ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
Course Introduction and Application Information
| Course Code: | MIS309 | ||||
| Course Name: | Data Science 3 | ||||
| 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 execution of a data analysis language together with its mathematical background. |
| Course Content: | Basic Python functions, classical algorithms in machine learning, mathematical infrastructures |
Learning Outcomes
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The students who have succeeded in this course;
1) Uses Python's core functions 2) Recognizes libraries 3) Comprehends the mathematical structure of decision tree algorithms. 4) Creates a decision tree with the help of R package. 5) Creates a decision tree with the help of Python libraries. 6) Explains the decision tree outputs. |
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) | Anaconda environment, Jupyter, DataBrick environments | |
| 3) | Python and its features | |
| 4) | Basic functions and structures in Python | |
| 5) | Working with libraries | |
| 6) | Decision tree algorithms and basic methods in machine learning | |
| 7) | Training and test set distinctions, achievement and performance concepts | |
| 8) | Decision trees mathematical background | |
| 9) | Decision trees mathematical background | |
| 10) | Creating a decision tree using the R package | |
| 11) | Evaluating the outputs of decision trees | |
| 12) | Creating a decision tree using a Python library | |
| 13) | Evaluating the outputs of decision trees, creating multiple models, model comparison | |
| 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 |
5 |
6 |
|---|---|---|---|---|---|---|
| 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 | Workload |
| Course Hours | 14 | 28 |
| Application | 14 | 42 |
| Quizzes | 5 | 10 |
| Midterms | 8 | 18 |
| Final | 15 | 28 |
| Total Workload | 126 | |