| 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;
- Understand the fundamental concepts of artificial intelligence, its application areas, and its impact on individuals, society, and the business world.
- Explain topics related to data science, big data, data mining, and machine learning, including their key components, differences, application purposes, and methodologies.
- Understand the structure, working model, and core features of the Python programming language.
- Learn the Python and data science development environments (such as Anaconda, Jupyter, Google Colab, Spyder, etc.) and their installation processes.
- Gain knowledge of variables, basic data types, assignments, and arithmetic operations in Python.
- Develop skills in using control and loop statements in Python.
- Acquire knowledge of data structures in Python.
- Understand the use of functions, as well as functional analysis and design in software development with Python.
- Develop competency in using classes within Python.
- Identify the main stages of data science projects.
- Recognize the processes of data collection, cleaning, processing, exploratory data analysis, and the design and application of data models and algorithms.
- Become familiar with supervised machine learning models and algorithms.
- Become familiar with unsupervised machine learning models and algorithms.
- Possess the knowledge and skills necessary to develop a term project at an applied level.
|
| Mode of Delivery | Face to face |
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| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course has two main objectives: (a) to gain essential programming knowledge and skills by learning the structure and components of the Python programming language, necessary for developing applications in data science, machine learning, enterprise, and web applications; (b) to implement entry-level data science and machine learning applications using the acquired programming knowledge and skills. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to artificial intelligence and fundamental concepts; application areas; impacts on personal, social, and business domains; data science, big data, data mining, machine learning and its components, Python software development environments and installations (Anaconda, Google Colab, Jupyter, Spyder, etc.) |
|
2nd Week |
Python programming language syntax and differences (spacing, cell concept), variables, basic data types, statements, assignments, and arithmetic operations; data structures in Python; Python's built-in packages and their applications |
|
3rd Week |
Control and loop statements, data processing with for and while statements, and error and exception handling |
|
4th Week |
Function usage, functional analysis, and design of Python software |
|
5th Week |
Fundamentals of object-oriented programming, the use of classes, strategies for arranging source code in Python projects (functional, object-oriented, module, and package concepts), organizing Python projects with the MVC template |
|
6th Week |
Data structures in Python and their usage |
|
7th Week |
General stages of data science and machine learning project management; project definition and initiation (needs analysis and problem definition, determination of project goals, preparation of project initiation document); project execution (data science steps: requirements specification, data source identification, and data acquisition) |
|
8th Week |
Midterm exam |
|
9th Week |
Exploratory data analysis, data preparation (cleaning, merging, transforming), Python libraries for data visualization (Seaborn, Matplotlib, etc.), Python libraries for data processing and computation (Pandas, Numpy, Scipy, etc.) |
|
10th Week |
Fundamental principles of machine learning, the stages and areas of its application, and the models of learning |
|
11th Week |
Supervised machine learning models (e.g., k-NN learning models) |
|
12th Week |
Unsupervised machine learning (e.g., k-means learning model) |
|
13th Week |
General review |
|
14th Week |
Presentation and evaluation of term projects |
|
| Recommended or Required Reading | Singh, P. (2019). Machine learning with PySpark. APress Media.
Mathur, P. (2019). Machine learning applications using Python: Case studies from healthcare, retail, and finance. APress Media.
Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: A guide for data scientists. O'Reilly Media.
Theobald, O. (2018). Machine learning for absolute beginners: A plain English introduction (Machine learning from scratch). Independent Publisher.
Mueller, J. P., & Massaron, L. (2016). Machine learning for dummies. John Wiley & Sons, Inc.
|
|---|
| Planned Learning Activities and Teaching Methods |
Projectct-Supported Traditional Instructional Method
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
1 |
10 |
| Project |
1 |
25 |
| Total(%) |
|
65 |
| Contribution of In-term Studies to Overall Grade(%) |
|
65 |
| Contribution of Final Examination to Overall Grade(%) |
|
35 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1.5 |
1.5 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
1 |
14 |
| Preparation for Final exam |
1 |
22 |
22 |
| Course hours |
13 |
2 |
26 |
| Preparation for Midterm exam |
1 |
14 |
14 |
| Laboratory (including preparation) |
13 |
1 |
13 |
| Final exam |
1 |
1.5 |
1.5 |
| Homework |
|
|
|
| Presentation (including preperation) |
1 |
3 |
3 |
| Project |
1 |
24 |
24 |
| Report writing |
1 |
23 |
23 |
| Total Work Load | 142 |
| Total Work Load / 30 (h) | 4.73 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
