Become a Data Detective: Mining Insights with RapidMiner

SAT Score Range

•

10 sessions

✨ Be the first

About

In this class, learners will explore the fundamentals of machine learning and data mining, starting with a quick background overview. Then, we’ll move into hands-on practice using RapidMiner, a no-code data mining tool. Students will learn how to prepare data and build real models such as prediction, classification, and clustering through simple drag-and-drop workflows. No prior experience is required, just curiosity and a desire to discover patterns in data!

Tutored by

Ipsa R 🇺🇸

View Profile

I’m Eepssa Rout, a recent Master’s graduate in Business Analytics from George Mason University. I enjoy helping students explore data mining and machine learning by breaking complex concepts into simple, practical steps. Using beginner-friendly, no-code tools like RapidMiner, I guide learners as they work with real data and understand how models and patterns appear in everyday life. I’m joining Schoolhouse to spark curiosity, build confidence and support anyone eager to discover the fundamentals of data, analytics, and ML.

Sessions

Upcoming

All

✋ ATTENDANCE POLICY

This class is designed for high school learners in 11th or 12th grade who are interested in data analytics. Students should join each session on time and be prepared to actively participate in both the discussion and hands-on activities. A laptop is required to run RapidMiner for the better learning experience. If a student misses multiple sessions or is unable to engage with the class requirements, they may be withdrawn to allow space for other learners.

SESSION 1

8

Dec

SESSION 1

Study Spaces

Mon 10:00 PM - Tue, 12:00 AM UTCDec 8, 10:00 PM - Dec 9, 12:00 AM UTC

In this class, you’ll learn the fundamentals of data mining and explore key concepts. I’ll demonstrate how models work and run examples in RapidMiner, giving you a clear understanding before diving into practical exercises and theory in the following sessions.

SESSION 2

11

Dec

SESSION 2

Study Spaces

Thu 10:00 PM - Fri, 12:00 AM UTCDec 11, 10:00 PM - Dec 12, 12:00 AM UTC

In this session, we’ll explore data using descriptive statistics and visual tools to understand patterns and relationships. Students will learn how to examine multivariate data, distinguish correlation from causation, and detect both linear and non-linear trends. Through hands-on practice in RapidMiner, we’ll perform data aggregation and visualizations to reveal deeper insights. By the end, students will be able to interpret datasets clearly and communicate key findings visually.

SESSION 3

15

Dec

SESSION 3

Study Spaces

Mon 10:00 PM - Tue, 12:00 AM UTCDec 15, 10:00 PM - Dec 16, 12:00 AM UTC

In this session, we’ll learn how to clean and reduce data to improve model performance. Students will explore prediction methods with a focus on Linear Regression, understanding the best-fit line, how it’s created, and how to evaluate its accuracy. We’ll practice these concepts in RapidMiner by preparing data and building our first predictive model. By the end, students will be able to apply regression techniques and assess how well their model fits the data.

SESSION 4

17

Dec

SESSION 4

Study Spaces

Wed 10:00 PM - Thu, 12:00 AM UTCDec 17, 10:00 PM - Dec 18, 12:00 AM UTC

In this session, we’ll extend regression concepts to Multiple Linear Regression, where more than one variable is used to make predictions. Students will learn how to evaluate MLR models, handle discrete (categorical) variables, and apply feature selection techniques to choose the most important predictors. Using RapidMiner, we’ll build and compare models to see how the right features can improve accuracy. By the end, learners will understand how to design stronger and more reliable regression models.

SESSION 5

19

Dec

SESSION 5

Study Spaces

Fri 10:00 PM - Sat, 12:00 AM UTCDec 19, 10:00 PM - Dec 20, 12:00 AM UTC

In this session, we’ll shift from predicting numbers to classifying outcomes using Logistic Regression. Students will learn how the logit function works and how to evaluate classification performance using metrics like confusion matrix, sensitivity, and specificity. We’ll also explore how altering the cutoff probability can change prediction results. Through RapidMiner practice, learners will build and assess a classification model to understand how well it separates different classes.

SESSION 6

22

Dec

SESSION 6

Study Spaces

Mon 10:00 PM - Tue, 12:00 AM UTCDec 22, 10:00 PM - Dec 23, 12:00 AM UTC

In this session, we’ll continue evaluating logistic regression using lift charts and scoring to measure how models perform in real scenarios. Then, we’ll introduce Decision Trees as another powerful classification method. Students will learn the structure of a decision tree, how recursive partitioning works, and how the model splits data to make predictions. Using RapidMiner, we’ll build and interpret a classification tree and compare its performance to previous models.

SESSION 7

5

Jan

SESSION 7

Study Spaces

Mon 10:00 PM - Tue, 12:00 AM UTCJan 5, 10:00 PM - Jan 6, 12:00 AM UTC

In this session, we’ll refine our decision tree models by learning pruning techniques and reviewing how data scoring is used to assess results. We’ll discuss the strengths and weaknesses of tree-based methods. Then, we’ll shift into unsupervised learning with clustering. Students will explore real applications of clustering, perform k-means clustering in RapidMiner, and learn how to evaluate, interpret, and properly name clusters. By the end, learners will understand when to use clustering and how to uncover natural groupings in data.

SESSION 8

7

Jan

SESSION 8

Study Spaces

Wed 10:00 PM - Thu, 12:00 AM UTCJan 7, 10:00 PM - Jan 8, 12:00 AM UTC

In this session, we’ll take a deeper look at how clustering works by understanding distance measures and how they affect the grouping of data. Students will compare different clustering algorithms—hierarchical and non-hierarchical—and learn how dendrograms represent cluster structure. We’ll revisit the k-means algorithm and discuss how selecting the right variables can improve clustering results. With hands-on practice in RapidMiner, learners will explore how different approaches lead to different insights from the same data.

SESSION 9

9

Jan

SESSION 9

Study Spaces

Fri 10:00 PM - Sat, 12:00 AM UTCJan 9, 10:00 PM - Jan 10, 12:00 AM UTC

In this session, we’ll explore affinity analysis to discover items or behaviors that frequently occur together. Students will use the FP-Growth algorithm and learn how minimum support helps identify meaningful patterns. We’ll build association rules and evaluate them using lift and confidence, while clearly understanding rule components such as premises and conclusions. With RapidMiner practice, learners will extract real insights from transaction-style data.

SESSION 10

12

Jan

SESSION 10

Study Spaces

Mon 10:00 PM - Tue, 12:00 AM UTCJan 12, 10:00 PM - Jan 13, 12:00 AM UTC

In this final session, students will apply everything they’ve learned to a self-chosen dataset. They will clean, explore, and prepare the data, then build predictive or clustering models using RapidMiner. Each student will evaluate their results and present key insights from their project. By the end, learners will have a complete data mining workflow they can showcase as a real accomplishment.

Public Discussion

Please log in to see discussion on this series.

Dec 8 - Jan 13

6 weeks

120 mins

/ session

Next session on December 8, 2025

SCHEDULE

Mondays

10:00PM