CS 506: Data Science Tools and Applications
| Spring 2025 | Instructor: Lance Galletti | Boston University |
Welcome to CS 506! This course provides a comprehensive introduction to data science tools and applications, combining theoretical foundations with hands-on practice.
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📚 Course Overview
This course covers the complete data science workflow, from data collection to model deployment. You’ll learn practical skills using real-world datasets and tools commonly used in industry.
What You’ll Learn
- Data Science Workflow - CRISP-DM methodology and best practices
- Data Preprocessing - Cleaning, transformation, and feature engineering
- Machine Learning - Clustering, classification, regression, and neural networks
- Data Visualization - Creating compelling visualizations and storytelling
- Real-world Applications - Industry-standard tools and techniques
Course Outcomes
By the end of this course, you will be able to:
- ✅ Apply the complete data science workflow to real problems
- ✅ Implement and evaluate machine learning algorithms
- ✅ Create compelling data visualizations and reports
- ✅ Use industry-standard tools (Python, pandas, scikit-learn)
- ✅ Communicate findings effectively to stakeholders
- ✅ Build a portfolio of data science projects
📋 Prerequisites
Required Background:
- Basic programming experience (any language)
- Familiarity with Python is highly recommended
- Basic statistics knowledge (mean, median, standard deviation)
What You’ll Learn:
- Data science workflow and methodology
- Data cleaning and preprocessing techniques
- Machine learning algorithms and implementation
- Data visualization and storytelling
- Real-world project development
If You’re Unsure:
- Contact the instructor if you have concerns about prerequisites
- We can provide additional resources for students who need them
- The course is designed to accommodate various backgrounds
Recommended Preparation:
- Review basic Python syntax if you’re new to it
- Familiarize yourself with Jupyter notebooks
- Brush up on basic statistics concepts
📊 Grading
| Component | Weight | Description |
|---|---|---|
| Participation | 10% | Active engagement in discussions and labs |
| Assignments | 30% | Weekly lab assignments and exercises |
| Midterm Report | 25% | Individual project report (due March 31) |
| Final Project | 35% | Real-world data science application (due May 1) |
Detailed Breakdown
Participation (10%):
- Active participation in class discussions
- Engagement in lab sessions
- Contribution to group activities
- Regular attendance and preparation
Assignments (30%):
- Weekly lab assignments
- Data analysis exercises
- Code implementation tasks
- Submission through Gradescope
Midterm Report (25%):
- Due: March 31, 2025
- Individual project report
- Data analysis and visualization
- Written report with findings
Final Project (35%):
- Due: May 1, 2025
- Individual or group project (up to 5 students)
- Real-world data science application
- Final presentation and report
📅 Course Schedule
Topics Covered
- Introduction to Data Science - Workflow, tools, and methodology (Lectures 1-2)
- Clustering - K-means, hierarchical, and density-based algorithms (Lectures 3-8)
- Singular Value Decomposition - Dimensionality reduction and feature extraction (Lectures 9-10)
- Classification - KNN, decision trees, Naive Bayes, and SVM (Lectures 11-15)
- Regression - Linear and logistic regression with evaluation (Lectures 16-20)
- Neural Networks - Deep learning fundamentals and applications (Lectures 21-23)
Semester Structure
- Duration: 12 weeks (Spring 2025)
- Total Lectures: 23
- Topics: 6 major topics with flexible duration
- Flexible Pacing: Topics can take 1-3 weeks depending on complexity
🎯 Course Components
Lectures
- Format: Interactive lectures with live coding
- Materials: Slides, worksheets, and code examples
- Location: Check your schedule
- Office Hours: See staff page for times and locations
Labs
- Format: Hands-on coding sessions
- Focus: Practical implementation of concepts
- Submission: Through Gradescope
- Support: TA assistance and peer collaboration
Projects
- Midterm Project: Individual analysis and report
- Final Project: Real-world application of your choice
- Portfolio: Build a collection of your best work
- Presentation: Share your findings with the class
🚀 Getting Started
1. Set Up Your Environment
- Install Python and Jupyter notebooks
- Set up required packages (pandas, numpy, matplotlib, scikit-learn)
- Configure your development environment
2. Join Our Community
- Discord: Join our server for discussions and help
- Gradescope: Submit assignments
- YouTube: Watch supplementary videos
3. Start Learning
- Review the course modules for detailed content
- Complete the setup tutorial
- Ask questions using the AI assistant!
📝 Contributing to Course Knowledge
Share Your Insights
Students can contribute their understanding and tips to help others learn:
- Create a Pull Request with your notes in the
student_notes/directory - Follow the format with sections for your thoughts, challenges, and tips
- Get instant feedback - PRs are automatically validated and merged
- Help future students - Your insights become part of the AI assistant’s knowledge
Note Format
---
title: "Your Topic - Your Understanding"
student_name: "Your Name"
topic: "Topic Name"
difficulty_level: "beginner/intermediate/advanced"
---
## What I Think
Your understanding of the concept...
## What I Found Challenging
Specific difficulties you encountered...
## My Tips for Other Students
Helpful advice and strategies...
📚 Course Resources
Essential Links
- Discord: Join our community
- Gradescope: Submit assignments
- YouTube: Supplementary videos
Learning Materials
- Lecture Slides: Available in each module
- Worksheets: Interactive Jupyter notebooks
- Code Examples: Complete implementations
- Student Notes: Peer insights and tips
Getting Help
- AI Assistant: Click the chat icon for instant help (requires OpenAI API key)
- Office Hours: See staff page for times
- Discord: Ask questions in our community
- Email: Contact the instructor for private concerns
🗓️ Important Dates
| Date | Event |
|---|---|
| Early Semester | Project proposal due |
| March 31 | Midterm report due |
| May 1 | Final project due |
| May 7 | Final exam |
🎓 Support and Communication
Office Hours
- Instructor: See staff page for times
- TAs: Available during lab sessions
- Discord: 24/7 community support
Communication Channels
- Course Questions: Use the AI assistant or Discord
- Private Concerns: Email the instructor
- Technical Issues: Discord or office hours
- Assignment Help: Gradescope or Discord
Ready to start your data science journey? Use the AI assistant to ask any questions, and don’t hesitate to reach out for help! 🚀