Data|Statistics|Research|Consultancy

Statistical Computing

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

Recommended Textbooks

R for Data Science

by Hadley Wickham and Garrett Grolemund

View Book Details

Python for Data Analysis

by Wes McKinney

View Book Details

Statistical Computing with R

by Maria L. Rizzo

View Book Details

An Introduction to Statistical Learning

by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

View Book Details

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