
Statistical Computing
Learn modern statistical computing techniques using R, Python, and other statistical software for data analysis and visualization.
Course Introduction
Statistical Computing introduces students to computational methods and software tools essential for modern data analysis. This course focuses on practical implementation of statistical techniques using R, Python, and other specialized software. Students will develop programming skills necessary for data manipulation, visualization, statistical modeling, and reproducible research.
Course Content
Module 1: Introduction to Statistical Computing
- Overview of statistical software packages
- Introduction to R programming environment
- Basic Python for data analysis
- Data structures and data types
- Script writing and reproducible research practices
Module 2: Data Management and Preprocessing
- Data import and export in various formats
- Data cleaning and validation techniques
- Data transformation and feature engineering
- Handling missing data
- Data merging and reshaping
Module 3: Data Visualization
- Principles of effective data visualization
- Creating static visualizations with ggplot2 (R) and Matplotlib/Seaborn (Python)
- Interactive visualizations with Plotly and Shiny
- Dashboard creation
- Visualization for different data types and relationships
Module 4: Statistical Modeling Implementation
- Linear and generalized linear models
- Regression diagnostics and model validation
- Resampling methods: bootstrap and cross-validation
- Regularization techniques
- Model selection and evaluation
Module 5: Advanced Topics and Applications
- Machine learning implementation in R and Python
- Time series analysis and forecasting
- Spatial data analysis
- Text mining and natural language processing basics
- Big data considerations and parallel computing
Assignments
- 1Data cleaning and preprocessing project
- 2Creating a comprehensive data visualization portfolio
- 3Statistical modeling and interpretation report
- 4Building an interactive dashboard with Shiny or Dash
- 5Reproducible research project with R Markdown or Jupyter Notebooks
Case Studies
Case Study 1
Predictive modeling for healthcare outcomes
Case Study 2
Market basket analysis for retail data
Case Study 3
Sentiment analysis of customer reviews
Case Study 4
Time series forecasting for financial data
Case Study 5
Spatial analysis of epidemiological data
Datasets
UCI Machine Learning Repository
Collection of datasets for various analytical tasks
Kaggle Datasets
Real-world datasets with analytical challenges
World Health Organization Data
Global health statistics for analysis
Financial Markets Data
Stock market and economic indicators
Recommended Textbooks
An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
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