Data Mining Specialization Course Syllabus

Overview: This Data Mining Specialization provides a comprehensive introduction to key data mining techniques, blending theoretical concepts with hands-on application. Over approximately 100 hours, learners will explore data visualization, text retrieval, text mining, pattern discovery, and clustering. Each module builds practical skills using real-world datasets and industry-standard tools, culminating in a capstone project. The course is ideal for those entering data science roles in technology, finance, healthcare, or marketing.

Module 1: Data Visualization

Estimated time: 15 hours

• Principles of effective data visualization
• Human perception and cognition in visual design
• Data extraction and preparation for visualization
• Using Tableau for creating interactive visualizations

Module 2: Text Retrieval and Search Engines

Estimated time: 30 hours

• Introduction to search engine architecture
• Inverted index and Boolean retrieval models
• Query processing and ranking algorithms
• Evaluation of text retrieval systems

Module 3: Text Mining and Analytics

Estimated time: 33 hours

• Statistical methods for text analysis
• Topic modeling and document clustering
• Sentiment analysis and opinion mining
• Extracting knowledge from unstructured text

Module 4: Pattern Discovery in Data Mining

Estimated time: 17 hours

• Frequent pattern mining concepts
• Apriori and FP-growth algorithms
• Association rule learning
• Applications in market basket analysis

Module 5: Cluster Analysis in Data Mining

Estimated time: 16 hours

• Clustering methodologies and algorithms
• K-means, hierarchical, and density-based clustering
• Clustering validation and evaluation metrics
• Handling high-dimensional data in clustering

Module 6: Final Project

Estimated time: 10 hours

• Apply data mining techniques to a real-world Yelp dataset
• Perform clustering and pattern discovery on user reviews
• Submit a comprehensive analysis report with visualizations

Prerequisites

• Basic knowledge of statistics
• Familiarity with programming (preferably Python or R)
• Understanding of fundamental data structures and algorithms

What You'll Be Able to Do After

• Understand and apply core data mining techniques to structured and unstructured data
• Design effective data visualizations using tools like Tableau
• Implement text retrieval and search systems
• Extract meaningful patterns and insights from large text datasets
• Conduct clustering analysis and evaluate results on real-world data