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- Essentials of R Programming
Curriculum
- 7 Sections
- 50 Lessons
- 26 Weeks
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- Introduction to R ProgrammingThis chapter will introduce you to R as a powerful programming language for statistical computing and graphics. You'll learn why R is widely used in data science and what makes it a preferred tool for data analysis, machine learning, and research.7
- Data Structures in R5
- Data Visualization in RThis module introduces the fundamentals of data visualization using R. Students will explore core visualization libraries and learn to present data in visually compelling and informative ways.8
- 3.1Introduction to Data Visualization ConceptsCreating Bar Charts, Line Graphs, and Histograms
- 3.2Using Base R Plotting Functions
- 3.3Creating Bar Charts, Line Graphs, and Histograms
- 3.4Customizing Visuals: Themes, Colors, and Labels
- 3.5Introduction to ggplot2: Grammar of Graphics
- 3.6Creating Scatter Plots and Boxplots
- 3.7Saving and Exporting Plots
- 3.8Best Practices for Effective Visualizations
- Statistical Analysis in RThis module covers essential statistical techniques in R, equipping learners with tools to summarize, interpret, and analyze datasets quantitatively.8
- 4.1Descriptive Statistics with R (mean, median, sd)
- 4.2Data Distributions and Probability Basics
- 4.3Hypothesis Testing (t-tests, chi-square)
- 4.4Correlation and Covariance Analysis
- 4.5Linear Regression Basics
- 4.6ANOVA (Analysis of Variance)
- 4.7Visualizing Statistical Relationships
- 4.8Interpreting Statistical Results in Reports
- Working with Real-World DataLearners will gain hands-on experience in handling messy, real-world data—importing, cleaning, transforming, and analyzing datasets to draw meaningful insights.8
- Course SummaryThis module wraps up the entire course, highlighting key takeaways, reinforcing core concepts, and preparing learners for the final project.6
- Final Project and ReviewLearners will apply everything they've learned to a comprehensive project using a real dataset. This final module will assess their understanding and encourage independent exploration.8
Peer Review and Feedback
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