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 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix