MIS206 Data Science 2Istinye UniversityDegree Programs Management Information Systems (English)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Management Information Systems (English)

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Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code: MIS206
Course Name: Data Science 2
Semester: Spring
Course Credits:
ECTS
5
Language of instruction: English
Course Condition:
Does the Course Require Work Experience?: No
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: 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
1) It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering. 3 3 3 3 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 3 3
3) Uses different information technologies and systems for understanding and solving various business problems. 2 3 3 3 3
4) Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods. 3 3 3 3 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.
7) Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively. 3 3 3 3 3
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. 3 3 3 3

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. 3
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.
9) Develops teamwork, negotiation, leadership and entrepreneurship skills.
10) Has universal ethical values, social responsibility awareness and sufficient legal knowledge. 1
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. 3

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