Fundamentals of Data Mining
An ever-increasing volume of research and industry data is being collected on a daily basis. Skilled data scientists are needed to process and filter the data, to detect new patterns or anomalies within the data, and gain deeper insight from the data.
This course provides students with a foundation in basic data mining, data analysis, and predictive modelling concepts and algorithms. Using practical exercises, students will learn data analysis and machine learning techniques for model and knowledge creation through a process of inference, model fitting, or learning from examples.
- Introduction to data mining and big data
- Data mining process and standards
- Classification and prediction methods
- Preparing input and output
- Decision tables
- Decision trees
- Classification rules
- Bayesian learning
- Association rules
- Numeric prediction: regression and model trees
- Clustering: k-means, hierarchical, probabilistic, EM
- Model training, testing, and evaluation
- Hands-on data mining projects
Software: WEKA is used for class assignments. There is no additional cost for this product.
Course typically offered: Online in Fall and Spring
Prerequisites: Statistics for Data Analytics or equivalent working knowledge is required. Linear Algebra for Machine Learning is also recommended, but not required. You can test your level of statistical knowledge by taking the online Self-Assessment quiz.
Next Steps: Upon completion of this course, consider taking Data Preparation for Analytics to continue learning.
More Information: For more information about this course, please contact email@example.com.
- COURSE NUMBER CSE-41258
- CREDIT 3.00 unit(s)
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