Unit 902: Capstone Project in Data Science

1. Introduction to Capstone Project in Data Science

• Definition and overview of a capstone project
• Importance in a data science curriculum
• Learning objectives and expected outcomes
• Role in bridging theory with real-world applications

2. Selecting a Capstone Project Topic

• Criteria for topic selection
  • Alignment with personal interests and strengths
  • Industry relevance and impact
  • Feasibility and resource availability
• Sources for project ideas (datasets, industry problems, academic research)
• Evaluating scope and complexity
• Ethical considerations and data privacy

3. Data Collection and Preprocessing

• Data collection techniques
  • Public datasets
  • APIs and web scraping
  • Data acquisition from organizations
• Data cleaning and preprocessing
  • Handling missing values
  • Data type conversions
  • Data normalization and scaling
• Data wrangling methods
• Ensuring data quality and integrity

4. Exploratory Data Analysis (EDA)

• Objectives of EDA
• Summary statistics
  • Measures of central tendency and dispersion
• Data visualization techniques
  • Histograms, boxplots, scatter plots
  • Correlation matrices
• Detecting outliers and anomalies
• Tools for EDA: Python libraries (pandas, matplotlib, seaborn)

5. Machine Learning Modeling

• Overview of machine learning in data science projects
• Types of algorithms
  • Regression (linear, logistic)
  • Classification (decision trees, SVM)
  • Clustering (K-means, hierarchical clustering)
  • Ensemble methods (random forest, boosting)
• Model selection criteria
• Implementation using Python frameworks (scikit-learn)

6. Feature Engineering and Selection

• Importance of feature engineering
• Techniques
  • Creating new features
  • Encoding categorical variables (one-hot, label encoding)
  • Dimensionality reduction (PCA, LDA)
• Feature selection methods
  • Filter, wrapper, and embedded methods
• Impact on model performance

7. Model Evaluation and Validation

• Evaluation metrics
  • Accuracy, precision, recall, F1 score
  • ROC curve and AUC
• Cross-validation techniques
• Hyperparameter tuning
  • Grid search, random search
• Avoiding overfitting and underfitting

8. Data Visualization and Interpretation

• Role of visualization in data science
• Visualization tools and libraries
  • matplotlib, seaborn
  • Tableau overview
• Best practices for effective visual communication
• Storytelling with data

9. Project Documentation and Presentation

• Components of project documentation
  • Problem statement
  • Data description
  • Methodology
  • Results and analysis
  • Conclusions and future work
• Preparing presentations
  • Structuring slides
  • Visual aids and demos
• Delivering to technical and non-technical audiences
• Incorporating feedback