Course Code: | MIS206 | ||||
Course Name: | Data Science 2 | ||||
Semester: |
Fall |
||||
Course Credits: |
|
||||
Language of instruction: | English | ||||
Course Condition: | |||||
Does the Course Require Work Experience?: | No | ||||
Type of course: | Departmental Elective | ||||
Course Level: |
|
||||
Mode of Delivery: | Face to face | ||||
Course Coordinator: | Dr. Öğr. Üy. OZAN EVKAYA | ||||
Course Lecturer(s): | Şebnem Özdemir | ||||
Course Assistants: |
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 |
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. |
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 |
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 Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
---|---|---|---|---|---|
Program Outcomes |
No Effect | 1 Lowest | 2 Average | 3 Highest |
Program Outcomes | Level of Contribution |
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 |
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 |