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The rapid growth of high-throughput data, including -omics technologies, gave rise to significant demand for data science skills and experience with bioinformatics methods of analysis. This online training program will cover practical and conceptual aspects of data science, including data wrangling, statistical analysis, and machine learning in application to high-throughput biomedical omics data using big data analysis tools on the T-BioInfo Platform. Throughout the course, students will get an understanding of the opportunities and limitations of machine learning in the context of basic pre-clinical and clinical research.
The training is designed as a combination of online resources, practical assignments, and live workshops that will be conducted via ZOOM. Throughout the course, we will review several project examples that demonstrate the successes and limitations of conventional machine learning (ML) methods and deep learning (DL) using data from public repositories. To learn more, we welcome you to review the information on this page and register for an upcoming webinar where the program topics will be introduced and questions about the training will be answered by our team.
Register for Biomedical Data Science in R (Webinar and 1-month Mentor Guided Program)
Data Science methods to integrate multi omics data in the area of oncology & various disorders
Machine Learning View of Multi-Omics Data Integration
R & Python - Genomics & Next Generation Sequencing (NGS) Data Analysis - Dr. Harpreet Kaur

Choose a tenure that supports your needs and join a community of our program experts!
Payment checkout links (Biomedical Data Science in R)
Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.
We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)
+91-9876134120
Key Topics Covered:
Many types of omics data require step-by-step preparation, exploration, annotation, and visualization to understand. The T-BioInfo platform was designed for big multi-omics data analysis hiding the complexities of data with a user-friendly and intuitive interface that eliminates the need for coding and advanced machine learning algorithms for data integration and mining.
A program that embeds data-driven concepts into biological projects, spanning the student learning journey from observer to participant in research. Project-based learning for big data bioinformatics is to go beyond the theory with real datasets, projects, and expert mentors. Work with curated datasets from publicly available repositories with easy-to-follow tutorials.
Data processing for Next Generation Sequencing, Mass-Spectroscopy, Structural and phenotypic data. Build and adapt pipelines using similar approaches to data mapping, quantification, and annotation that are used to prepare data for downstream statistical analysis, train machine learning models and annotate features.
Online bioinformatics coding exercises to learn and explore R scripting and understand how to analyze and visualize -omics data to extract meaningful insights from large biological datasets. Learn, practice, and achieve bioinformatics greatness with concise exercises and interesting challenges right in the comfort of your browser!
A Complete Data Science Crash Course with Hands-on Training and curated Case Study Datasets | |||
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Data Wrangling and Exploratory Data Analysis |
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Statistical and Machine Learning Approaches |
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Coding Environments and Best Practices in R |
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Introduction to Case Studies using Research Omics Data |
Program Syllabus: Biomedical Data Science in R
Session Title | Description |
Introduction to the program
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Processing high throughput (BIG) data
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Major Types of Machine Learning Methods
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Machine Learning for Data visualization
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Unsupervised Learning: Clustering
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Supervised Learning: Classification
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Combining/selecting ML methods
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Selecting a project for ML analysis
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Register for the Online Mentor Guided Training Program
Payment checkout links (Biomedical Data Science in R)
Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.
We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)
+91-9876134120
A Complete Overview
Browser-based, hands-on experience with big data
Learn, practice and gain experience independent of your technological limitations, including:
- R and Python browser-based data analysis and code review'
- Process and Analyze large-scale Omics data
- Create reproducible workflows for data processing, analysis and integration
- Apply methods to curated datasets from peer-reviewed journals


