| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- They will see general information about Python Programming Language, creating functions and loops, writing basic code, and functions of basic packages (modules).
- They will see the general use of Spyder and JupyterLab interfaces and the basic code writing on them.
- Data parsing and merging,
- Data visualization,
- They will learn some data mining techniques both theoretically and practically.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | NA |
|---|
| Course Description | It is a course for learning Python programing language and making applications on Basic Data Mining techniques. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Python |
|
2nd Week |
Data Structures in Python |
|
3rd Week |
Conditional Expressions and Loops |
|
4th Week |
Input Structure, Functions in Python |
|
5th Week |
Some Functional Packages (Modules) |
|
6th Week |
Numpy and Pandas Packages |
|
7th Week |
Data Manipulation |
|
8th Week |
Mid-term examination |
|
9th Week |
Data Visualization |
|
10th Week |
Introduction to Data Mining, Current Issues on Data Science |
|
11th Week |
Data mining technique 1 (theoretical and applied) |
|
12th Week |
Data mining technique 2 (theoretical and applied) |
|
13th Week |
Data mining technique 3 (theoretical and applied) |
|
14th Week |
General review and presentations |
|
| Recommended or Required Reading | "Data Mining: Concepts and Techniques", Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2001.
"Data Mining: Practical Machine Learning", Ian H. Witten, Eibe Frank, Morgan Kaufmann, 2000.
"Principles of Data Mining (Adaptive Computation and Machine Learning)", D Hand, MIT Press, 2001.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Case Study
- Problem Solving
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Project |
1 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
3 |
42 |
| Preparation for Final exam |
1 |
25 |
25 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Project |
1 |
16 |
16 |
| Total Work Load | 149 |
| Total Work Load / 30 (h) | 4.96 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
