CAP4767: Data Mining with Python

Miami Dade College | School of Engineering and Technology

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👋 Welcome

This repository contains all course materials for CAP4767 Data Mining. Everything you need — readings, demo notebooks, exercises, labs, and project files — lives here.

Your Canvas course remains the official source for:

• Due dates and deadlines
• Assignment submissions
• Grades and feedback
• Announcements

Think of this GitHub repo as your textbook and workbench. Think of Canvas as your gradebook and calendar.

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🚀 Getting Started

First Time Here?

1. Bookmark this page — You'll return here weekly
2. Set up Google Colab — See Getting Started Guide
3. Test your setup — Open any notebook from the demos/ folder in Colab

New to GitHub?

You don't need to know Git commands for this course. Simply:

• Click on folders to navigate
• Click on .md files to read them
• Click on .ipynb files, then click "Open in Colab" badge to work with notebooks

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📁 Repository Structure

Folder	What's Inside	When to Use It
readings/	Chapter readings aligned to learning objectives	Before class — complete assigned reading
demos/	Notebooks from in-class demonstrations	During class — follow along with the professor
exercises/	Group exercise starter notebooks	In class — collaborative breakout practice
labs/	Individual lab starter notebooks	After class — independent assignments
case-studies/	Midterm and final project materials	Major assessments
data/	Datasets used across multiple chapters	Reference as needed
resources/	Slides, cheat sheets, helpful links	Reference as needed
solutions/	Exercise and lab solutions	Released after deadlines

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📅 Course Flow

Each week follows this pattern:

┌─────────────────────────────────────────────────────────────────┐
│  BEFORE CLASS                                                   │
│  ✓ Complete the chapter reading                                 │
│  ✓ Review the demo notebook                                     │
│  ✓ Watch the video companion (when available)                   │
└─────────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────────┐
│  SESSION 1                                                      │
│  → Presentation: concepts, analogies, real-world context        │
│  → Live demo: professor codes, you follow along                 │
└─────────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────────┐
│  SESSION 2                                                      │
│  → Group exercise: collaborative breakout with your team        │
│  → Share-out: one group member presents findings to class       │
└─────────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────────┐
│  AFTER CLASS                                                    │
│  ✓ Complete individual lab assignment (weeks assigned)          │
│  ✓ Submit in Canvas                                             │
└─────────────────────────────────────────────────────────────────┘

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📚 Chapter Overview

Week	Chapter	Topic	Key Techniques
1	1	Time Series Foundations	Rolling windows, resampling, seasonal decomposition
2	2	Time Series Forecasting	SARIMAX, Prophet, RMSE, R²
3	3	Regression	Linear, multiple, logistic regression
4	4	Customer Churn: EDA + Logistic Regression	Cramér's V, Cohen's d, classification report
4	5	Neural Networks	Keras ANN, confusion matrix, ROC curve
5	6	RFM + CLTV + K-Means	Customer segmentation, Elbow method, cluster profiles
6	7	Market Basket Analysis	Apriori, support, confidence, lift, mlxtend
7	8	RFM Across Domains	Universal framework, sequence analysis, anomaly detection
—	—	Midterm — Assigned Week 4 | Due Week 5	RFM + CLTV + K-Means pipeline
—	—	Final — Assigned Week 6 | Due Week 8	Full data mining capstone

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🛠️ Tools We Use

Tool	Purpose	Access
Google Colab	Write and run Python code	colab.research.google.com
GitHub	Access course materials	You're here!
Canvas	Submissions, grades, communication	MDC Canvas

No software installation required. Everything runs in your browser.

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📦 Key Libraries

# Data
import pandas as pd
import numpy as np

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Machine Learning
from sklearn.linear_model import LogisticRegression
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Time Series
from statsmodels.tsa.statespace.sarimax import SARIMAX
from prophet import Prophet

# Neural Networks
import tensorflow as tf
from tensorflow import keras

# Market Basket Analysis
from mlxtend.frequent_patterns import apriori, association_rules

All libraries are pre-installed in Google Colab. No setup required.

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❓ Getting Help

1. Check the reading — Most questions are answered in the chapter materials
2. Review the demo notebook — Annotated code examples with explanations
3. Post in Canvas — Classmates and professor can help
4. Office Hours — See Canvas for schedule

When asking for help with code:

• Describe what you're trying to do
• Share the error message (screenshot or copy/paste)
• Tell us what you've already tried

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📋 Quick Links

• Getting Started Guide
• Chapter Readings
• Demo Notebooks
• Group Exercises
• Individual Labs
• Midterm + Final Materials
• Datasets
• Python Quick Reference

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⚠️ Important Notes

• Do not fork this repository — Simply access it directly
• Submissions go to Canvas — Never submit work via GitHub
• Solutions are released after deadlines — Check the solutions/ folder
• Materials may be updated — Refresh your browser to see the latest versions
• Group exercise notebooks — Work together in class, each student submits individually via Canvas

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Questions about this repository? Ask in Canvas or bring them to class.