PRINCIPLES OF DATA MINING

Course Description

Data mining, which is the study of algorithms and computational paradigms that enable computers to search datasets for patterns and regularities and make predictions and forecasts is covered in this course. Knowledge discovery is introduced comprehensively. The course explores data selection, cleaning, coding, the application of various statistical and machine learning approaches, and visualization of the resulting structures, which are all steps in knowledge discovery. Students who successfully complete this course are supposed to learn about several data mining techniques, including classification, rule-based learning, decision trees, and association rules. Additionally, students are expected to learn about selection and cleaning of data, machine learning methods for "learning" about "hidden" patterns in data, and reporting and visualizing the resulting knowledge.

PRINCIPLES OF DATA MINING

Evaluation Tools (Active Term)

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Course outcomes

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Course Syllabus

Reference Books & Course Materials

1. 01 Introduction to Data Mining, 2nd edition, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson, 2018

Learning Outcomes

1. L01 Define key concepts, terminology, and processes in data mining, and explain its role in extracting meaningful patterns from large datasets. SOLO 2
2. L01 Identify and describe various data types, sources, and formats, and evaluate their suitability for different data mining tasks. SOLO 3
3. L01 Apply appropriate data collection methods, including implementing web scraping techniques, to gather and prepare real-world datasets for analysis. SOLO 4
4. L04 Discuss and assess ethical, legal, and privacy concerns related to data mining practices, and formulate strategies for data handling. SOLO 5
5. L05 Perform data preprocessing operations such as cleaning, integration, transformation, and reduction to enhance data quality and model performance. SOLO 4
6. L06 Implement and evaluate classification algorithms (both supervised and unsupervised) using appropriate metrics, and interpret model evaluation results to draw data-driven conclusions. SOLO 5

Program Outcomes

1. P01 Create a user interface in a contemporary object-oriented language to allow users to access business data
2. P02 Identify and analyze user needs and take them into account in the selection, creation, integration, evaluation and administration of computing-based systems
3. P03 Analyze common business functions and identify, design, and develop appropriate information technology solutions
4. P04 Design and develop software solutions for contemporary business environments by employing appropriate problem-solving strategies
5. P05 Configure and administer database server to support contemporary business environments.
6. P06 Administer or mange a relational database for a small to medium size company
7. P07 Be able to effectively integrate IT-based solutions into the user environment
8. P08 Understand professional, ethical, legal, security and social issues and responsibilities
9. P09 Be able to apply effective communication skills consistent with the professional environment
10. P10 Be able to apply effective collaboration skills in teamwork consistent with the professional environment

Po-Lo Matrix