Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
R Programming for Data Analytics in Bioinformatics focuses R squarely on the analytics of biological and omics data. You learn to wrangle large bioinformatics datasets in R, apply the statistics that omics analysis requires, and use Bioconductor and related tools for genomic and expression analytics — with visualisation to communicate results. The emphasis is the data-analytics workflow for bioinformatics specifically. You finish able to run a bioinformatics data-analytics workflow in R. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers R programming for data analytics in bioinformatics — using R to analyse omics and biological datasets, from wrangling and statistics to bioinformatics workflows.
📋 Course Objectives
1. Wrangle large bioinformatics datasets in R.
2. Apply omics-appropriate statistics.
3. Use Bioconductor for genomic analytics.
4. Visualise bioinformatics results.
5. Build a reproducible analytics workflow.
👥 Who Should Enroll?
• Bioinformatics researchers and analysts
• Omics and molecular-biology scientists
• Data analysts in life science
• Students of bioinformatics
🚀 Key Learning Outcomes
• The ability to analyse bioinformatics data in R.
• An omics-analytics perspective.
• A reproducible R workflow.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of R Programming For Data Analytics In Bioinformatics and Core Biological Principles
Develop a comprehensive understanding of R programming fundamentals, including data types, operators, and control structures, to analyze bioinformatics data • Analyze genomic and proteomic data using R packages such as Bioconductor and genomics, to extract meaningful insights • Configure R environments, including setting up RStudio, installing packages, and managing dependencies, to ensure efficient data analysis workflows
Laboratory Techniques, Protocols, and Data Collection
Design and implement laboratory experiments, including PCR, sequencing, and microarray analysis, to generate high-quality bioinformatics data • Evaluate the quality and integrity of biological samples, including DNA, RNA, and protein, to ensure reliable data analysis • Optimize laboratory protocols, including data collection and storage, to minimize errors and ensure reproducibility
Bioinformatics Tools and Computational Analysis
Implement bioinformatics tools, including BLAST, GenBank, and UniProt, to analyze and interpret genomic and proteomic data • Analyze high-throughput sequencing data, including RNA-seq and ChIP-seq, to identify differential gene expression and regulatory elements • Develop and apply computational models, including machine learning and statistical algorithms, to predict biological outcomes and identify patterns in bioinformatics data
Research Methodology and Experimental Design
Design and develop research studies, including hypothesis testing and experimental design, to investigate biological questions and hypotheses • Evaluate the statistical power and sample size requirements of bioinformatics studies, including power analysis and sample size calculation • Develop and implement data validation and verification protocols, including data quality control and assurance, to ensure reliable research findings
Advanced R Programming For Data Analytics In Bioinformatics Applications and Translational Research
Develop and apply advanced R programming techniques, including data visualization and machine learning, to analyze and interpret complex bioinformatics data • Analyze and integrate multi-omics data, including genomics, transcriptomics, and proteomics, to identify biological insights and patterns • Design and implement data-driven approaches, including data mining and text mining, to extract meaningful insights from large-scale bioinformatics datasets
Regulatory Compliance, Bioethics, and Safety Standards
Evaluate and implement regulatory compliance protocols, including IRB and IACUC, to ensure ethical and responsible bioinformatics research • Develop and apply bioethics principles, including informed consent and data privacy, to protect human subjects and ensure responsible data sharing • Configure and implement safety standards, including laboratory safety and data security, to prevent accidents and ensure data integrity
Industry Applications, Career Pathways, and Case Studies
Develop and apply industry-relevant skills, including data analysis and interpretation, to drive business decisions and improve outcomes • Evaluate and pursue career pathways, including bioinformatics and data science, to apply R programming skills in real-world settings • Analyze and discuss case studies, including success stories and challenges, to illustrate the application and impact of R programming in bioinformatics
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
| Covered Tool / Platform | Bioconductor |
| Covered Tool / Platform | genomics |
| Covered Tool / Platform | TensorFlow |
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