Basic Data Analysis Course on R for Students & Young Researchers

The Bangladesh Research Society (BDRS) is pleased to announce the “Basic Data Analysis Course on R for Students & Young Researchers”, a practical, research-oriented training program designed to develop participants’ foundational and applied skills in R Programming, Statistical Analysis, Data Visualization, and Research Data Management.

In contemporary academic research, proficiency in data analysis and statistical programming has become increasingly important for students, thesis researchers, and early-career researchers. R is an open-source statistical programming language and a widely used research platform for quantitative research, survey data analysis, thesis and dissertation research, statistical modelling, data visualization, and reproducible research workflows.

This course has been designed not merely to introduce participants to R as a software tool, but to develop the ability to work with research data through the complete analytical process—from data preparation and cleaning to statistical analysis, visualization, interpretation, modelling, and research-oriented reporting.

About the Course

The course will provide a structured introduction to the R Programming Language and RStudio environment, followed by practical applications in research data analysis and statistical modelling.

Participants will work with research-oriented datasets and practical exercises to develop a clear understanding of how data can be managed, analyzed, visualized, interpreted and presented for academic and research purposes. The training will follow a hands-on and project-based learning approach, enabling participants to apply the concepts learned in real or simulated research contexts.

Course Objectives

By completing this course, participants will be able to:

  • Develop a foundational understanding of R Programming and RStudio.
  • Import, export, manage, clean, and preprocess research datasets.
  • Perform descriptive and inferential statistical analyses using R.
  • Generate appropriate and informative research data visualizations.
  • Conduct correlation, hypothesis testing, regression, and ANOVA.
  • Understand and apply selected statistical modelling techniques.
  • Perform basic time-series, forecasting, and panel-data analysis.
  • Assess statistical model assumptions and diagnostics.
  • Apply structured statistical workflows to research datasets.
  • Present and communicate analytical findings in a research-oriented manner.

Course Contents

Module 01: R Programming & RStudio

  • Introduction to R and RStudio
  • R environment and workspace
  • Basic R syntax and programming concepts
  • Objects, functions, operators, and packages
  • Working with scripts and reproducible workflows

Module 02: Data Import, Export & Management

  • Importing data into R
  • Exporting datasets and analytical outputs
  • Working with CSV, Excel and other common data formats
  • Data structures and basic data management
  • Variable and dataset organization

Module 03: Data Types & Variable Management

  • Numeric, character, logical and categorical data
  • Factors and variable coding
  • Missing values
  • Variable transformation and recoding

Module 04: Data Cleaning & Preprocessing

  • Identifying and handling missing data
  • Detecting inconsistent and invalid values
  • Data transformation
  • Filtering, sorting and restructuring datasets
  • Preparing datasets for statistical analysis

Module 05: Descriptive Statistics & Frequency Analysis

  • Measures of central tendency
  • Measures of dispersion
  • Frequency distributions
  • Cross-tabulation
  • Summary statistics
  • Exploratory Data Analysis (EDA)

Module 06: Data Visualization

  • Principles of research data visualization
  • Bar charts, histograms, boxplots and scatterplots
  • Distribution and relationship visualization
  • Advanced visualization using ggplot2
  • Presenting publication-oriented analytical graphics

Module 07: Correlation & Association Analysis

  • Pearson and Spearman correlation
  • Association between variables
  • Correlation matrices
  • Interpretation and reporting

Module 08: Hypothesis Testing

  • Research hypotheses and statistical hypotheses
  • Parametric and non-parametric testing
  • t-tests and related procedures
  • Interpretation of statistical significance
  • Effect size and practical interpretation

Module 09: Linear Regression Analysis

  • Simple and multiple linear regression
  • Model estimation and interpretation
  • Regression coefficients
  • Model fit and explanatory power
  • Interpretation of regression outputs

Module 10: Logistic Regression & Classification

  • Introduction to logistic regression
  • Binary outcome modelling
  • Odds and odds ratios
  • Model interpretation
  • Basic classification concepts

Module 11: ANOVA & Post-hoc Analysis

  • One-way ANOVA
  • Group comparison
  • Post-hoc testing
  • Assumption assessment
  • Interpretation and reporting

Module 12: Time Series Analysis & Forecasting

  • Introduction to time-series data
  • Trends and seasonality
  • Time-series visualization
  • Basic forecasting concepts
  • Practical forecasting applications

Module 13: Panel Data Analysis

  • Introduction to panel datasets
  • Structure of panel data
  • Basic panel-data concepts
  • Practical analytical workflow

Module 14: Statistical Model Diagnostics & Assumption Testing

  • Model assumptions
  • Residual diagnostics
  • Multicollinearity
  • Normality and homoscedasticity
  • Model evaluation and interpretation

Module 15: Applied Data Analysis & Statistical Workflows

  • Research-oriented analytical workflow
  • Selecting appropriate statistical methods
  • From research question to statistical analysis
  • Interpreting analytical results
  • Organizing outputs for academic research

Module 16: Capstone Data Analysis Project

  • Research dataset selection
  • Data preparation
  • Statistical analysis
  • Visualization
  • Interpretation of findings
  • Research-oriented presentation of results

Final Session: Review, Q&A & Live Project

  • Comprehensive course review
  • Participant questions and discussion
  • Live data-analysis exercise
  • Project presentation and feedback

Learning Format & Course Benefits

Participants enrolled in the program will receive:

  • 16+ Live and Recorded Interactive Sessions
  • 3 Practical Projects with Assignments
  • Hands-on experience with research datasets
  • Project-Based Learning Approach
  • Practical training in research-oriented data analysis
  • Classes conducted three days per week
  • Certificate of Completion
  • Performance Recognition for outstanding participants

 

The course is designed to balance conceptual understanding with practical application, enabling participants to develop skills that can be directly applied to thesis research, academic projects, survey research, quantitative studies, and other research activities.

Class Schedule

Class Days: Saturday, Monday & Wednesday
Time: 10:00 PM – 11:00 PM
Time Zone: Bangladesh Standard Time (BST)

Registration Information

CategoryRegistration Fee
BDRS Society MembersBDT 900
Non-MembersBDT 1,500

 

Registration Deadline: 20 August 2026

Payment Information

Payment Method: Send Money

bKash / Nagad: 01917558417

Participants are requested to follow the payment instructions carefully.

Important: Participants must mention their own name in the Reference field while making the payment.

After completing the payment, participants must submit the Registration Form to confirm their participation in the course.

Who Should Join?

The course is particularly suitable for:

  • Undergraduate and Graduate Students
  • Thesis and Dissertation Researchers
  • Early-Career Researchers
  • Academic Professionals
  • Quantitative Research Enthusiasts
  • Students interested in research data analysis
  • Individuals interested in learning R Programming and Statistical Analysis
  • Researchers seeking practical skills for academic data analysis

 

No advanced programming background is required. The course is structured to gradually introduce participants to R and research-oriented statistical analysis through practical learning.

Why Learn R for Research?

R is more than a statistical software package. It is a comprehensive open-source data analysis ecosystem that supports a wide range of research activities, including:

Data Management → Data Cleaning → Statistical Analysis → Data Visualization → Regression Modelling → Forecasting → Research Reporting

Its flexibility, extensive package ecosystem, reproducibility, and suitability for advanced statistical analysis make R a valuable tool for students and researchers working with quantitative data.

Developing practical skills in R can help researchers improve their research workflow, analytical capacity, data interpretation, and academic research skills.

Contact

Email: bdresearchsociety@gmail.com

For registration-related queries and further information, participants may contact the Bangladesh Research Society through the official email address.