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R Programming, Python, Statistical Analysis & Data Science for Research Training Course

60 Lessons

About This Course

The R Programming, Python, Statistical Analysis & Data Science for Research Training Course is a premium, competency-based programme designed to equip researchers, data analysts, scientists, academics, postgraduate students, and development professionals with advanced competencies in R programming, Python programming, statistical analysis, data science, scientific computing, machine learning, artificial intelligence (AI), predictive analytics, data visualization, reproducible research, and research automation. Participants develop practical expertise in data management, statistical modelling, exploratory data analysis, programming, automation, dashboard development, AI-assisted coding, cloud computing, and research workflows using modern open-source technologies. Through hands-on coding laboratories, real-world research datasets, multidisciplinary case studies, cloud-based analytics, and capstone projects, participants gain the capability to analyse complex data, build reproducible analytical pipelines, develop predictive models, communicate scientific evidence, and support evidence-based decision-making across health, agriculture, food systems, environmental science, business, government, and international development.

What You’ll Learn

  • Develop proficiency in R and Python programming for data management, statistical computing, automation, reproducible research, and scientific analysis.
  • Apply advanced statistical analysis, data science, and machine learning techniques to analyse complex datasets and generate reliable evidence for research and decision-making.
  • Build reproducible analytical workflows using R Markdown, Quarto, Jupyter Notebook, Git, and modern research computing tools to improve transparency and collaboration.
  • Create interactive data visualizations, dashboards, predictive models, and AI-assisted analytical solutions that communicate complex findings effectively to technical and non-technical audiences.
  • Lead digital research and analytics projects by integrating programming, artificial intelligence, cloud computing, statistical modelling, and data-driven innovation across multidisciplinary sectors.

Course Curriculum

12 modules  ·  60 lessons

  • Introduction to R Programming and Python Programming
  • Programming Fundamentals for Data Science
  • Development Environments (RStudio, VS Code and Jupyter Notebook)
  • Data Structures, Objects and Programming Logic
  • Reproducible Research and Scientific Computing Principles
  • Importing and Exporting Data from Multiple Sources
  • Data Cleaning, Transformation and Feature Engineering
  • Data Wrangling Using dplyr, tidyr and pandas
  • Programming Functions, Loops and Automation
  • Managing Large and Complex Research Datasets
  • Exploratory Data Analysis (EDA)
  • Descriptive Statistics and Data Summarization
  • Probability Distributions and Statistical Inference
  • Data Visualization Using ggplot2, matplotlib and plotly
  • Publication-Quality Figures and Graphics
  • Hypothesis Testing and Confidence Intervals
  • Correlation, Regression and ANOVA
  • Generalized Linear Models and Mixed Models
  • Survival Analysis and Longitudinal Data Analysis
  • Advanced Statistical Programming Workflows
  • Introduction to Machine Learning
  • Supervised and Unsupervised Learning
  • Predictive Modelling and Model Evaluation
  • Feature Selection and Model Optimization
  • Artificial Intelligence Applications in Research
  • Advanced Data Visualization Principles
  • Interactive Dashboards Using Shiny, Dash and Plotly
  • Executive Reporting and Data Storytelling
  • KPI Development and Performance Monitoring
  • Interactive Research Communication
  • R Markdown and Quarto for Automated Reporting
  • Jupyter Notebook Workflows
  • Git, GitHub and Collaborative Coding
  • FAIR Data Principles and Open Science
  • Research Documentation and Workflow Management
  • SQL Fundamentals for Data Science
  • Connecting R and Python to Databases
  • APIs and Automated Data Collection
  • Cloud Computing Using Google Colab and Cloud Platforms
  • Big Data Processing and Scalable Analytics
  • Deep Learning Fundamentals
  • Natural Language Processing (NLP)
  • AI-Assisted Coding and Prompt Engineering
  • Time Series Analysis and Forecasting
  • Geospatial Analytics and Multidisciplinary Applications
  • Public Health and Epidemiological Data Analysis
  • Agriculture, Food Systems and Environmental Analytics
  • Business, Finance and Market Analytics
  • Monitoring, Evaluation and Impact Analytics
  • Research Decision Support Systems
  • Generative Artificial Intelligence (GenAI)
  • AI Agents and Intelligent Research Assistants
  • Cloud-Native Analytics and MLOps
  • Digital Research Infrastructure and High-Performance Computing
  • Future Skills for Data Scientists and Research Professionals
  • Developing an End-to-End Data Science Workflow
  • Building a Reproducible Statistical Analysis Pipeline
  • Designing Machine Learning and Predictive Analytics Models
  • Creating Interactive Dashboards and Executive Reports
  • Executive Capstone Presentation: Data Science, Programming and Evidence-Based Decision Support System

Who Should Attend

  • Researchers, academics, postgraduate students, and research supervisors conducting quantitative research.
  • Data analysts, statisticians, biostatisticians, and data scientists working with research or operational datasets.
  • Public health, agriculture, food systems, environmental, engineering, and life science professionals performing advanced data analysis.
  • Monitoring, evaluation, learning (MEL), GIS, and development professionals using data for evidence-based decision-making.
  • Government agencies, NGOs, consulting firms, and private sector organizations implementing data-driven programmes and digital transformation.

Prerequisites

  • The course is suitable for beginners seeking programming skills as well as experienced professionals wishing to strengthen competencies in R, Python, statistical analysis, data science, machine learning, and research computing.

Key Benefits

  • Master R programming, Python programming, statistical analysis, and data science using internationally recognized analytical workflows and open-source technologies.
  • Develop practical expertise in statistical modelling, machine learning, artificial intelligence (AI), predictive analytics, and research automation for scientific and organizational excellence.
  • Build reproducible research pipelines, interactive dashboards, and publication-quality visualizations that improve analytical quality and research transparency.
  • Strengthen competencies in cloud computing, version control, collaborative coding, and AI-assisted programming, preparing for modern digital research environments.
  • Enhance career opportunities in research, academia, healthcare, agriculture, government, finance, consulting, and international development through globally relevant programming and analytics skills.

Delivery Technique

  • Expert-led coding masterclasses integrating R, Python, statistics, and data science.
  • Hands-on programming laboratories using real-world multidisciplinary datasets.
  • Interactive coding challenges, machine learning workshops, and data visualization exercises.
  • Collaborative problem-solving projects supported by expert mentoring and peer review.
  • Executive capstone project involving complete analytical workflow development, predictive modelling, and research reporting.