Information

Course Code:

MIS206

Course Name:

Data Science 2

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

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:

Dr. Öğr. Üy. OZAN EVKAYA

Course Lecturer(s):

Şebnem Özdemir

Course Assistants:

Course Objective and Content

Course Objectives:	The goal of this course for each student is: To teach the vital step of the data analysis, pre-processing, not only by using a tool/language but also mathematical background.
Course Content:	This course covers Data Pre-process, Missing Data, Outliers, Noise in Data, Statistical Methods for Pre-process, Completion of Missing Value, LDA, EDA, Feature Extraction, and Usage of a Data Analyze Tool/Language

Learning Outcomes

The students who have succeeded in this course;
1) Upon successful completion of the course students know special cases related to values in a data set.
2) Upon successful completion of the course students defines pre-processing
3) Upon successful completion of the course students explains usage of the statistical methods in the pre-process
4) Upon successful completion of the course students apply the mathematical formula for completing the missing values
5) Upon successful completion of the course students apply the analysis of outliers, filling / extracting missing data, feature selection by using a data analysis language and explains the results obtained.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction to course, the fundamental concepts, general evaluation of the semester and pinned points related to measurement activities
2)	Outliers, Missing Values, Noise in Dataset, Simple Codes for Data Analysis, Detecting Special Conditions by Using Graphs, Reading the Story that Hidden in Dataset
3)	Mathematical Calculations for Completing Missing Value
4)	Mathematical Calculations for Completing Missing Value
5)	Usage of the Data Analysis Tool for Completing Missing Value
6)	Usage of the Data Analysis Tool for Completing Missing Value
7)	Type of Regression for Reading Data Story, LDA, EDA
8)	Type of Regression for Reading Data Story, LDA, EDA
9)	Curse of Dimensionality, Feature Extraction/Engineering
10)	Mathematical Background of Feature Extraction Methods
11)	Usage of the Data Analysis Tool for Feature Extraction
12)	Usage of the Data Analysis Tool for Feature Extraction
13)	Usage of the Data Analysis Tool for Feature Extraction
14)	Disruptive Concepts/Technologies/Situations
15)	Disruptive Concepts/Technologies/Situations
16)	Final Exam

Sources

Course Notes / Textbooks:	Active learning will be conducted. Students are supposed to search and learn topics discussed in the lesson.
References:	Ek kaynak ihtiyacı bulunmamaktadır. - There is no need for additional resources.

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

Program Outcomes

Level of Contribution

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	1	% 20
Midterms	1	% 35
Final	1	% 45
total		% 100
PERCENTAGE OF SEMESTER WORK		% 55
PERCENTAGE OF FINAL WORK		% 45
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	Completing the Activity Requirements	Workload
Course Hours	14	1	4		70
Quizzes	2	10	1		22
Midterms	1	15	1		16
Final	1	20	1		21
Total Workload					129