Freelance Trainer for R Programming for Data Science Course

Freelance Trainer for R Programming for Datascience Course : Apply ONLY IF YOU ARE A TRAINER.

Course Overview : Today’s organizations no longer settle for data collection. They want evidence. They want accountability. They want actionable insights. Whether you are in government evaluating policy outcomes, in an NGO tracking donor-funded projects, or in the private sector optimizing operations, your ability to analyze and present data is directly tied to your credibility. This course transforms R programming from a daunting technical skill into a practical decision-making asset. You don’t need to become a statistician to succeed; you need structured guidance, hands-on practice, and a clear understanding of how to apply R in real-world settings. Through live coding, exercises, and applied case studies, you’ll learn how to clean messy datasets, explore patterns, run statistical tests, create predictive models, and build visualizations that tell compelling stories. By the end of the training, you will not only know R syntax, but you’ll also have the confidence to use R programming for data science in your own professional environment. You’ll be able to justify decisions with evidence, generate insights that influence strategies, and communicate findings in a way stakeholders trust. xplore patterns, run statistical tests, create predictive models, and build visualizations that tell compelling stories. By the end of the training, you will not only know R syntax, but you’ll also have the confidence to use R programming for data science in your own professional environment. You’ll be able to justify decisions with evidence, generate insights that influence strategies, and communicate findings in a way stakeholders trust.

Target Audience: This R Programming for Data Science Training is designed for:

• Data analysts and aspiring data scientists • Managers who want to interpret organizational data effectively • NGO professionals evaluating program outcomes • Finance professionals conducting forecasts and risk analysis • Public sector staff handling policy evaluation and impact studies • Researchers and academics modernizing their data analysis skills • HR and operations managers analyzing workforce data • Marketing and business intelligence professionals tracking campaigns • IT professionals supporting analytics initiatives • Anyone ready to transform data into decisions using R

Learning Objectives: This course equips you to analyze, visualize, and model data using R with confidence. By the end, you will be able to:

• Understand the fundamentals of R programming for data science • Import, clean, and manage complex datasets • Apply descriptive and inferential statistics using R • Create compelling data visualizations with ggplot2 • Conduct regression and predictive modeling • Work with time series and large datasets • Automate repetitive data workflows with scripts • Communicate insights through reproducible R Markdown reports Professional and Organizational Impact

When you master R for data science, you unlock the ability to transform data into opportunity. You will:

• Build in-demand technical skills in R programming Gain confidence in managing large and messy datasets • Reduce reliance on external consultants for data analysis • Strengthen your analytical credibility in professional settings • Enhance your strategic decision-making with evidence • Position yourself as a data-informed leader • Increase career competitiveness in data-driven roles

Organizations that embrace R-powered analysis operate smarter, faster, and with more accountability. They gain:

• Reliable and evidence-based insights for decision-making • Reduced costs by using an open-source alternative to expensive tools • Enhanced transparency and reproducibility of analyses • Stronger program evaluation and monitoring frameworks • Agility in forecasting and scenario modeling • Consistent and professional reporting systems • Improved collaboration across teams through shared R workflows

Training Methodology This is a hands-on, outcome-driven training designed to demystify R and make it practical. Expect: • Live coding sessions with step-by-step guidance Practical exercises on cleaning, analyzing, and visualizing data • Group projects applying R to real-world datasets • Visualization workshops using ggplot2 • Case studies from finance, NGOs, and public sector programs • Reflection prompts that connect R skills to your work context Templates and reusable scripts to take back to your organization

Course Outline

MODULE 1: INTRODUCTION TO R PROGRAMMING • Installing and navigating R and RStudio • Understanding objects, variables, and data structures • Vectors, matrices, lists, and data frames • Scripts vs. R Markdown for analysis • Writing your first R program

MODULE 2: IMPORTING AND CLEANING DATA • Reading CSV, Excel, and database files • Handling missing data and outliers • Data wrangling with dplyr • Recoding and transforming variables • Creating automated cleaning pipelines

MODULE 3: EXPLORATORY DATA ANALYSIS (EDA) • Descriptive statistics with R functions • Summarizing and profiling datasets • Identifying patterns, distributions, and outliers • Generating frequency and cross-tab tables • Case study: quick analysis of organizational survey data

MODULE 4: DATA VISUALIZATION WITH GGPLOT2 • Fundamentals of the grammar of graphics • Creating bar, line, and scatter plots • Visualizing distributions and relationships • Customizing themes, labels, and colors • Building dashboards with ggplot extensions

MODULE 5: STATISTICAL ANALYSIS WITH R • Hypothesis testing and p-values • Correlation and association measures • T-tests, chi-square, and ANOVA • Non-parametric methods for non-normal data • Applying inferential statistics to NGO project outcomes

MODULE 6: REGRESSION AND PREDICTIVE MODELING • Linear regression for trend analysis • Logistic regression for classification • Model assumptions and diagnostics • Predictive modeling in finance and operations • Communicating regression results to decision-makers

MODULE 7: WORKING WITH TIME SERIES DATA • Importing and structuring time series • Identifying seasonality and trends • Forecasting techniques with R • Case study: economic or financial forecasting • Automating recurring time series analysis

MODULE 8: BIG DATA AND INTEGRATION • Handling large datasets efficiently • Using R with databases and SQL • Accessing APIs for real-time data • Introduction to R with cloud computing platforms • Connecting R outputs with Excel or BI tools

MODULE 9: REPRODUCIBLE ANALYSIS WITH R MARKDOWN • Creating professional reports with code and outputs • Embedding tables, charts, and models • Automating recurring reports for managers • Customizing templates for organizations • Sharing reports with stakeholders

MODULE 10: CAPSTONE PROJECT: REAL-WORLD DATA CHALLENGE • Defining a problem relevant to your organization • Applying data import, cleaning, and analysis • Visualizing and modeling data with R • Presenting findings in R Markdown • Peer feedback and final reflections

Please apply with cv and photograph

Job Types: Part-time, Temporary, Contract
Contract length: 12 months

Pay: AED50.00 per hour

Expected hours: 40 per week