Learning Objectives
5 objectives- Understand the significance and objectives of a capstone project in data science.
- Develop skills to select a relevant and feasible project topic aligned with personal interests and industry needs.
- Acquire techniques for data collection, preprocessing, exploratory data analysis, and feature engineering.
- Apply machine learning algorithms effectively and evaluate model performance rigorously.
- Master the documentation, visualization, and presentation of a data science project to communicate insights clearly.
Content Outline
PreviewUnit 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
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