Basic Data Analysis Course on R for Students & Young Researcher
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 & WednesdayTime: 10:00 PM – 11:00 PMTime Zone: Bangladesh Standard Time (BST) Registration Information Category Registration Fee BDRS Society Members BDT 900 Non-Members BDT 1,500 Registration Deadline: September 25, 2026 Register Now Who Is Eligible for BDRS Membership Benefits? The Bangladesh Research Society (BDRS) extends its membership benefits to students, researchers, and academic enthusiasts who have recently participated in BDRS activities or are members of affiliated university-based research societies and research clubs. You may avail BDRS Member Benefits if you meet any one of the following criteria: 1. Recent BDRS Participants If you have participated in any course, training, workshop, or academic program organized by BDRS within the last 12 months, your valid certificate, confirmation document or proof of participation will be recognized for BDRS membership benefits. 2. Members of Affiliated Research Societies & Research Clubs Members of the following research societies and research clubs are also eligible for BDRS Member Benefits: Bangladesh Research Society (BDRS) Dhaka University Research Society (DURS) Rajshahi University Research Society (RURS) BUP Research Society Barishal University Research Society (BURS) JKKNIU Research