Find latent patterns in different types of data from customer purchases to staff performance
Evaluate the accuracy and effectiveness of clustering analyses
Understand the purpose and implications of what clustering methods can and cannot achieve
|Prerequisites||Intro to R|
|Practice||4 to 7 hours|
Syllabus: Clustering and Finding Patterns
This course is designed for students with a basic familiarity with R and some experience with data analysis and data manipulation. In just 100 minutes of instructional time, students learn how to think about finding patterns, perform different types of clustering analyses, and evaluate the quality of the results.
By the end of this course, students will be able to:
- Find latent patterns and groups in different types of data
- Evaluate the accuracy and effectiveness of clustering analyses
- Understand the purpose and implications of what clustering methods can and cannot achieve
- Concept reviews: these are comprised of short five question quizzes that cover the most important concepts and ideas in each lesson. They encourage holistic understanding and are multi-faceted question types (i.e. drag and drop, fill-in-the-blanks, matching, etc).
- Exercises: these are additional videos that cover the coding functions in the instructional video in more depth. They are project-based and include coding templates for students to strengthen their skills outside of the course.
- Accompanying PDFs to use as reference materials
- R code templates from the instructional videos and exercises
- Data sets used in the instructional videos and exercises
1. Introduction to clustering (30 min)
The impact of data
Where is data today?
What is clustering?
2. Clustering politicians (30 min)
3. Clustering athletes (15 min)
Clustering basketball salaries
4. Clustering customers (27 min)
Finding customer patterns
Digging into our data
Calculating the best number of clusters
Additional tips and resources
Total instructional time: 1 hr, 42 min
Savanna Flakes is a nationally awarded Instructional Specialist who enjoys using data to transform instructional practices. She coaches administrators and teachers internationally on effective pedagogy, such as data analysis, professional development, collaborative learning teams, technology integration, differentiation, student engagement and inclusive practices. Savanna believes “Data Matters.” By knowing how to analyze and sort student data, educators are empowered to make informed instructional decisions to increase every student’s achievement.
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