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ThinklabAcademy
ThinklabAcademy
Certificate of Completion

This is to certify that

Md. Tarikul Islam

has successfully completed the course

Basic to Advance R Programming
Issued on August 10, 2026  ·  Certificate ID: TLA-DS-2026-9268
Signature
Prof. Dr. Md. Kamrul Hasan
Course Instructor
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ThinklabAcademy
Verified Certificate

This certifies that

Md. Tarikul Islam

has successfully completed the course

Basic to Advance R Programming

Instructor
Prof. Dr. Md. Kamrul Hasan
Level
all
Issued On
August 10, 2026
Certificate ID
TLA-DS-2026-9268

Topics Completed

Resourses
  • ○ Resourses
Lesson 01 : Introduction || Installation || Interface
  • ✓ Introduction || Installation || Interface
Lesson 02 : R Basics: Working Directory Setup || Data Import and Exploration
  • ✓ R Basics: Working Directory Setup, Data Import, and Exploration
Lesson 03 : Visualization using ggplot2 || Preparation of riport || R in VS code
  • ✓ 1. Basic Plot
  • ○ 2. Adding a New Variable
  • ✓ 3. Data Visualization Using the ggplot() Function
  • ✓ 4. Visual Studio Code (VS Code): Introduction and Setup
  • ✓ 5. Central Limit Theorem in R || Publishing with RPubs
Lesson 04 : Preparation || Manipulation & Exploration in R
  • ✓ 1. Preparing Data for Analysis || How to clean data
  • ✓ 2. Coding categorization
  • ○ 3. Explore height variable
  • ○ 4. Using the Pipeing Operator in R
  • ○ 5. Using show() || is.na() || mutate() || Formula and Logical Operators
  • ○ 6. Select and Filter Function
  • ○ 7. Assignment
  • ○ 8. Assignment Answers
  • ○ 9. Checking Normality of Data
  • ○ 10. Visual check of Normality
  • ○ 11. Calculating Frequency in R
Lesson 05 : R for Experimental design || CRD || RCBD || Factorial || Split Plot
  • ○ 1. Experimental Design
  • ○ 2. Introduction to Social Research
Lesson 06 : t- test
  • ○ 1. Selecting the Appropriate Statistical Test
  • ○ 2. Understanding the t-Test
Lesson 07: ANOVA test
  • ○ 1. One way Anova
  • ○ 2. Effect of Treatment Replication and CV
  • ○ 3. eta aquare test
  • ○ 4. Post-Hoc Analysis
  • ○ 5. Homework
Lesson 08 : Post Hoc Analysis || HSD test || Visualization of ANOVA for journal articles
  • ○ 1. Continuation of Anova
  • ○ 2. HSD Test: Additive and Interactive Models
  • ○ 3. Visualizing Interaction
  • ○ 4. Post Hoc Analysis and Standard Error Calculation
  • ○ 5. Using the nlme and lme4 library in R
Lesson 09 : Chi-Square test & Correlation || Visualization of results
  • ○ 1. Mosaic Plot, chi test, Residual
  • ○ 2. Cramer's V
  • ○ 3. Conditions of chi sq test || Fisher's Test
  • ○ 4. cor.test || Correlation
  • ○ 5. Multivariate Correlation Coefffficient
  • ○ 6. Mixed Effect Model
Lesson 10 : Simple Regression || Visualization
  • ○ 1. Formula of Regression, Create Data set
  • ○ 2. Simple Regression
  • ○ 3. See All Residuals
  • ○ 4. Simple Correlation
  • ○ 5. Visualization
  • ○ 6. Model Diagonistic || Linearity of Relationship
  • ○ 7. Heteroskedasticity
  • ○ 8. Leverage (Cook's Distance)
  • ○ 9. Assumption of Linear Regression
Lesson 11 : Multiple Regression || Visualization
  • ✓ 1. Removing Observations with Missing Values, Loading Data, and Generating a Summary
  • ✓ 2. Introducing Multicullinearity
  • ✓ 3. Building a Multiple Regression Model
  • ✓ 4. Detecting Multicollinearity Using VIF
  • ✓ 5. Detecting Heteroskedasticity and Its Solutions
  • ✓ 6. Visualizing Regression Results in R
  • ✓ 7. Detecting and Solving Endogeneity Problems
  • ✓ 8. Pairs Plot
Lesson 12 : Logistic Regerssion || Cross-Validation || Bootstrapping
  • ✓ 1. Creating a Dataset for Logistic Regression and Fitting a Logistic Regression Model
  • ✓ 2. Fitting a Logistic Regression Model and Making Predictions
  • ✓ 3. Visualization
  • ✓ 4. Diagonistic Plots
  • ✓ 5. Resampling Bootstrapping, Cross-validation
  • ✓ 6. Defining a Bootstrap Function with 100 Bootstrap Iterations
  • ✓ 7. Bootstrap Results
  • ✓ 8. Cross Validation || Configure 10 fold CV
  • ✓ 9. Run Cross Validation
  • ✓ 10. Visualization
  • ✓ 11. ROC || AUC
Lesson 13 : PCA || Cluster Analysis
  • ✓ 1. Why use PCA
  • ✓ 2. Bartlett's Test and PCA Run
  • ✓ 3. Visualization
  • ✓ 4. PCA Rotation Reslut || Screeplot elbow plot
  • ✓ 5. Bi plot
  • ✓ 6. What Is Cluster Analysis?
  • ✓ 7. K-Means Clustering: A Simple Introduction
  • ✓ 8. Visualization
  • ✓ 9. Run Cluster Analysis || Visualization
  • ✓ 10. A. Heirarchial Clustering || Visualization
Lesson 14 : Exploratory Factory Analysis || Confirmatory Factory Analysis || Reliability & Validity
  • ✓ 1. What is Factor Analysis
  • ✓ 2. Sempot for Ploting
  • ✓ 3. Splitting Data into Two Halves and Cleaning the Split Data
  • ✓ 4. Determining the Number of Factors: Parallel Analysis, VSS, and Factor Retention Methods
  • ✓ 5. Correlation among Factors || Visualization
  • ✓ 6. Define S-factor Structure
  • ✓ 7. Fit the Confirmatory model, View, Summary, Vizualization, Workflow
  • ✓ 8. What is Reliability and Validity
  • ✓ 9. Describe Function|| Alpha Function || Omega Function
  • ✓ 10. KR 20
Lesson 15 : Path Analysis || SEM.R
  • ○ 1. Load Library || Load Data
  • ○ 2. Democracy Model || Fit the Path Model || Sum
  • ○ 3. Plot the Directional Path Diagram
  • ○ 4. Show the Result in Table
  • ○ 5. Extract and View Compiled Sum table || see all figures
  • ○ 6. Unified model || how to overcome
  • ○ 7. SEM
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