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Data Management and Statistical Data Analysis using R Training

Home Data Analysis Courses Data Management and Statistical Data Analysis using R Training

Introduction

Data Management and Statistical Data Analysis using R is a practical, professional training course designed to equip participants with the skills needed to manage, analyze, visualize, and interpret data using the R programming language. The course focuses on real-world data handling, statistical analysis, and reproducible research practices applicable across research, development, business, and policy environments.

Participants will learn how to import, clean, transform, analyze, and visualize data using R and RStudio. The course emphasizes applied statistical techniques, interpretation of results, and best practices in data management and reporting. By the end of the training, participants will be able to independently conduct data analysis projects using R.

Learning Objectives

By the end of this 5-day training, participants will be able to:

  • Understand the fundamentals of R and RStudio for data analysis.
  • Import, clean, and manage datasets from multiple sources.
  • Apply data transformation and manipulation techniques using R.
  • Perform descriptive and inferential statistical analyses.
  • Visualize data using professional-quality charts and graphics.
  • Apply regression and basic modeling techniques in R.
  • Interpret and communicate statistical outputs effectively.
  • Use reproducible research and reporting tools in R.
  • Apply best practices in data management and documentation

Important Dates
Classroom Training Calendar

START DATEEND DATEREGISTRATION
DEADLINE		COURSE COSTClick to Apply.
Jan 26, 2026Jan 30, 2026Jan 20, 2026USD 1000Register
Feb 23, 2026Feb 27, 2026Feb 18, 2026USD 1000Register
Mar 23, 2026Mar 27, 2026Mar 18, 2026USD 1000Register
Apr 27, 2026May 1, 2026Apr 21, 2026USD 1000Register
May 25, 2026May 29, 2026May 20, 2026USD 1000Register
June 29, 2026July 3, 2026June 22, 2026USD 1000Register
July 27, 2026July 31, 2026July 21, 2026USD 1000Register
Aug 24, 2026Aug 28, 2026Aug 19, 2026USD 1000Register
Sept 28, 2026Oct 2, 2026Sept 22, 2026USD 1000Register
Oct 26, 2026Oct 30, 2026Oct 20, 2026USD 1000Register
Nov 30, 2026Dec 4, 2026Nov 25, 2026USD 1000Register
Dec 21, 2026Dec 25, 2026Dec 16, 2026USD 1000Register

Online Training Calendar

START DATEEND DATEREGISTRATION DEADLINECOURSE COST
(Classroom)		Click to Apply.
Jan 5, 2026Jan 9, 2026Jan 1, 2026USD 500Register
Feb 16, 2026Feb 20, 2026Feb 10, 2026USD 500Register
Mar 2, 2026Mar 6, 2026Feb 26, 2026USD 500Register
Mar 30, 2026Apr 3, 2026Mar 25, 2026USD 500Register
May 18, 2026May 22, 2026May 12, 2026USD 500Register
June 29, 2026July 3, 2026June 23, 2026USD 500Register
July 20, 2026July 24, 2026July 15, 2026USD 500Register
Aug 31, 2026Sept 4, 2026Aug 25, 2026USD 500Register
Sept 21, 2026Sept 25, 2026Sept 16, 2026USD 500Register
Oct 19, 2026Oct 23, 2026Oct 14, 2026USD 500Register
Nov 30, 2026Dec 4, 2026Nov 25 2026USD 500Register
REGISTER HERE

Course Outline:

Day 1: Introduction to R and Data Management Fundamentals

Key Topics:

  • Introduction to data management and statistical analysis
  • Overview of R and RStudio environment
  • Installing and managing R packages
  • R syntax and basic programming concepts
  • Data types, objects, and structures in R
  • Importing data (Excel, CSV, SPSS, Stata, databases)
  • Understanding datasets and metadata

Practical Activities:

  • Hands-on setup of R and RStudio
  • Importing and exploring sample datasets

 

Day 2: Data Cleaning, Transformation, and Management

Key Topics:

  • Data quality concepts and common data issues
  • Handling missing data and outliers
  • Data cleaning techniques
  • Data transformation using dplyr and tidyr
  • Sorting, filtering, grouping, and summarizing data
  • Creating and recoding variables
  • Data validation and documentation

Practical Activities:

  • Cleaning a real-world dataset
  • Creating a clean, analysis-ready dataset

 

Day 3: Descriptive Statistics and Data Visualization

Key Topics:

  • Descriptive statistics (mean, median, variance, SD)
  • Frequency tables and cross-tabulations
  • Exploratory Data Analysis (EDA)
  • Data visualization principles
  • Creating plots using ggplot2
  • Bar charts, histograms, boxplots, scatterplots
  • Customizing and exporting graphics

Practical Activities:

  • Generating descriptive statistics
  • Designing publication-quality visualizations

 

Day 4: Inferential Statistics and Modeling in R

Key Topics:

  • Introduction to inferential statistics
  • Probability distributions and sampling
  • Hypothesis testing (t-tests, chi-square tests, ANOVA)
  • Correlation analysis
  • Simple and multiple linear regression
  • Model diagnostics and assumptions
  • Interpreting statistical outputs

Practical Activities:

  • Running hypothesis tests in R
  • Building and interpreting regression models

 

Day 5: Advanced Analysis, Reporting, and Reproducibility

Key Topics:

  • Introduction to generalized models (logistic regression)
  • Time series and basic forecasting (overview)
  • Working with large datasets
  • Reproducible research using R Markdown
  • Creating automated reports and dashboards
  • Exporting results and sharing outputs
  • Best practices in data management and ethics

Practical Activities:

  • Creating an R Markdown report
  • Capstone exercise: end-to-end data analysis project

 

Target Participants

This course is suitable for:

  • Data Analysts and Data Officers
  • Researchers and Academicians
  • Monitoring and Evaluation (M&E) Professionals
  • Statisticians and Economists
  • Public Health and Social Science Professionals
  • Development Practitioners and NGO Staff
  • Government and Policy Analysts
  • Graduate students and early-career professionals

Certification

Upon successful completion of the course, all participants will be issued a certificate of completion at no extra cost.

Tailor-Made Course
We can also do this as a tailor-made course to meet organizational-specific needs. Contact us to find out more: training@wise-gis.com

Group Discount

WE OFFER 10% – 20% DISCOUNT TO GROUPS OF MORE THAN 5 PARTICIPANTS.