DAT-220 covers essential concepts such as data preprocessing, exploratory data analysis, and predictive modeling. Students engage with key methodologies including classification, clustering, and association rule mining using tools like R and Python. Case studies illustrate real-world applications, while hands-on projects involve analyzing datasets to derive actionable insights, with emphasis on data-mining tasks such as classification, clustering, and sequential pattern discovery.
Preprocessing before mining
The course establishes data preprocessing as a foundational step, since raw data is rarely ready for mining as-is — cleaning, formatting, and preparing data genuinely determines the quality of any subsequent analysis.
Multiple mining methodologies for different questions
DAT-220 covers classification, clustering, and association rule mining as genuinely distinct methodologies, each suited to answering different types of analytical questions about a dataset.
Key topics in DAT220
- Data preprocessing
- Exploratory data analysis
- Classification and clustering methods
- Association rule mining
- Predictive modeling
- Using R and Python for data mining
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Worked example: matching mining methodology to question
- Classification question: Predicting which category a new data point belongs to
- Clustering question: Discovering natural groupings within data with no predefined categories
- Lesson: DAT-220 teaches that choosing the right mining methodology depends on the specific analytical question being asked, not applying one technique universally
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Frequently asked questions
Raw data almost always contains inconsistencies, missing values, or formatting issues that must be addressed before any mining methodology can produce reliable results, and applying sophisticated classification or clustering algorithms to poorly prepared data risks producing misleading or simply incorrect insights. DAT-220 covers preprocessing first because the quality of any subsequent data mining work depends fundamentally on this preparatory step, regardless of how advanced the mining technique applied afterward.
These methodologies answer genuinely different analytical questions — classification predicts which predefined category new data belongs to, clustering discovers natural groupings without predefined categories, and association rule mining identifies relationships between variables — and a data analyst who only knows one methodology can only answer the specific type of question that technique addresses. DAT-220 covers multiple methodologies because real-world data mining problems vary in what kind of question they're actually asking, requiring different tools for different situations.