Course Content

Part 1: Data Types & Structures

1. Data Types

   • Character

   • Numeric

   • Integer

   • Logical

   • Complex

2. Data Structures

   • Vector

   • Factor

   • Matrix

   • Data Frame

   • List

Part 2: Reading, Downloading & Installing Data & Packages

1. Reading & importing data from various sources

2. Downloading biological datasets from UCSC and Pepper

3. Installing R packages from CRAN, GitHub, and Bioconductor

Part 3: Data Visualization

1. Creating different types of plots in R

   • Bar Plots, Histograms, Scatter Plots, Box Plots

   • Density Plots, Violin Plots, Line Plots

   • PCA (Principal Component Analysis), Heatmaps

Part 4: Data Transformation & Programming in R

1. Data manipulation & transformation techniques

   • Filtering, Selecting, Arranging, Mutating, Summarizing

   • Spreading, Gathering, Separating, and Pulling data

2. Relational Data & Joins

   • Left/Right Join

   • Semi/Anti Join

   • Inner/Full Join

3. Programming Basics

   • Writing Functions

   • Iteration (Loops & Apply functions)

Part 5: Basic Statistical Inference

1. Descriptive Statistics

   • Measures of Centrality

   • Variance, Standard Deviation

2. Probability & Sampling

   • Normal Distribution

   • Standard Error

   • Sampling Variability

3. Resampling Techniques

   • Bootstrapping

   • Virtual Resampling

   • Confidence Intervals

4. Hypothesis Testing Methods

   • T-Test

   • ANOVA (Analysis of Variance)

   • Chi-Square Test

Part 6: Introduction to Bioconductor & Genomic Data Analysis

1. Introduction to Bioconductor, a powerful tool for analyzing genomic data

   • Genomic Annotation & Gene Ontology (GO) Analysis

   • Multiple Sequence Alignment & Phylogenetic Tree Construction

   • Quality Control for Sequencing Reads (Trimming & Filtering Reads)

   • Working with Genomic Data in R:

     • Introduction to GenomicRanges package

     • Understanding the Summarized Experiment Object

Who Should Enroll?

• - Beginners wanting to learn R from scratch.

• - Researchers & Biologists working with genomic data.

• - Data Scientists interested in bioinformatics applications.

• - Students in life sciences, computational biology, or bioinformatics.

• No prior programming knowledge is required—just a passion for learning and analyzing biological data!