Data & AnalyticsBeginner → IntermediateContent updated Oct 2026

Data Analysis Using R

From a raw questionnaire file to tested, charted, report-ready findings — data cleaning, descriptive and inferential statistics, all in R and RStudio.

Duration: Confirmed at enrollment
Structure: 7 modules
Level: Beginner → Intermediate
Mode: Physical or live online

About this course

Practical skills for using R and RStudio to import, clean, transform, analyse, visualise and interpret data. You work with common data formats — including SPSS files — and apply descriptive and inferential statistical techniques to real-world datasets.

Who this is for

Researchers, thesis students and faculty working with survey data
NGO, INGO and development-sector monitoring and evaluation staff
SPSS users who want a free, scriptable alternative
Analysts in health, banking, HR and market research

What you’ll be able to do

Work confidently in R and RStudio with scripts, packages and core data structures
Import and export Excel, CSV and SPSS data and check it on arrival
Clean, manage, merge and append datasets using keys and matching variables
Create calculated, recoded and categorised variables ready for analysis
Produce descriptive statistics, frequency tables and cross-tabulations
Choose and run t-tests, ANOVA, chi-square, correlation and regression, and interpret p-values and confidence intervals
Build clear charts in R and export tables, charts and results for reports
Produce a Quarto report that reruns on next month’s data with one click

Tools you’ll use

R
RStudio
R packages
Excel (.xlsx)
CSV
SPSS (.sav)
Quarto

Curriculum

The complete content, exactly as taught. Download it as a PDF.

Module 1R environment and data fundamentals15 topics

Introduction to R and RStudio

Introduction to R programming
RStudio interface and workspace
R scripts and the R console
Packages and libraries
Basic R syntax and data structures
Using an AI assistant to write and explain R code — and checking it

Understanding data

Introduction to data analysis
Sample questionnaire and dataset structure
Variables and observations
Data types and properties
Categorical and numerical variables

Working with data formats

Excel (.xlsx)
CSV (.csv)
SPSS (.sav)
Other commonly used data formats

Module 2Data import, cleaning and management17 topics

Data import and export

Importing Excel, CSV and SPSS data
Exporting processed datasets
Checking imported data

Data cleaning

Find and replace
Missing values
Identifying duplicate records
Removing duplicates
Data consistency checks

Data management

Create, read, update and delete (CRUD)
Sorting data
Selecting variables and records
Filtering data
Comparing datasets

Combining datasets

Merging datasets
Joining datasets
Appending / combining datasets
Understanding keys and matching variables

Module 3Data transformation and preparation12 topics

Calculations and formulas

Creating calculated variables
Applying formulas
Conditional calculations

Recoding data

Recoding categorical variables
Recoding numerical variables
Handling special / missing codes

Text and string processing

Cleaning text data
String manipulation
Extracting and modifying text

Data categorization

Creating categories
Grouping numerical values
Creating analysis-ready variables

Module 4Descriptive data analysis14 topics

Descriptive statistics

Mean
Median
Mode
Standard deviation
Minimum and maximum
Range
Quartiles

Frequency analysis

Frequency tables
Counts
Percentages
Proportions

Cross-tabulation

Two-way tables
Pivot-style summaries
Grouped summaries

Module 5Statistical analysis11 topics

Statistical concepts

Hypothesis testing
Null and alternative hypotheses
P-value
Confidence intervals
Statistical significance
Selecting an appropriate statistical test

Statistical tests

Independent and paired t-test
ANOVA
Chi-square test
Correlation analysis
Regression analysis

Module 6Data visualization and reporting19 topics

Visualization fundamentals

Principles of effective visualization
Choosing the appropriate chart
Understanding variables and visualization types

Charts and graphs

Bar chart
Histogram
Box plot
Pie chart
Line chart
Scatter plot
Other appropriate visualizations

Creating visualizations in R

Generate charts using R
Customise titles, labels and axes
Compare groups
Visualise distributions and relationships

Exporting results

Export charts
Export tables
Export analysis results
Prepare outputs for reports and presentations
Reproducible reports with Quarto (HTML, Word and PDF)

Module 7Applied data analysis project4 topics

End-to-end data analysis project

Raw data → import → cleaning → transformation → descriptive analysis → statistical analysis → visualization → interpretation → final output

Certification

Project completion
Practical assessment
Course completion / certification

Your project

Module 7 is an end-to-end project: raw data → import → cleaning → transformation → descriptive analysis → statistical analysis → visualization → interpretation → final output.

Where you’ll use this

Where Data Analysis Using R is applied
Application area	Typical applications
Business & corporate analytics	Sales analysis, customer analysis, business performance, market analysis
Research & survey analysis	Questionnaire data, survey cleaning, descriptive statistics, hypothesis testing
Banking & finance	Customer analysis, financial data analysis, risk-related analysis, reporting
Marketing analytics	Customer segmentation, campaign analysis, customer behaviour and survey analysis
Healthcare & public health	Patient datasets, health surveys, disease-related research, statistical analysis
Education & academic research	Student performance, surveys, assessments, research data and statistical testing
Government & development sector	Census / survey data, socioeconomic research, programme monitoring and evaluation
NGOs & INGOs	Survey analysis, project evaluation, beneficiary data, monitoring and reporting
Social science research	Demographic analysis, behavioural research, questionnaire analysis and hypothesis testing
Hospitality & tourism	Customer satisfaction, preferences, service-quality and tourism survey analysis
Manufacturing & operations	Quality analysis, production data, process performance and operational reporting
HR & people analytics	Employee surveys, workforce analysis, performance and retention-related analysis
Data-driven reporting	Automated analysis, statistical summaries, charts and report-ready outputs

Before you start

None. R, RStudio and the statistical concepts are all taught from the start. Familiarity with survey or spreadsheet data helps. A laptop you can install R and RStudio on.

Who teaches it

The course is designed and taught by data and AI practitioners — people who build data pipelines, dashboards and AI systems for businesses — so every exercise reflects how the tool is used on real work.

Certificate

Certification requires completing the project and passing a practical assessment.

Every certificate carries an ID that employers can check at trainingcenter.gexonai.com/verify.

Frequently asked questions

I use SPSS today. Is this for me?

Yes. You’ll import .sav files directly, and everything you do is saved as a script you can rerun on next month’s data.

Do I need to know statistics already?

No. Module 5 covers the concepts — hypotheses, p-values, confidence intervals and choosing the right test — before you run them.

How is the certificate awarded?

By completing the end-to-end project and passing a practical assessment.

Do you use AI tools in this course?

Yes, carefully. You’ll use an AI assistant to explain R errors and draft code, and learn to check its statistics before you trust them — a wrong test choice or a misread p-value is easy for AI to produce and hard to spot later.

What does it cost, and when is the next batch?

We share the fee and schedule when we confirm your batch. Register below and we’ll contact you within one working day — registering doesn’t commit you to anything.

Can my organisation send a group?

Yes. We run this course as a private batch for teams, and can build exercises around your own data. See corporate training.

Pairs well with

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