Building Data-Driven Solutions for Tomorrow's Business Challenges
ISM6251 | Fall 2025
Dr. Tim Smith
School of Information Systems and Management
Course Description: A hands-on introduction to machine learning techniques with a focus on practical business applications. Students will learn to frame problems, prepare data, build and evaluate models, and communicate results to business stakeholders.
| Week | Topic | Key Concepts | Deliverables |
|---|---|---|---|
| 1 | Course Introduction & ML Overview | Python, Jupyter, ML Basics | Python Course (Part 1), Discussion, Quiz |
| 2 | ML Foundations and Python | pandas, sklearn, matplotlib, Overfitting | Python Course (Part 2), Practice Assignment |
| 3 | Data Preparation Deep Dive | Missing Data, Imputation, Filtering | Data Wrangling Assignment |
| 4 | Linear Regression | MSE, R², Outlier Detection | Build Linear Models, Revenue Prediction Case |
| 5 | Logistic Regression | Binary Classification, Confusion Matrix | Customer Churn Classification |
| 6 | K-Nearest Neighbors | Multi-class, Feature Scaling | Product Recommendation Assignment |
| Week | Topic | Key Concepts | Deliverables |
|---|---|---|---|
| 7 | Support Vector Machines | SVC/SVM, Class Imbalance | Fraud Detection Assignment |
| 8 | Decision Trees & Hyperparameter Tuning | ROC/AUC, Model Interpretability | Credit Scoring Assignment |
| 9 | Ensemble Methods | Random Forests, XGBoost | Advanced Prediction Models |
| 10 | Neural Networks | ANNs, SHAP, Feature Importance | Customer Behavior Modeling |
| 11 | Clustering Techniques | K-Means, DBSCAN, Hierarchical | Customer Segmentation Analysis |
| 12 | Recap & Final Exam | Course Review | Final Exam, Final Project Due |
| Component | Weight | Description |
|---|---|---|
| Weekly Assignments | 15% | Hands-on coding exercises and problem sets |
| Discussion Participation | 15% | Canvas discussions, peer engagement |
| In-Class Quizzes | 20% | Closed book, Honorlock proctored |
| Final Project | 25% | Group project with peer evaluation |
| Final Exam | 25% | Comprehensive, closed book |
| DataCamp Courses | 5% (Bonus) | Optional supplementary learning |
# Step 1: Install Anaconda (recommended)
# Download from: https://www.anaconda.com/products/individual
# Step 2: Create a new environment
conda create -n ism6251 python=3.9
# Step 3: Activate the environment
conda activate ism6251
# Step 4: Install required packages
pip install numpy pandas scikit-learn matplotlib seaborn jupyter
# Step 5: Launch Jupyter Notebook
jupyter notebook
# Step 6: Test your setup
import pandas as pd
import numpy as np
import sklearn
print("Setup successful!")