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Course Name (English)
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DATA MINING
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| Course Code |
ISP565 |
| Course Description |
The Data Mining course introduces the concepts and methods of data mining and shows its relationship with data science. All the steps involved in knowledge data discovery will be discussed. Topics include Introduction to Data Mining, Data Preparation and Pre-processing, Classification, Model Evaluation & Selection, Clustering, Association Analysis, and ends with Future Trends & Challenges. The algorithm for each modelling process is discussed with supporting examples using real-world datasets. These datasets are used for model building using assessable technology with easy-to-use platforms. The findings will be presented through digital tools. The knowledge and practical skills gained from this course would benefit the students for solving real problems in industry or society-related issues for various SDG-based applications. |
| Transferable Skills |
1. Demonstrate ability to identify and articulate self skills and knowledge.
2. Demonstrate analytical skills using technology |
| Teaching Methodologies |
Lectures, Lab Work, Discussion, Project-based Learning |
| CLO |
| CLO1 |
Assess the methods of data mining related to real application in data science |
| CLO2 |
Manipulate data mining methods based on the given tasks using data mining tools |
| CLO3 |
Organise the use of digital skills in model development related to the data mining project |
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| Pre-Requisite Courses |
No course recommendations |
| Reading List | | Recommended Text | - Shuzlina Abdul Rahman & Sofianita Mutalib 2021, Predictive Analytics Applications with WEKA [ISBN: 9789672355038]
- Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar 2019, Introduction to Data Mining, 2nd Ed., Addison-Wesley [ISBN: 9780133128901]
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| Reference Book Resources | - Mohammed J. Zaki,Wagner Meira, Jr 2020, Data Mining and Machine Learning, Cambridge University Press [ISBN: 9781108473989]
- Davy Cielen,Arno Meysman,Mohamed Ali 2016, Introducing Data Science, Manning Publications [ISBN: 9781633430037]
- Galit Shmueli,Peter C. Bruce,Inbal Yahav,Nitin R. Patel,Kenneth C. Lichtendahl, Jr. 2017, Data Mining for Business Analytics, John Wiley & Sons [ISBN: 9781118879368]
- Sebastian Raschka,Vahid Mirjalili 2017, Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow, 2 Ed. [ISBN: 9781787125933]
- Jiawei Han,Jian Pei,Micheline Kamber 2011, Data Mining: Concepts and Techniques, 3 Ed., Elsevier [ISBN: 0123814804]
- Ian H. Witten,Eibe Frank,Mark A. Hall,Christopher J. Pal 2011, Practical Machine Learning Tools and Techniques, 2017 Ed., Morgan Kaufmann [ISBN: 97801280429]
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| Article/Paper List | This Course does not have any article/paper resources |
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| Other References | This Course does not have any other resources |
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