Course Code: | MIS304 | ||||
Course Name: | Data Science 4 | ||||
Semester: |
Fall |
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Course Credits: |
|
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Language of instruction: | English | ||||
Course Condition: | |||||
Does the Course Require Work Experience?: | No | ||||
Type of course: | Departmental Elective | ||||
Course Level: |
|
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Mode of Delivery: | Face to face | ||||
Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
Course Lecturer(s): | Şebnem Özdemir | ||||
Course Assistants: |
Course Objectives: | The aim of this course is to teach the construction of machine learning models and their execution with the help of a data analysis language with its mathematical background. |
Course Content: | Identify the role of data as a business asset, understand the principles of predictive modeling, recognize how different data science methods can support business decision-making, learn basic data analytic techniques for solving business problems, understand the promises and limitations of big data, gain some experience in using data analytic tools. |
The students who have succeeded in this course;
1) Recognizes classical machine learning methods. 2) Explains the model generation process using R or Python. 3) Creates a model using multiple algorithms 4) Compares the models, chooses the performance one. |
Week | Subject | Related Preparation |
1) | Introduction to the Course – Basic Concepts – Overview of Content to be Learned and Assessment and Evaluation Activities for a Whole Semester | |
2) | Regression concept, Linear and Logistic regression | |
3) | Regression concept, Linear and Logistic regression | |
4) | Regression concept, Linear and Logistic regression | |
5) | Bayesian methods, Naive Bayes | |
6) | Bayesian methods, Naive Bayes | |
7) | Neighborhood, similarity concept, KNN | |
8) | Support Vector Machines | |
9) | Support Vector Machines | |
10) | Support Vector Machines | |
11) | Artificial neural networks | |
12) | Artificial neural networks | |
13) | Remodeling with CV Methods | |
14) | Distruptive Concepts | |
15) | Final Exam |
Course Notes / Textbooks: | Ek kaynak ihtiyacı bulunmamaktadır. - There is no need for additional resources. |
References: | Ek kaynak ihtiyacı bulunmamaktadır. - There is no need for additional resources. |
Course Learning Outcomes | 1 |
2 |
3 |
4 |
---|---|---|---|---|
Program Outcomes |
No Effect | 1 Lowest | 2 Average | 3 Highest |
Program Outcomes | Level of Contribution |
Semester Requirements | Number of Activities | Level of Contribution |
Quizzes | 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 | 0 | 2 | 28 | |||
Application | 14 | 0 | 3 | 42 | |||
Quizzes | 1 | 10 | 10 | ||||
Midterms | 1 | 15 | 2 | 17 | |||
Final | 1 | 20 | 2 | 22 | |||
Total Workload | 119 |