ECTS - Introduction to Data Science
Introduction to Data Science (SE422) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Introduction to Data Science | SE422 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
|
| Course Objectives | |
| Course Learning Outcomes |
The students who succeeded in this course; |
| Course Content | Python programming language for data science, data scraping, data manipulation, data visualization, use of vectors and matrices in data science, review of statistical concepts for data science, conditional probability, Bayes?s theorem, normal distribution, prediction, regression, classification and clustering. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|
Sources
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | - | - |
| Final Exam/Final Jury | - | - |
| Toplam | 0 | 0 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Develops the ability to apply advanced knowledge of mathematics, science, and engineering to the analysis, design, and optimization of complex systems. | |||||
| 2 | Implements long-term research and development studies in the major fields of Electrical and Electronics Engineering. | |||||
| 3 | Use modern engineering tools, techniques and facilities in design and other engineering applications. | X | ||||
| 4 | Does research actively on innovation and entrepreneurship. | |||||
| 5 | Develops the ability to effectively communicate and present research outcomes. | |||||
| 6 | Keeps up with recent advancements in science and technology and effectively accesses relevant information. | |||||
| 7 | Will have professional and ethical responsibility. | |||||
| 8 | Develops ability to effectively communications in both Turkish and English. | |||||
| 9 | Develops ability on project management. | |||||
| 10 | Develops the ability to work successfully at project teams in interdisciplinary fields. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | 3 | 5 | 15 |
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 5 | 5 |
| Prepration of Final Exams/Final Jury | 1 | 8 | 8 |
| Total Workload | 124 | ||