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By University of Illinois at Urbana-Champaign. Instructors: Abel Bliss, Professor, Department of Computer Science; John C. Hart, Professor, Department of Computer Science; ChengXiang Zhai, Professor, Department of Computer Science.

Purpose:

- Understand the basic concepts and principles in data mining and visualization.
- Learn commonly used algorithms for mining both structured and unstructured (text) data.
- Understand how to handle a large amount of text data with search engines.
- Gain experience applying some of the algorithms to solve real world data mining problems.

Module 1 - Pattern Discovery in Data Mining

- Introduction to data mining
- Concepts and challenges in pattern discovery and analysis
- Scalable pattern discovery algorithms
- Pattern evaluation
- Mining flexible patterns in multi-dimensional space
- Mining sequential patterns
- Mining graph patterns
- Pattern-based classification
- Application examples of pattern discovery

Module 2 - Text Retrieval and Search Engines

- Introduction to text data mining
- Basic concepts in text retrieval
- Information retrieval models
- Implementation of a search engine
- Evaluation of search engines
- Advanced search engine technologies

Module 3 - Cluster Analysis and Data Mining

- Basic concept and introduction
- Partitioning methods
- Hierarchical methods
- Density-based methods
- Probabilistic models and EM algorithm
- Spectral clustering
- Clustering high dimensional data
- Clustering streaming data
- Clustering graph data and network data
- Constraint-based clustering and semi-supervised clustering
- Application examples of cluster analysis

Module 4 - Text Mining and Analytics

- Overview of text analytics and applications
- Extending a search engine to support text analytics (text categorization, text clustering, text summarization)
- Topic mining and analysis with statistical topic models
- Opinion mining and summarization
- Integrative analysis of text and structured data

Module 5 - Visualization

- Week 1: Visualization Infrastructure (graphics programming and human perception)
- Week 2: Basic Visualization (charts, graphs, animation, interactivity)
- Week 3: Visualizing Relationships (hierarchies, networks)
- Week 4: Visualizing Information (text, databases)

Capstone

The 6-week long capstone project class will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the specialization to solve interesting real-world data mining challenges. After finishing the capstone project class, you can expect to generate tangible results such as reports or prototype systems, which can be shown to potential employers to demonstrate their skills. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.

You must take the capstone project class after taking all the other courses in this specialization.

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