School of Engineering \ Artificial Intelligence Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
Material Science and Nanotechnology Engineering Bachelor's Degree Program
Fundamentals of data science; introduction to coding in Plthon; probability and statistical hypothesis testing; data visualization, and data science ethics; a brief introduction to machine leaming algorithms.
Textbook and / or References
| Tentative Assesment Methods |
| Activities |
Number |
Weight (%) |
| Course Attendance/Participation |
- |
- |
| Laboratory |
- |
- |
| Application |
- |
- |
| Homework |
4 |
20% |
| Project |
1 |
20% |
| Presentation |
- |
- |
| Field Work |
- |
- |
| Internship |
- |
- |
| Course Boards |
- |
- |
| Quiz |
- |
- |
| Midterm Exam |
1 |
25% |
| Final Exam |
1 |
35% |
|
Total |
100% |
| Tentative ECTS-Workload Table |
| Activities |
Number/Weeks |
Duration (Hours) |
Workload |
| Course Hours (first 6 weeks) |
6 |
4 |
24 |
| Course Hours (last 6 weeks) |
6 |
3 |
18 |
| Homework |
4 |
10 |
40 |
| Project |
1 |
30 |
30 |
| Presentation |
- |
- |
- |
| Internship |
- |
- |
- |
| Course Boards |
- |
- |
- |
| Preparation for Quiz |
- |
- |
- |
| Preparation for Midterm Exam |
1 |
15 |
15 |
| Final Exam |
1 |
2 |
2 |
| Preparation for Final Exam |
1 |
20 |
20 |
| Study Hours Out of Class (preliminary work, reinforcement, etc.) |
1 |
30 |
30 |
| Total Workload | | |
179 |
| Total Workload / 30 | | |
179 / 30 |
| | |
|
| ECTS Credits of the Course | | |
6 |
|
Program Outcome
**
|
| 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
|
Course Outcome
|
|---|
| 1 |
A
|
A
|
C
|
|
A, C
|
|
|
|
|
|
|
| 2 |
A
|
|
|
|
|
|
|
B
|
|
|
|
| 3 |
|
|
|
|
C, D
|
|
|
|
|
|
|
| 4 |
|
A
|
|
|
A, C, D
|
|
|
|
|
|
|
| 5 |
A
|
|
|
|
|
|
|
|
B
|
|
|
| 6 |
|
|
|
|
C, D
|
A, B
|
|
A, C
|
|
A
|
|