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Data Mining: Advanced Concepts and Algorithms

As the amount of research and industry data being collected daily continues to grow, intelligent software tools are increasingly needed to process and filter the data, detect new patterns and similarities within it, and extract meaningful information from it. Data mining and predictive modeling offer a means of effective classification and analysis of large, complex, multi-dimensional data, leading to discovery of functional models, trends and patterns.

Building upon the skills learned in previous courses, this course covers advanced data mining, data analysis, and pattern recognition concepts and algorithms, as well as models and machine learning algorithms.

Topics include:

  • Data mining with big data
  • Artificial neural networks
    • Back-propagation
    • Feed-forward networks
    • Radial-basis functions
    • Recurrent neural networks
  • Probability graph models and Bayesian learning
  • Hidden Markov models
  • Support vector machines
  • Ensemble learning: bagging, boosting, stacking
  • Random forests
  • Data mining tools
  • Text mining

Practical experience:

  • 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: Data Preparation for Analytics and Fundamentals of Data Science or equivalent experience required.

Next Steps: Upon completion of this course, consider taking the Data Mining Practicum to continue learning.

More Information: For more information about this course, please contact

Course Number: CSE-41262
Credit: 3.00 unit(s)
Related Certificate Programs: 

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