Computer Programming
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Computer Programming
Course Details
Trade and Industry Vocational School
Programme of Computer Programming
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 03831203 | Data mining | 2 | Autumn | 3 | 2+1+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | Project-Based Learning (PBL), Case Studies and Real-Life Examples |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Lect. Abubakar MAYANJA |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Abubakar MAYANJA | TSMYO-T213 | [email protected] | 7829 |
Course Content
A general introduction to the topics of Data Science. ,Basic data models, relational models of entities, relational models and SQL databases and related data models. Sources and types of big data, frequent analysis. Data literacy , Data Analysis and preprocessing , Data science project management and Student presentations on research topics and techniques . ,Recommendation systems; includes topics.
Objectives of the Course
It lays out the fundamentals of data science, which is rapidly evolving in the 21st century and is very popular with researchers and practitioners alike. The course transfers the basic skills that a data scientist should have to the student and enables the student to apply them to different fields.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | X |
| Support Courses | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P11 | Creates algorithms and data structures and performs mathematical calculations. | 3 |
| P13 | Performs database design and management. | 4 |
| P20 | Öğrencilerin bilgi teknolojilerini etkin ve verimli bir şekilde kullanarak akademik ve profesyonel hayatlarında karşılaştıkları problemleri çözme yetkinliği kazanmaları. | 3 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knows how to develop algorithms and creates a data structure suitable for the algorithm. | P.4.1 | 1,2,7 |
| O2 | Knows the basic elements of a computer. | P.1.1 | 7 |
| O3 | Knows how to use the internet and do research. | P.1.2 | 3 |
| O4 | To be able to define the basic concepts of information technologies and computer systems and to explain the relationships between these concepts. | P.20.1 | 1,3,4,5 |
| O5 | Ability to effectively use basic software applications (e.g. presentation software, etc.) and prepare professional documents with these tools. | P.20.2 | 4,6,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to Data Mining Overview of Data Mining Definition and importance Differences between data mining, machine learning, and statistics Applications of Data Mining Business, healthcare, finance, and social media Case studies of data mining in industry Data Mining Process Steps in a data mining project (CRISP-DM, SEMMA) Data collection, cleaning, and preprocessing |
| 2 | Data Preprocessing and Exploration Data Cleaning and Transformation Handling missing values and outliers Data normalization and standardization Feature Engineering and Selection Dimensionality reduction (PCA, LDA) Feature selection methods (filter, wrapper, embedded) Exploratory Data Analysis (EDA) Visualizations for data distribution Correlation analysis and feature importance |
| 3 | Classification Techniques Introduction to Classification Supervised vs. unsupervised learning Key evaluation metrics (accuracy, precision, recall, F1-score) Classification Algorithms Decision Trees, Naïve Bayes, K-Nearest Neighbors Support Vector Machines (SVM) Ensemble methods (Bagging, Boosting, Random Forests) Model Evaluation and Cross-validation K-fold cross-validation Confusion matrix and ROC curve analysis |
| 4 | Clustering Techniques Introduction to Clustering Difference between classification and clustering Applications and challenges in clustering Clustering Algorithms K-Means, Hierarchical Clustering DBSCAN, Mean Shift Evaluation metrics for clustering (Silhouette score, Dunn Index) Cluster Interpretation and Visualization |
| 5 | Association Rule Mining Introduction to Association Rule Mining Market basket analysis and frequent itemsets Applications in retail, finance, and healthcare Key Algorithms Apriori Algorithm FP-Growth Algorithm Evaluation of Association Rules Support, confidence, lift Rule pruning and interestingness measures |
| 6 | Advanced Data Mining Techniques Anomaly Detection Types of anomalies (point, contextual, collective) Algorithms for anomaly detection (Isolation Forest, LOF) Text Mining and Natural Language Processing (NLP) Text preprocessing (tokenization, stemming, stop-words removal) Text classification, sentiment analysis, topic modeling Time Series Analysis Techniques and applications in financial forecasting Basic time series models (ARIMA, Exponential Smoothing) |
| 7 | Mid-Term Exams |
| 8 | Presentations of students about research topics and techniques. |
| 9 | Data Analysis |
| 10 | Data Preprocessing |
| 11 | Data Science Project Management |
| 12 | Presentations of students about research topics and techniques. |
| 13 | Presentations of students about research topics and techniques. |
| 14 | Presentations of students about research topics and techniques. |
Textbook or Material
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | 1 | 20 (%) |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 50 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 2 | 28 |
| Midterms | 1 | 15 | 15 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 14 | 1 | 14 |
| Laboratory | 14 | 1 | 14 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 15 | 15 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 128 | ||
| Total Work Load / 30 | 4,27 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P4 | P20 |
|---|---|---|---|---|
| O1 | Knows the basic elements of a computer. | 5 | - | - |
| O2 | Knows how to use the internet and do research. | 3 | - | - |
| O3 | Knows how to develop algorithms and creates a data structure suitable for the algorithm. | - | 4 | - |
| O4 | To be able to define the basic concepts of information technologies and computer systems and to explain the relationships between these concepts. | - | - | 5 |
| O5 | Ability to effectively use basic software applications (e.g. presentation software, etc.) and prepare professional documents with these tools. | - | - | 5 |