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 Be able to apply knowledge of programming
2. P02 Be able to design software systems of varying complexity
3. P03 Be able to identify, categorize, and develop solutions for computer orientated challenges.
4. P04 Be able to demonstrate autonomy and responsibility in managing computer programming projects
5. P05 Be able to follow the state of the arts concepts in computer technology
6. P06 Be able to design, implement, and evaluate a computational system to meet desired needs within realistic constraints.
7. P07 Be able to use appropriate techniques, skills, and tools necessary for computing practice.
8. P08 Be able to apply appropriate technologies and techniques for the collection and analysis of organizational and environmental data to facilitate evidence-based decision making
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