New Year Offer End Date: 30th April 2024
Program

R: Advanced Data Analytics for Life Sciences & Research Careers

Unlock the Power of R for Biological Data Analysis and Research Insights.

Skills you will gain:

About Program:

R is one of the most widely used programming languages in biological research due to its powerful statistical and visualization capabilities. From genomic data analysis to ecology, R enables scientists to perform data manipulation, statistical modeling, and complex visualizations to interpret and communicate results effectively. This workshop provides a hands-on approach to mastering R, focusing on its application in biological sciences, genomics, biostatistics, and bioinformatics.

Participants will be guided through data preprocessing, statistical testing, and the creation of advanced visualizations using R libraries such as ggplot2, dplyr, and Bioconductor. They will also be introduced to R Markdown for creating dynamic reports. By the end of the workshop, participants will be able to confidently analyze biological datasets, interpret results, and apply R to a range of research questions in life sciences.

Aim: R is one of the most widely used programming languages in biological research due to its powerful statistical and visualization capabilities. From genomic data analysis to ecology, R enables scientists to perform data manipulation, statistical modeling, and complex visualizations to interpret and communicate results effectively. This workshop provides a hands-on approach to mastering R, focusing on its application in biological sciences, genomics, biostatistics, and bioinformatics.

Participants will be guided through data preprocessing, statistical testing, and the creation of advanced visualizations using R libraries such as ggplot2, dplyr, and Bioconductor. They will also be introduced to R Markdown for creating dynamic reports. By the end of the workshop, participants will be able to confidently analyze biological datasets, interpret results, and apply R to a range of research questions in life sciences.

Program Objectives:

Learn the basics of R programming and its application to biological research.
Apply R libraries (ggplot2, dplyr, Bioconductor) for data manipulation and visualization.
Understand statistical analysis techniques in R for biological datasets.
Create reproducible reports and visualizations using R Markdown.
Gain proficiency in using R for genomic data analysis and bioinformatics applications.

What you will learn?

Day 1- Introduction to R and RStudio

  • Overview of R programming for researchers
  • Installation and setup of RStudio
  • R vs. other programming languages in research
  • RStudio IDE basics: Console, scripts, and workspace
  • Data Structures in R, Vectors, lists, data frames, and matrices
  • Organizing and analyzing biological data with R
  • Mathematical Operations for Biological Data
  • Basic mathematical operations in R & Statistical functions
  • Performing complex calculations with R
  • Hands-On: Practical examples from biological research data

Day 2 – Tools, File Handling & Data Transformation

  • Exploring data import/export, transforming datasets, and performing analytics for biological applications.
  • File Handling in R, Importing biological data files & Exporting data and results
  • Data validation and cleaning during import, Common pitfalls and best practices for file handling
  • Data Transformation & Analytics, Data manipulation using dplyr and tidyr packages
  • Filtering, sorting, reshaping, and merging data, Addressing missing values and inconsistencies in biological datasets
  • Real-time demo: Performing differential expression analysis
  • Understanding gene expression datasets & Visualizing results and interpreting p-values and fold changes
  • Hands-On: Differential Expression Analytics

Day 3 – Advanced Analytics, Visualization & Career Roadmap

  • Advanced data analysis techniques, visualization tools, and applying R in real-world research scenarios.
  • Differential Data Visualization, Visualizing research data: Volcano plots, heatmaps, PCA
  • Advanced plotting with ggplot2, Interpretation of visualizations for biological data insights
  • Multivariate Analytics, Introduction to multivariate analysis in biological research
  • Principal Component Analysis (PCA), clustering techniques
  • Hands-on example with biological datasets: grouping and interpreting data
  • Protein Abundance Analytics & Research Proposal Design
  • Quantitative analysis of protein expression and abundance
  • Hands-on: Working with protein data sets, Designing a research proposal with R-based analysis

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

Intended For :

Undergraduate/postgraduate degree in Biotechnology, Bioinformatics, Genetics, Molecular Biology, Computational Biology, or related fields.
Professionals working in biomedical research, data science, and bioinformatics sectors.
Data scientists or researchers with an interest in bioinformatics, genomics, and statistical analysis.
Individuals eager to learn R programming for data analysis in biological research.>

Career Supporting Skills

Bioinformatics Genomics StatisticalModeling DataAnalysis RProgramming Visualization ReproducibleResearch BioinformaticsTools Biostatistics GeneticAnalysis

Program Outcomes

Learn the basics of R programming and its application to biological research.
Apply R libraries (ggplot2, dplyr, Bioconductor) for data manipulation and visualization.
Understand statistical analysis techniques in R for biological datasets.
Create reproducible reports and visualizations using R Markdown.
Gain proficiency in using R for genomic data analysis and bioinformatics applications.

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FREEDOM TO LEARN 10% OFF All Courses & Workshops Use Code: NANOINDIA10 ⏳ Offer Ends In: Loading... Learn Today. Lead Tomorrow. Explore Programs →