ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
Course Introduction and Application Information
| Course Code: | MIS304 | ||||
| Course Name: | Data Science 4 | ||||
| Semester: | Fall | ||||
| 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 Objective and Content
| 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. |
Learning Outcomes
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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. |
Course Flow Plan
| 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 |
Sources
| 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 - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
|---|---|---|---|---|
| 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 |
| Quizzes | 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 | 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 | ||||||