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HIF 539: Big Data and Data Mining

Course Description

This course provides an accessible introduction to the core principles and techniques of data mining and big data analytics. Students will learn how to explore, preprocess, and analyze data to extract meaningful patterns and insights. The course introduces fundamental concepts such as classification, clustering, and association rule mining using real-world examples. It covers the basics of big data platforms like Hadoop and Spark to demonstrate how data mining scales in modern distributed environments. This course emphasizes intuitive understanding and practical applications over mathematical rigor, making it ideal for students with little or no prior background in data science. (3 credits)

Prerequisite

  • None

Student Learning Outcomes (SLOs)

Upon successful completion of the course, the student will be able to:

  1. Describe the fundamental concepts of data mining and big data and their role in modern analytics.
  2. Apply data preprocessing techniques such as cleaning, normalization, and transformation.
  3. Perform basic classification tasks using intuitive methods like decision trees and k-nearest neighbors.
  4. Apply clustering techniques to group data based on similarity and explore how to interpret the results.
  5. Generate simple association rules from transactional data and explain their practical uses.
  6. Explain the architecture and role of big data tools such as Hadoop, MapReduce, and Spark.
  7. Analyze large datasets using scalable data mining techniques in distributed environments.
  8. Create insights and findings from data through small projects or case studies using real-world data.

 

Course Activities and Grading

AssignmentsWeight

Discussions (Weeks 1-7)

20%

Assignments (Weeks 1-7)

50%

Presentations (Week 8)

5%

Final Project (Weeks 6-8)

25%

Total

100%

Required Textbooks

Available through Charter Oak State College's Book Bundle

  • Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals: Concepts, drivers & techniques. Pearson.
  • Han, J., Pei, J., & Tong, H. (2022). Data mining: Concepts and techniques. (4th ed.). Morgan Kaufmann. 

Course Schedule

Week

SLOs

Readings and Exercises

Assignments

1

1,6

Topic: Intro to Data Mining and Big Data  

  • Read and Review:
    • Han, Chapter 1
    • Erl, Chapter 1
  • Read assigned material
  • Review lecture material
  • Participate in Discussions
  • Submit the Written Assignment

2

2

Topics: Cleaning and Organizing Data

  • Read and Review:
    • Han, Chapter 2
    • Erl, Chapter 2
  • Read assigned material
  • Review lecture material
  • Participate in Discussions
  • Submit the Written Assignment

3

3

Topic: Classification: Fundamentals

  • Read and Review:
    • Han, Chapter 3
    • Han, Chapter 4
  • Read assigned material
  • Review lecture material
  • Participate in Discussions
  • Submit the Written Assignment

4

3,4

Topic: Classification: Advanced Topics

  • Read and Review:
    • Han, Chapter 4
  • Read assigned material
  • Review lecture material
  • Participate in Discussions
  • Submit the Written Assignment

5

1,2,4

Topic: Clustering

  • Read and Review:
    • Han, Chapter 10
  • Read assigned material
  • Review lecture material
  • Participate in Discussions
  • Submit the Written Assignment

6

6,7

Topics: Big Data Fundamentals & Ecosystem

  • Read and Review:
    • Erl, Chapters 3-5
  • Read assigned material
  • Review lecture material
  • Participate in Discussions
  • Submit the Written Assignment
  • Submit Proposal for the Final Project

7

6,7

Topics: Hadoop & Spark Architecture

  • Read and Review:
    • Erl, Chapter 5
    • Erl, Chapter 6 
  • Read assigned material
  • Review lecture material
  • Participate in Discussions
  • Submit the Written Assignment

8

6,7,8

Topics: Big Data Integration & Final Project Presentation

  • Read and Review:
    • Erl, Chapter 7
    • Erl, Chapter 8
  • Read assigned material
  • Review lecture material
  • Submit Presentation
  • Submit Final Project
  • Complete Course Evaluation

COSC Accessibility Statement

Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.

COSC Policies, Course Policies, Academic Support Services and Resources

Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.