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

1
Data cleaning and preprocessing project
2
Creating a comprehensive data visualization portfolio
3
Statistical modeling and interpretation report
4
Building an interactive dashboard with Shiny or Dash
5
Reproducible 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

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Contact for Enrollment

Data|Statistics|Research|Consultancy