Applied Data Science Capstone Course Syllabus

Overview: This capstone course provides a hands-on opportunity to apply the end-to-end data science methodology to a real-world problem. Over approximately 5 weeks with a flexible schedule, learners will progress through key stages of a data science project, including data collection, wrangling, visualization, modeling, and final presentation. Each module builds on the previous one, culminating in a comprehensive project that showcases practical skills in Python, machine learning, and data storytelling. Estimated time commitment is about 20–25 hours total.

Module 1: Introduction and Data Collection

Estimated time: 5 hours

• Understand the project context and objectives
• Identify relevant data sources for analysis
• Access data using APIs
• Extract data via web scraping with BeautifulSoup

Module 2: Data Wrangling and Exploration

Estimated time: 5 hours

• Clean and preprocess raw data
• Handle missing values and data inconsistencies
• Perform exploratory data analysis (EDA)
• Apply statistical methods and visualizations to uncover patterns

Module 3: Data Visualization and Dashboarding

Estimated time: 5 hours

• Create informative visualizations using Matplotlib and Seaborn
• Build interactive maps with Folium
• Develop dynamic dashboards using Plotly Dash

Module 4: Machine Learning and Model Evaluation

Estimated time: 5 hours

• Build classification models using Scikit-learn
• Train and test models including Decision Trees, K-Nearest Neighbors, and Support Vector Machines
• Evaluate model performance using accuracy, precision, recall, and F1-score
• Compare models and refine for better performance

Module 5: Final Report and Presentation

Estimated time: 5 hours

• Compile findings into a comprehensive report
• Summarize methodology, results, and insights
• Prepare a stakeholder-ready presentation

Module 6: Final Project

Estimated time: 5 hours

• Deliverable 1: Complete data collection and cleaning script
• Deliverable 2: Interactive dashboard and visualization notebook
• Deliverable 3: Final report and model evaluation summary

Prerequisites

• Proficiency in Python programming
• Familiarity with data analysis using Pandas
• Understanding of machine learning concepts and techniques

What You'll Be Able to Do After

• Apply the full data science lifecycle to real-world problems
• Collect and clean data from APIs and web sources
• Create interactive visualizations and dashboards
• Build, compare, and evaluate classification models
• Demonstrate end-to-end project skills with a shareable portfolio piece