Biology – AI-Powered Learning
Discover. Decode. Transform
Course Objective
This stream explores how AI is transforming modern biology into a data-driven, predictive, and scalable science. Through modules on genomics, protein modeling, drug discovery, neuroscience, immunology, and more, learners will gain hands-on knowledge of AI’s applications across the life sciences.
By merging biological knowledge with intelligent systems, this course equips learners to analyze complex datasets, model biological systems, and uncover new insights in health, ecology, and biotechnology.
Whether you are a student, researcher, healthcare professional, or innovator, this program will teach you how to decode biological data, optimize research workflows, and accelerate discoveries using AI.
Intro to AI in Biology
AI for Molecular Simulations
What it’s about
This module introduces how AI helps simulate the behavior of molecules, proteins, and chemical reactions. Instead of spending months in a physical lab, learners will see how digital models predict interactions quickly.
What you will learn
- Basics of how molecular structures are represented digitally.
- How AI predicts bonding, folding, and reactions.
- The link between biology, chemistry, and computation.
What the output will be
By the end, you’ll generate a simple AI-assisted simulation that predicts how a molecule behaves in different conditions.
What you can do after completing it
Use these skills to understand drug discovery, design small experiments virtually, and interpret molecular interactions without advanced lab resources.
Intro to AI in Biology
Programming for Biology Data
What it’s about
Here, you’ll explore the role of coding in biology — from DNA sequences to protein databases. The module focuses on how AI reads and organizes biological data for analysis.
What you will learn
- How to process DNA and protein sequences digitally.
- Organizing biological datasets for machine learning.
- Basics of automating repetitive biological tasks.
What the output will be
You’ll create a simple program that reads biological data (like DNA letters or protein chains) and produces organized results ready for AI analysis.
What you can do after completing it
Apply your knowledge to handle biological datasets, assist in research projects, or automate parts of your coursework and lab analysis.
Intro to AI in Biology
AI Models for Biological Predictions
What it’s about
This module focuses on how machine learning models predict outcomes in biology — such as disease risk, protein structures, or genetic variations.
What you will learn
- Core ideas behind training AI models on biological data.
- Understanding input (like genetic sequences) and output (like disease likelihood).
- Basics of evaluating accuracy and reliability in predictions.
What the output will be
You’ll build a simple predictive model that takes sample biological data and forecasts a potential outcome.
What you can do after completing it
Apply AI to make predictions in your studies or projects, such as modeling health risks, genetic markers, or crop yield improvements in agriculture biology.
Intro to AI in Biology
Cloud-Based AI Labs for Biology
What it’s about
This module introduces virtual lab spaces where AI and biology meet. You’ll learn how to run biology experiments online using cloud environments, making research accessible from anywhere.
What you will learn
- How cloud notebooks support biological AI projects.
- Uploading, cleaning, and analyzing biology data remotely.
- Collaborating on biology experiments virtually.
What the output will be
You’ll create a shared, cloud-based notebook containing your first AI-driven biology experiment.
What you can do after completing it
Work with peers or mentors on online projects, access powerful AI tools without needing expensive hardware, and contribute to open-source biology research.
Intro to AI in Biology
Exploring Real Biological Data with AI
What it’s about
This final module gives you hands-on exposure to real biological datasets — like cell images, tissue structures, or gene expression profiles. You’ll see how AI visualizes and analyzes them.
What you will learn
- How large biological datasets are collected and shared.
- Techniques to visualize cells, proteins, and tissues.
- How AI spots patterns in images and sequences that humans might miss.
What the output will be
You’ll build a small project where AI visualizes and interprets real biological samples (like cell structures).
What you can do after completing it
Use open biological datasets for your own projects, join global challenges in cell biology or genetics, and contribute insights to scientific discussions with evidence-based AI outputs.
Genomics & DNA Analysis
Exploring Genomes with Digital Maps
What it’s about
This module introduces digital genome maps that allow you to explore DNA like a road atlas. You’ll learn how researchers organize, visualize, and compare genetic information across species.
What you will learn
- How DNA sequences are stored and accessed digitally.
- Understanding genes, chromosomes, and variations.
- How to navigate genome databases for biological insights.
What the output will be
You’ll create a simple genome “map view” highlighting the location of a gene of interest and its role in a biological process.
What you can do after completing it
Use genome browsers to explore genetic traits, study evolutionary differences, or connect classroom biology with real-world genetic data.
Genomics & DNA Analysis
Visualizing DNA Data in Detail
What it’s about
Here you’ll dive into interactive visual tools that show DNA, RNA, and protein data at a fine-grained level. Think of it as a microscope for genetic data.
What you will learn
- How to upload and view sequencing results.
- Ways to detect mutations, structural variations, and gene expression.
- Basics of comparing healthy vs. diseased genetic samples.
What the output will be
You’ll generate a visual snapshot of DNA sequences, marking key mutations or patterns.
What you can do after completing it
Apply your skills to analyze sequencing experiments, support research in genetics, or interpret DNA results for educational projects.
Genomics & DNA Analysis
Learning Genomics Through Guided Workflows
What it’s about
This module focuses on beginner-friendly genomic analysis platforms designed like learning playgrounds. They use step-by-step pipelines to help you understand how DNA is processed and interpreted.
What you will learn
- Basics of DNA barcoding and identifying species.
- How guided workflows teach sequencing, alignment, and annotation.
- Linking raw DNA data to real biological questions.
What the output will be
You’ll complete a guided workflow and produce a report showing the identification of a sample’s DNA and its closest relatives.
What you can do after completing it
Use such workflows to learn DNA analysis at school or college level, design small experiments (like identifying plants or microbes), and build confidence before moving into advanced genomics.
Genomics & DNA Analysis
Running Genomics Experiments in the Cloud
What it’s about
This module introduces powerful online platforms where you can analyze DNA data without installing anything. They allow you to run complex bioinformatics workflows entirely in the cloud.
What you will learn
- How to upload and manage DNA sequencing datasets.
- Running pipelines for genome assembly, annotation, or expression analysis.
- Collaborating on large-scale projects with shared workflows.
What the output will be
You’ll build your first cloud-based workflow that processes a DNA dataset and produces an annotated output.
What you can do after completing it
Take part in collaborative research, join open-data projects, or analyze DNA datasets from public repositories — all without high-end computers.
Genomics & DNA Analysis
Advanced Genome Browsing & Comparative Genomics
What it’s about
This final module explores comprehensive genome browsers that integrate data from multiple projects worldwide. It’s like a global library for DNA analysis.
What you will learn
- How large-scale genome projects are organized.
- Tools for comparing genomes across species.
- How scientists track genetic variations linked to diseases.
What the output will be
You’ll create a comparative visualization showing differences between two genomes, highlighting evolutionary or medical insights.
What you can do after completing it
Use global genomic resources for research, prepare presentations on genetic discoveries, or contribute to community science projects on human health, plants, or animals.
Protein Structure & Modeling
Predicting Protein Shapes with AI
What it’s about
Proteins are the building blocks of life, but their 3D shapes determine how they actually work. In this module, you’ll learn how AI can predict protein structures from just their amino acid sequences.
What you will learn
- Why protein folding is essential for biological functions.
- How AI “reads” sequences and predicts 3D shapes.
- How structure prediction links to drug design and disease research.
What the output will be
You’ll generate a predicted 3D model of a simple protein and visualize its folded structure.
What you can do after completing it
Apply these skills to explore how protein mutations affect health, support lab research with computational models, or contribute to open science challenges in protein structure.
Protein Structure & Modeling
Visualizing Proteins in 3D
What it’s about
This module focuses on interactive visualization. You’ll explore proteins in three dimensions — rotating, zooming, and coloring them to reveal their secrets.
What you will learn
- How to load and explore protein files.
- Understanding secondary structures like helices and sheets.
- Highlighting active sites and binding pockets.
What the output will be
You’ll create a 3D image of a protein, showing its key structural features.
What you can do after completing it
Use protein visualization to prepare class presentations, research posters, or explain biological concepts more effectively.
Protein Structure & Modeling
Advanced Molecular Exploration
What it’s about
This module takes protein exploration to the next level by combining interactive visuals with computational analysis. You’ll learn how to measure distances, compare structures, and simulate interactions.
What you will learn
- Comparing different conformations of the same protein.
- Measuring molecular distances and bond angles.
- Exploring docking and ligand binding visually.
What the output will be
You’ll produce a comparative analysis between two protein structures, highlighting differences in shape or binding sites.
What you can do after completing it
Use advanced exploration to support structural biology projects, design small research studies, or collaborate on molecular docking work in drug discovery.
Protein Structure & Modeling
Building Protein Models from Scratch
What it’s about
Sometimes experimental data is incomplete. This module teaches you how to build protein models by filling gaps, assembling fragments, and refining shapes.
What you will learn
- Basics of homology modeling (building new structures using known ones).
- Understanding templates and alignments.
- Refining and validating models for accuracy.
What the output will be
You’ll construct a modeled version of a protein using templates and generate a validation report.
What you can do after completing it
Apply these skills to predict structures for newly discovered proteins, assist in functional annotation, or build models for student projects in molecular biology.
Protein Structure & Modeling
Designing and Engineering Proteins
What it’s about
This final module goes beyond observation into innovation. You’ll learn how researchers design or modify proteins to create new functions — a foundation for synthetic biology.
What you will learn
- How proteins can be redesigned for stability or efficiency.
- Principles of computational protein design.
- Basics of predicting how changes affect folding and function.
What the output will be
You’ll design a simple protein variant and evaluate how it might behave compared to the original.
What you can do after completing it
Contribute to projects in enzyme engineering, synthetic biology, or even design novel proteins for industrial, environmental, or medical applications.
Molecular Docking & Drug Design
Introduction to Molecular Docking
What it’s about
This module introduces the idea of “docking” — predicting how small molecules (like potential drugs) fit into proteins. Think of it as testing how keys fit into locks at the molecular level.
What you will learn
- The basics of protein–ligand interactions.
- Why docking is essential in drug discovery.
- How computers simulate binding between molecules.
What the output will be
You’ll perform a simple docking run showing how a small molecule binds to a protein’s active site.
What you can do after completing it
Understand how researchers identify promising drug candidates, support coursework with visual examples, and explain docking in presentations or reports.
Molecular Docking & Drug Design
Virtual Screening for Drug Candidates
What it’s about
Here you’ll learn how to test many molecules at once — a process called virtual screening. Instead of trying one by one in the lab, AI speeds up the process by predicting which molecules are most likely to work.
What you will learn
- How to prepare multiple molecules for screening.
- Ranking results to identify the strongest binders.
- The concept of scoring functions in docking.
What the output will be
You’ll generate a ranked list of molecules, highlighting the top candidate for binding to a chosen protein.
What you can do after completing it
Apply these skills to mimic real drug discovery pipelines, contribute to class or research projects, and gain experience in computational chemistry workflows.
Molecular Docking & Drug Design
Preparing and Converting Molecules
What it’s about
Before docking, molecules must be in the right format. This module focuses on preparing proteins and ligands — cleaning, converting, and optimizing their digital structures.
What you will learn
- Basics of molecular file formats.
- Converting structures between different representations.
- Optimizing molecules for docking analysis.
What the output will be
You’ll prepare a clean protein structure and a ligand ready for docking experiments.
What you can do after completing it
Assist labs or peers in preparing datasets, troubleshoot issues with incompatible formats, and ensure smooth workflows in computational biology projects.
Molecular Docking & Drug Design
Cloud-Based Docking Workflows
What it’s about
This module introduces cloud platforms that let you perform docking online without powerful local computers. You’ll run a docking experiment through an easy-to-use web interface.
What you will learn
- Uploading molecules to cloud servers.
- Running docking jobs remotely.
- Interpreting online docking results and reports.
What the output will be
You’ll generate a docking report with predicted binding poses and binding energy values.
What you can do after completing it
Run docking projects even with limited hardware, collaborate with others remotely, and integrate results into research assignments or publications.
Molecular Docking & Drug Design
User-Friendly Docking Tools
What it’s about
Finally, you’ll explore simplified docking platforms designed for beginners. These tools automate complex steps so you can focus on understanding the results.
What you will learn
- How user-friendly platforms simplify docking workflows.
- Interpreting docking poses visually.
- Recognizing key protein–ligand interactions like hydrogen bonds.
What the output will be
You’ll create a visual docking model showing a ligand bound inside the protein’s active site.
What you can do after completing it
Confidently explain docking results in class or research presentations, create figures for projects, and begin exploring drug design ideas without needing advanced programming.
Bioinformatics Data Mining
Mining Biological Datasets
What it’s about
This module introduces how scientists work with huge biological datasets — from gene expression to protein activity. You’ll learn how AI helps organize and analyze them to reveal hidden patterns.
What you will learn
- Basics of biological data formats and structures.
- How to filter and preprocess large-scale datasets.
- Identifying meaningful patterns from noisy data.
What the output will be
You’ll prepare a cleaned dataset, ready for further biological analysis.
What you can do after completing it
Handle real biological datasets in research projects, contribute to open-data science initiatives, or support lab work by preparing data for advanced analysis.
Bioinformatics Data Mining
Accessing Public Gene Expression Repositories
What it’s about
Thousands of gene expression experiments are freely available online. This module teaches you how to tap into these repositories and retrieve useful biological information.
What you will learn
- How large repositories store experimental results.
- Steps to extract data for specific genes, tissues, or diseases.
- Basics of analyzing expression levels across samples.
What the output will be
You’ll download and summarize a gene expression dataset focused on a condition of your choice.
What you can do after completing it
Compare healthy vs. diseased samples in studies, create mini research projects, or practice analyzing real experimental data without stepping into a wet lab.
Bioinformatics Data Mining
Understanding Biological Pathways
What it’s about
This module explores how genes and proteins work together in pathways — the “networks” that control life processes like metabolism, immunity, and growth.
What you will learn
- Basics of biological pathways and how they’re represented.
- How to map genes to known pathways.
- How disruptions in pathways connect to diseases.
What the output will be
You’ll create a visual map of a pathway showing how specific genes and proteins interact.
What you can do after completing it
Explain complex biological processes in class or research, identify pathway changes in disease studies, and build pathway diagrams for presentations
Bioinformatics Data Mining
Visualizing Networks of Biological Data
What it’s about
Biology isn’t just about individual molecules — it’s about networks. This module shows you how to visualize complex interactions in easy-to-understand network diagrams.
What you will learn
- How biological networks (genes, proteins, pathways) are represented visually.
- Basics of network analysis like hubs and connections.
- How visualization simplifies big data exploration.
What the output will be
You’ll generate a network diagram showing relationships between genes or proteins.
What you can do after completing it
Use network visualization in research posters, explain systems biology to peers, or explore hidden relationships in large datasets.
Bioinformatics Data Mining
Exploring Protein–Protein Interactions
What it’s about
This final module focuses on how proteins talk to each other. Since no protein works alone, understanding these interactions is critical for biology and medicine.
What you will learn
- Basics of protein–protein interaction networks.
- How to search for known interactions in databases.
- How AI predicts possible new interactions.
What the output will be
You’ll build an interaction map showing how multiple proteins connect and function together.
What you can do after completing it
Investigate disease mechanisms, identify potential drug targets, or contribute to collaborative studies that rely on protein interaction insights.
Microscopy & Image Analysis
Basics of Biological Image Processing
What it’s about
This module introduces how raw microscopy images (cells, tissues, or organisms) are processed digitally. You’ll explore how brightness, contrast, and noise affect biological observations.
What you will learn
- Fundamentals of digital image processing.
- Adjusting brightness, contrast, and filters for clarity.
- Why preprocessing is essential before analysis.
What the output will be
You’ll produce a cleaned and enhanced image of a biological sample, ready for deeper analysis.
What you can do after completing it
Improve clarity in your microscopy images, prepare them for class presentations, or support lab work by producing publication-ready visuals.
Microscopy & Image Analysis
Automating Cell Image Analysis
What it’s about
This module focuses on how AI automates repetitive tasks like counting cells, measuring their shapes, or tracking growth across time-lapse images.
What you will learn
- How to segment and identify individual cells in images.
- Measuring cell features (size, shape, intensity).
- Automating analysis across large batches of images.
What the output will be
You’ll generate a dataset of measurements (e.g., cell counts, sizes) from a collection of images.
What you can do after completing it
Apply automation to analyze large biological experiments, assist in disease research (like tumor size detection), or save hours of manual counting in lab projects.
Microscopy & Image Analysis
Advanced Biological Image Workflows
What it’s about
Here, you’ll learn how integrated platforms combine many image processing tools into one workflow — from image acquisition to analysis and visualization.
What you will learn
- How modular workflows streamline image analysis.
- Applying multiple steps like filtering, segmentation, and quantification.
- Customizing pipelines for different microscopy studies.
What the output will be
You’ll create a workflow pipeline and apply it to analyze a sample biological image set.
What you can do after completing it
Design your own image analysis workflows, compare multiple experiments consistently, and adapt pipelines for diverse research needs.
Microscopy & Image Analysis
Pathology Image Analysis
What it’s about
This module explores how digital pathology turns microscope slides into large images that AI can analyze — helping detect diseases and study tissues more efficiently.
What you will learn
- Basics of whole-slide imaging and digital pathology.
- Techniques for analyzing tissue samples.
- How AI supports diagnosis and biomarker discovery.
What the output will be
You’ll generate a digital annotation of a tissue image, highlighting key regions of interest.
What you can do after completing it
Assist in research on cancer and other diseases, create annotated datasets for medical training, or explore careers in digital pathology and biomedical imaging.
Microscopy & Image Analysis
Interactive 3D & AI-Powered Insights
What it’s about
This final module introduces interactive visualization platforms where biological images can be explored in 3D and enriched with AI-driven insights.
What you will learn
- Basics of multi-dimensional image visualization (2D, 3D, time-lapse).
- How AI highlights patterns invisible to the human eye.
- Combining biological images with quantitative data.
What the output will be
You’ll produce an interactive visualization of a cell or tissue dataset with annotated insights.
What you can do after completing it
Explore large, complex datasets in 3D, create engaging visuals for research communication, and apply AI-powered insights to real biological discoveries.
AI for Ecology & Biodiversity
Community Science & Species Identification
What it’s about
This module introduces how everyday people contribute to biodiversity studies by photographing plants, animals, and insects. AI then helps identify species and build global databases.
What you will learn
- Basics of community-driven biodiversity monitoring.
- How AI recognizes species from images.
- Why citizen science is crucial for conservation.
What the output will be
You’ll create a digital log of identified species from images and observations.
What you can do after completing it
Join global biodiversity projects, contribute to conservation efforts, and apply AI-powered identification in field trips or personal studies.
AI for Ecology & Biodiversity
Organizing & Retrieving Ecological Data
What it’s about
Ecological data often comes in messy forms — scattered across reports and formats. This module shows how specialized systems organize and retrieve it for analysis.
What you will learn
- How ecological datasets (climate, soil, species counts) are structured.
- Ways AI simplifies cleaning and preparing these datasets.
- How organized data supports reliable ecological studies.
What the output will be
You’ll extract and organize an ecological dataset, preparing it for further analysis.
What you can do after completing it
Support environmental projects with well-structured data, streamline biodiversity research, or design student projects using real-world ecological datasets.
AI for Ecology & Biodiversity
Exploring Global Biodiversity Databases
What it’s about
This module focuses on global biodiversity libraries that collect millions of records from around the world — showing where species are found and how they’re changing.
What you will learn
- Basics of species occurrence records.
- How to search for distribution data of specific organisms.
- How large-scale data supports ecology and conservation.
What the output will be
You’ll generate a distribution map of a chosen species using global biodiversity data.
What you can do after completing it
Study species ranges for school or research, contribute to conservation reports, or analyze biodiversity trends across regions.
AI for Ecology & Biodiversity
Mapping Life Across the Planet
What it’s about
Here you’ll see how AI integrates maps, environmental data, and species records to create a “map of life” — showing where organisms live and where they may be threatened.
What you will learn
- How maps combine with biodiversity data.
- Basics of habitat modeling and predicting species ranges.
- How mapping informs conservation planning.
What the output will be
You’ll create a habitat map predicting where a species is likely to thrive.
What you can do after completing it
Apply habitat maps in research or conservation projects, explain biodiversity threats with evidence, or design classroom activities on species distribution.
AI for Ecology & Biodiversity
Monitoring Birds with AI
What it’s about
Birds are key indicators of ecosystem health. This module introduces how AI helps monitor bird populations through sound recordings, sightings, and community data.
What you will learn
- Basics of bird monitoring and migration tracking.
- How AI identifies species from calls and sightings.
- Why birds are crucial for studying climate change and ecology.
What the output will be
You’ll generate a bird diversity report for a region using community and AI-driven data.
What you can do after completing it
Contribute to global bird monitoring projects, support eco-tourism initiatives, or link bird studies to broader ecological research.
Systems Biology Simulation
Simulating Biological Pathways
What it’s about
This module introduces the idea of simulating biochemical pathways — like metabolism, signaling, or gene regulation — using mathematical models. You’ll see how equations can represent life processes.
What you will learn
- Basics of systems biology and pathway modeling.
- How reactions are translated into equations.
- Simulating how concentrations of molecules change over time.
What the output will be
You’ll build a simple simulation of a metabolic pathway and produce time-course graphs of molecule levels.
What you can do after completing it
Apply simulations to test “what-if” scenarios (e.g., drug effects), support research projects, or explain complex biology in classrooms with visual models.
Systems Biology Simulation
Designing Biological Networks
What it’s about
Here, you’ll learn how to design complex diagrams of biological networks — turning messy data into clear visual models that AI can simulate.
What you will learn
- Basics of drawing and structuring biological networks.
- Connecting reactions, feedback loops, and regulators.
- Turning diagrams into computational models.
What the output will be
You’ll design a digital diagram of a signaling or metabolic network and prepare it for simulation.
What you can do after completing it
Communicate biological processes visually, prepare network diagrams for research reports, or collaborate with teams by sharing clear system maps.
Systems Biology Simulation
Modeling Rules and Interactions
What it’s about
Biological systems are complex — with countless molecules interacting. This module shows how rule-based modeling can simplify the process by defining interaction rules instead of drawing every possibility.
What you will learn
- What rule-based modeling is and why it matters.
- How to define interaction rules between molecules.
- Predicting large networks from a small set of rules.
What the output will be
You’ll create a rule-based model of a protein–protein interaction system and run a basic simulation.
What you can do after completing it
Handle larger, more realistic biological systems in your studies, design simplified models for complex processes, and practice modern approaches to systems biology.
Systems Biology Simulation
Virtual Cell Environments
What it’s about
This module explores digital cell environments where you can simulate entire processes inside a cell — from signal transmission to spatial diffusion.
What you will learn
- Basics of cell-level simulations.
- How spatial modeling adds depth to systems biology.
- Linking cell behavior to pathway activity.
What the output will be
You’ll run a simulation of a cellular signaling pathway inside a virtual cell environment.
What you can do after completing it
Use virtual cells to test drug effects, explore cellular dynamics, or demonstrate experiments in education without wet-lab costs.
Systems Biology Simulation
Flexible Modeling & Experimentation
What it’s about
This final module focuses on flexible platforms where you can script, modify, and extend simulations — giving you full control over system design.
What you will learn
- Basics of scripting biological models.
- Running multiple simulations with different parameters.
- Comparing outcomes to understand sensitivity and robustness.
What the output will be
You’ll create and test variations of a systems biology model, generating comparison plots of results.
What you can do after completing it
Design custom experiments for research projects, explore “virtual labs” for hypothesis testing, and contribute simulations to collaborative science communities.
AI in Neuroscience
Modeling Neurons and Neural Morphology
What it’s about
This module introduces how digital models of neurons are created — capturing their shapes, branches, and connections. These models form the basis for studying how the brain processes information.
What you will learn
- Basics of neuron anatomy and digital representation.
- How AI helps analyze neuron morphology.
- Why neuron shape matters for connectivity and function.
What the output will be
You’ll generate a digital reconstruction of a neuron, showing its dendrites and axons.
What you can do after completing it
Use these models to study brain circuits, simulate neural activity, or explain brain function in educational projects.
AI in Neuroscience
Standardizing Brain Imaging Data
What it’s about
Brain research produces massive amounts of MRI, EEG, and other imaging data. This module teaches how standardized formats organize such data so AI can analyze it consistently.
What you will learn
- Basics of brain imaging file formats.
- Why standardized structures are essential for sharing data.
- How AI uses well-organized data for reliable results.
What the output will be
You’ll prepare a brain imaging dataset in a standardized format, ready for analysis.
What you can do after completing it
Access and work with open brain imaging studies, collaborate with labs more effectively, or apply consistent practices in your own neuroscience projects.
AI in Neuroscience
Analyzing Brain Signals
What it’s about
This module dives into brain signal analysis — from EEG waveforms to complex neural oscillations. AI helps detect patterns that reveal how the brain thinks, learns, or reacts.
What you will learn
- Basics of brain signals (EEG, MEG).
- How to preprocess signals for clarity.
- Detecting patterns linked to cognition, sleep, or movement.
What the output will be
You’ll create a time-frequency plot of brain activity, showing meaningful rhythms.
What you can do after completing it
Apply signal analysis to projects in psychology or neuroscience, practice reading brainwaves, or explore how AI supports mental health and brain–computer interfaces.
AI in Neuroscience
Visualizing Brain Images in 3D
What it’s about
Brains are complex structures. This module shows how imaging data (like MRI or fMRI scans) can be viewed and explored in 2D and 3D for deeper understanding.
What you will learn
- Basics of brain imaging visualization.
- Navigating slices and 3D reconstructions.
- Highlighting regions linked to functions or diseases.
What the output will be
You’ll generate a 3D visualization of brain imaging data, highlighting specific regions of interest.
What you can do after completing it
Create brain visuals for reports or presentations, explore structural differences in healthy vs. diseased brains, or support neuroscience research with visual insights.
AI in Neuroscience
Accessing and Sharing Open Neuroscience Data
What it’s about
This final module focuses on open platforms that provide brain imaging and signal datasets for free, enabling collaboration and discovery worldwide.
What you will learn
- How open neuroscience repositories work.
- Searching for datasets by brain region, condition, or modality.
- Basics of uploading and sharing data for collaboration.
What the output will be
You’ll download a sample brain imaging dataset and prepare a mini-analysis report.
What you can do after completing it
Contribute to global neuroscience projects, practice with real-world brain data, or explore mental health and cognition studies with AI support.
Single Cell & Transcriptomics
Understanding Single-Cell Data Landscapes
What it’s about
This module introduces single-cell analysis, where instead of studying whole tissues, you zoom in to see what each individual cell is doing. You’ll learn how AI clusters cells into groups based on gene activity.
What you will learn
- Basics of single-cell transcriptomics.
- How to handle raw gene expression data.
- Clustering cells to identify distinct populations.
What the output will be
You’ll create a simple 2D visualization (like a scatterplot) where similar cells cluster together, revealing hidden patterns.
What you can do after completing it
Identify cell types in tissues, explore disease vs. healthy samples, or analyze developmental stages cell by cell.
Single Cell & Transcriptomics
Large-Scale Transcriptomics with AI
What it’s about
Single-cell datasets are massive — often thousands of cells with thousands of genes each. This module shows how AI reduces complexity, finds structure, and makes the data easier to understand.
What you will learn
- Dimensionality reduction for high-dimensional data.
- Visualizing large datasets without losing key details.
- Identifying rare but important cell populations.
What the output will be
You’ll generate a visual map showing how cells relate to one another across a large dataset.
What you can do after completing it
Work with big single-cell datasets confidently, contribute to collaborative projects, or explore gene activity at scale.
Single Cell & Transcriptomics
Tracing Cell Development and Lineage
What it’s about
Cells don’t just exist — they change, divide, and develop over time. This module teaches you how to trace these trajectories, showing how one cell type matures into another.
What you will learn
- Basics of cell lineage tracing with transcriptomics.
- How to order cells along “pseudotime.”
- Identifying key transition points in development.
What the output will be
You’ll create a developmental pathway map showing how cells evolve from one state to another.
What you can do after completing it
Study developmental biology, explore how stem cells turn into specialized cells, or investigate how diseases alter cell fates
Single Cell & Transcriptomics
Exploring Public Single-Cell Repositories
What it’s about
This module introduces platforms where researchers share single-cell datasets. These repositories make it possible to practice analysis without generating your own data.
What you will learn
- Basics of public single-cell data portals.
- How to search for datasets by tissue, condition, or organism.
- Accessing interactive visualization dashboards.
What the output will be
You’ll download a public dataset and create a simple report of findings.
What you can do after completing it
Practice analysis on real-world data, join global research projects, and apply insights from shared single-cell studies to your own work.
Single Cell & Transcriptomics
Interactive Data Exploration and Sharing
What it’s about
Finally, you’ll learn how to explore massive single-cell datasets interactively, filter them by cell type or gene, and share results with collaborators.
What you will learn
- Interactive visualization of cells and gene expression.
- Filtering by markers to identify specific populations.
- Sharing findings with peers through interactive platforms.
What the output will be
You’ll create an interactive dashboard where others can explore your analyzed single-cell dataset.
What you can do after completing it
Present single-cell results in class or research, collaborate more effectively, and communicate insights to both scientific and non-scientific audiences.
AI for Plant Biology
Image-Based Plant Analysis
What it’s about
This module introduces how AI analyzes plant images to measure growth, leaf size, or stress symptoms — all without manual observation.
What you will learn
- Basics of digital plant phenotyping.
- How AI detects traits like color, shape, and texture.
- Using image data to monitor plant health.
What the output will be
You’ll generate a digital analysis report of plant images showing measurable traits (e.g., leaf area or growth rate).
What you can do after completing it
Apply AI image analysis to monitor crops, assist in agriculture research, or develop tools for sustainable farming practices.
AI for Plant Biology
Open Science in Plant Biology
What it’s about
This module explores community-driven platforms for sharing genetic parts, designs, and plant research. It introduces the idea of open-source biology for plants.
What you will learn
- Basics of open biological resources.
- How researchers share standardized plant data and genetic parts.
- Why openness accelerates plant innovation.
What the output will be
You’ll design and document a simple plant biology project using shared resources.
What you can do after completing it
Collaborate on plant science globally, explore synthetic biology projects, and use open resources to innovate in plant research
AI for Plant Biology
Exploring Plant Genomes
What it’s about
Plants have large and complex genomes. This module introduces genomic libraries where researchers study plant DNA to understand growth, stress resistance, and evolution.
What you will learn
- Basics of plant genome databases.
- How to search for genes linked to traits (like drought tolerance).
- How AI helps compare plant species genetically.
What the output will be
You’ll generate a gene list linked to a specific plant function or trait.
What you can do after completing it
Apply genomic insights to crop improvement projects, understand plant evolution, or support agriculture biotechnology research.
AI for Plant Biology
Comparative Plant Genomics
What it’s about
This module dives deeper into comparing plant species. By aligning their genomes, you’ll learn how scientists discover evolutionary links and key traits.
What you will learn
- Basics of comparative genomics in plants.
- How to align genomes to find similarities and differences.
- How comparative data supports breeding programs.
What the output will be
You’ll create a comparative chart showing genetic differences between plant species.
What you can do after completing it
Support agricultural planning, explore genetic diversity, or apply knowledge to sustainable food systems research.
AI for Plant Biology
Collaborative Plant Science Platforms
What it’s about
This final module introduces cloud-based collaborative environments where researchers store, share, and analyze plant biology data together.
What you will learn
- How collaborative plant science platforms function.
- Basics of storing and analyzing large plant datasets online.
- How teams use shared spaces to accelerate discovery.
What the output will be
You’ll contribute to a shared workspace with plant data, documenting findings in a collaborative format.
What you can do after completing it
Join global agriculture and plant science projects, share your results with peers, and contribute to solving food security and sustainability challenges.
Computational Evolutionary Biology
Building Evolutionary Trees
What it’s about
This module introduces phylogenetics — the study of evolutionary relationships. You’ll learn how DNA and protein sequences can be compared to build “family trees” of species.
What you will learn
- Basics of sequence alignment and comparison.
- How trees represent evolutionary history.
- Why tree-building is key for studying evolution.
What the output will be
You’ll generate a simple evolutionary tree showing relationships between species based on sample sequences.
What you can do after completing it
Create phylogenetic trees for class projects, explore ancestry of organisms, or interpret evolutionary links for research assignments.
Computational Evolutionary Biology
Dating Evolutionary Events
What it’s about
This module focuses on estimating when events occurred in evolutionary history. You’ll learn how algorithms use genetic changes as “clocks” to measure time.
What you will learn
- Basics of molecular clock models.
- How to estimate divergence times between species.
- The role of probability and uncertainty in dating events.
What the output will be
You’ll create a dated evolutionary tree with approximate timelines of species divergence.
What you can do after completing it
Study the origins of species, estimate the age of evolutionary events, or add timelines to phylogenetic research projects.
Computational Evolutionary Biology
Analyzing Large Phylogenetic Datasets
What it’s about
Big datasets with hundreds or thousands of sequences can be challenging. This module introduces high-performance methods for handling such large-scale phylogenetic analyses.
What you will learn
- Basics of large dataset alignment.
- How algorithms manage complex evolutionary models.
- Why speed and efficiency matter in computational biology.
What the output will be
You’ll run a phylogenetic analysis on a large sequence dataset and generate a tree optimized for speed and accuracy.
What you can do after completing it
Handle larger projects in evolutionary biology, work with genomic-scale data, or support labs analyzing massive genetic datasets.
Computational Evolutionary Biology
Exploring Probabilistic Models of Evolution
What it’s about
This module dives into statistical models that estimate the likelihood of different evolutionary trees. Instead of one fixed answer, you’ll explore many possible scenarios.
What you will learn
- Basics of Bayesian inference in evolutionary biology.
- How probabilities help choose the best tree.
- Why multiple tree hypotheses matter in research.
What the output will be
You’ll generate a set of possible evolutionary trees and evaluate their probabilities.
What you can do after completing it
Critically assess evolutionary results, explain uncertainty in science, or contribute to advanced phylogenetics projects.
Computational Evolutionary Biology
Summarizing and Interpreting Trees
What it’s about
Once many trees are generated, researchers need clear summaries. This module shows how to combine outputs into a single, easy-to-interpret evolutionary picture.
What you will learn
- Basics of summarizing tree sets.
- Identifying consensus branches and shared patterns.
- Creating annotated evolutionary timelines.
What the output will be
You’ll produce a consensus evolutionary tree with annotations showing support values and time estimates.
What you can do after completing it
Present evolutionary trees in research papers, support conservation biology projects with evolutionary insights, or explain species history to broader audiences.
AI-Powered Laboratory Automation
Automating Experiments with Robotics
What it’s about
This module introduces robotic systems that can pipette, mix, and run experiments with high precision. Instead of doing repetitive lab tasks by hand, AI and robotics ensure accuracy and speed.
What you will learn
- Basics of robotic liquid handling.
- How AI improves reproducibility in lab experiments.
- Why automation reduces errors and saves time.
What the output will be
You’ll design a digital workflow for an automated experiment (e.g., preparing samples or running a PCR setup).
What you can do after completing it
Apply automation to research, reduce manual errors, and set up lab experiments that can run with minimal human supervision.
AI-Powered Laboratory Automation
Robotics Frameworks for Biology
What it’s about
This module focuses on how robotics frameworks manage lab automation hardware. You’ll learn how AI helps coordinate robotic arms, sensors, and devices to work together.
What you will learn
- Basics of robotic coordination in lab settings.
- How sensors and actuators communicate through frameworks.
- How AI optimizes robotic workflows.
What the output will be
You’ll simulate a simple robotic workflow where machines handle tasks like transferring samples or analyzing data.
What you can do after completing it
Design robotics-assisted lab processes, collaborate with engineers, and apply robotics thinking to biological workflows.
AI-Powered Laboratory Automation
Smart Connected Lab Devices
What it’s about
Modern lab instruments don’t work in isolation — they connect online for real-time monitoring and control. This module shows how AI helps researchers track experiments remotely.
What you will learn
- Basics of connected lab devices and dashboards.
- How AI monitors and analyzes experiment data in real time.
- Why cloud connectivity makes labs more efficient.
What the output will be
You’ll generate a digital dashboard summarizing results from a simulated connected lab experiment.
What you can do after completing it
Remotely monitor experiments, improve collaboration in multi-site labs, and use smart dashboards for decision-making in real time.
AI-Powered Laboratory Automation
Cloud-Based Lab Automation
What it’s about
This module explores cloud laboratories — facilities where experiments can be run remotely using AI-driven instructions. You design the experiment, and robots perform it for you.
What you will learn
- Basics of cloud-based lab operations.
- How to send and track experimental workflows remotely.
- How AI ensures quality control in cloud labs.
What the output will be
You’ll submit a digital workflow for a cloud experiment and receive a report with the simulated results.
What you can do after completing it
Run experiments without being physically present in a lab, scale research quickly, and explore collaborations with global cloud labs.
AI-Powered Laboratory Automation
Managing Digital Lab Records with AI
What it’s about
Every experiment generates data, and keeping it organized is essential. This module introduces digital lab notebooks that AI helps manage — making research more searchable and sharable.
What you will learn
- Basics of electronic lab notebooks (ELNs).
- How AI organizes and tags lab data automatically.
- Best practices for collaboration and reproducibility.
What the output will be
You’ll create a structured digital lab record of a simulated experiment with automated tagging and organization.
What you can do after completing it
Maintain organized lab notebooks, collaborate with peers more effectively, and ensure compliance with modern research standards.
AI in Immunology
Exploring Immune Data Repositories
What it’s about
This module introduces large immunology databases where clinical and experimental data is stored. These repositories allow researchers to explore how the immune system behaves under different conditions.
What you will learn
- Basics of immune-related datasets (cells, proteins, responses).
- How AI helps mine data for patterns in immunity.
- The importance of shared data for vaccine and therapy research.
What the output will be
You’ll retrieve and summarize an immune dataset, identifying trends such as responses to infection or vaccination.
What you can do after completing it
Use immune databases for coursework, support immunology research projects, or explore how AI connects data to health outcomes.
AI in Immunology
Analyzing Immune Receptor Diversity
What it’s about
The immune system relies on diverse receptors to recognize countless pathogens. This module focuses on analyzing the variety and patterns of these receptors.
What you will learn
- Basics of immune receptor repertoires (T-cell and B-cell receptors).
- How AI detects diversity, clonality, and expansion.
- The role of receptor analysis in disease and treatment monitoring.
What the output will be
You’ll generate a diversity profile showing patterns of immune receptors in a dataset.
What you can do after completing it
Study how immune repertoires shift during infection, evaluate therapy effects, or explore adaptive immunity in real data.
AI in Immunology
Standardizing Immune Genetics
What it’s about
Immunology depends on complex genetic information about antibodies and receptors. This module teaches how standardized references help researchers interpret this complexity.
What you will learn
- Basics of immune gene databases.
- How genetic sequences of antibodies and receptors are classified.
- How AI uses standardized references for accurate analysis.
What the output will be
You’ll classify sample receptor sequences and generate a standardized annotation.
What you can do after completing it
Work with immune genetics in research, compare receptor sequences globally, or contribute to antibody engineering projects.
AI in Immunology
Integrating Single-Cell and Immune Data
What it’s about
This module connects single-cell sequencing with immune profiling, revealing how immune cells behave individually.
What you will learn
- Basics of combining transcriptomics with immune marker detection.
- How AI distinguishes different immune cell subtypes.
- Why this integration matters for understanding disease responses.
What the output will be
You’ll produce a cell-type map showing immune cell populations within a tissue sample.
What you can do after completing it
Explore immune cell diversity in diseases like cancer or infections, apply single-cell methods to immunology projects, and link cell states to therapy responses.
AI in Immunology
Collaborative Immunology Research Platforms
What it’s about
This final module highlights cloud-based platforms where scientists share, analyze, and visualize immune data together.
What you will learn
- Basics of collaborative immunology platforms.
- How immune datasets are standardized for team research.
- How AI supports large-scale immune monitoring projects.
What the output will be
You’ll generate a collaborative project summary using immune data, including visualizations of immune responses.
What you can do after completing it
Join global immunology collaborations, contribute to vaccine and therapy studies, and share insights on immune health with broader communities.
AI for Microbiology
Understanding Microbial Communities
What it’s about
This module introduces how scientists study entire microbial ecosystems — not just individual microbes. You’ll learn how DNA sequencing and AI reveal which microbes live in a sample and what roles they play.
What you will learn
- Basics of microbiome research.
- How sequencing identifies bacteria, fungi, and viruses.
- The role of AI in clustering and classifying microbial communities.
What the output will be
You’ll generate a community profile showing which microbes are present in a given dataset.
What you can do after completing it
Apply microbiome analysis to gut health studies, soil ecosystems, or water quality research.
AI for Microbiology
Processing Microbial Sequencing Data
What it’s about
Raw sequencing data needs careful cleaning and processing. This module teaches how AI workflows organize and transform raw reads into meaningful biological insights.
What you will learn
- Basics of microbial sequencing workflows.
- Steps like filtering, clustering, and taxonomy assignment.
- How AI ensures speed and accuracy in processing.
What the output will be
You’ll process a microbial dataset and produce a clean, usable set of microbial identities.
What you can do after completing it
Work with real microbiome datasets, assist in research pipelines, or prepare data for advanced ecological and medical microbiology studies.
AI for Microbiology
Profiling Microbial Populations
What it’s about
This module focuses on profiling — understanding not just which microbes are present, but how abundant they are and what roles they play in an ecosystem.
What you will learn
- Basics of metagenomics profiling.
- Identifying microbial abundance and diversity.
- How AI links microbial profiles to functions or diseases.
What the output will be
You’ll create a microbial abundance chart that highlights dominant and rare species in a sample.
What you can do after completing it
Study microbial links to human health (like gut bacteria), analyze soil or ocean microbiomes, or explore how microbial balance affects ecosystems.
AI for Microbiology
Visualizing and Interpreting Microbiome Data
What it’s about
Microbial data can be overwhelming. This module introduces visualization techniques that turn complex microbiome datasets into easy-to-read charts, maps, and networks.
What you will learn
- Basics of microbiome visualization.
- Comparing microbiomes across different samples.
- How AI highlights hidden trends in microbial communities.
What the output will be
You’ll produce comparative visualizations of microbiomes (e.g., healthy vs. diseased gut samples).
What you can do after completing it
Use microbiome visuals for scientific communication, compare microbiomes across environments, or present findings in research projects.
AI for Microbiology
Sharing and Analyzing Microbial Data in the Cloud
What it’s about
This final module focuses on cloud-based microbiome platforms that let researchers upload, analyze, and share microbial datasets with global teams.
What you will learn
- Basics of cloud-based microbiome analysis.
- How large-scale datasets are processed online.
- How AI supports reproducible and collaborative microbiome research.
What the output will be
You’ll upload a sample dataset to a cloud system and generate a sharable analysis report.
What you can do after completing it
Contribute to global microbiome projects, collaborate with research groups, and analyze complex datasets without high-end local resources.
Synthetic Biology Tools
Designing DNA Constructs
What it’s about
This module introduces how scientists digitally design DNA sequences before building them in the lab. AI makes the process faster and reduces errors in planning genetic experiments.
What you will learn
- Basics of digital DNA design.
- How genes, promoters, and regulatory parts fit together.
- The importance of planning before lab synthesis.
What the output will be
You’ll design a simple DNA construct digitally and generate a visual map of the sequence.
What you can do after completing it
Plan cloning experiments, explain gene design in class, or support labs in creating well-structured DNA sequences.
Synthetic Biology Tools
Collaborative Platforms for Synthetic Biology
What it’s about
Synthetic biology is a team effort. This module explores digital platforms where researchers design, share, and manage genetic projects collaboratively.
What you will learn
- Basics of cloud-based lab notebooks for synthetic biology.
- How AI organizes projects and tracks experiments.
- Why collaboration speeds up genetic engineering.
What the output will be
You’ll create a shared project space documenting a synthetic biology experiment.
What you can do after completing it
Work with research groups remotely, keep organized records, and contribute to open synthetic biology projects.
Synthetic Biology Tools
Managing Complex Biological Data
What it’s about
Synthetic biology often involves huge datasets — from sequences to protein interactions. This module shows how integrated platforms help manage, analyze, and interpret these data.
What you will learn
- Basics of sequence management and annotation.
- Organizing genetic libraries and experiment records.
- How AI finds patterns in synthetic biology data.
What the output will be
You’ll generate an annotated dataset of DNA sequences, linked to functional notes.
What you can do after completing it
Support genetic engineering workflows, streamline research records, or create structured reports for biotech projects.
Synthetic Biology Tools
Designing Genetic Circuits with AI
What it’s about
This module explores how AI helps design biological circuits — networks of genes that act like logic gates (on/off switches). These circuits can be programmed to sense, compute, and respond inside cells.
What you will learn
- Basics of genetic circuit design.
- How logic concepts (AND, OR, NOT) apply to biology.
- How AI simulates outcomes of engineered circuits.
What the output will be
You’ll design a simple genetic circuit (e.g., a sensor that responds to a chemical signal) and simulate its behavior.
What you can do after completing it
Apply circuit design to synthetic biology competitions, innovate in biosensing projects, or explore bio-computing concepts.
Synthetic Biology Tools
AI-Powered Bio-Design Automation
What it’s about
This final module highlights advanced design platforms that use AI to automate the entire workflow — from DNA design to experiment planning and optimization.
What you will learn
- How AI integrates design, simulation, and build steps.
- Basics of optimization for cost, efficiency, and accuracy.
- How automation accelerates synthetic biology innovation.
What the output will be
You’ll generate an end-to-end design plan for a synthetic biology project, including construct design, circuit simulation, and build strategy.
What you can do after completing it
Contribute to cutting-edge bioengineering projects, design synthetic organisms with practical applications, or explore biotech entrepreneurship.
AI-Driven Biomedical Imaging
Medical Image Analysis & 3D Reconstruction
What it’s about
This module introduces how AI processes medical scans to create 3D models of organs, bones, or tissues. Instead of flat slices, you’ll learn how images combine into full reconstructions.
What you will learn
- Basics of CT, MRI, and PET image handling.
- How AI segments regions of interest.
- Creating 3D reconstructions from 2D scan slices.
What the output will be
You’ll generate a 3D model of an organ from medical imaging data.
What you can do after completing it
Visualize anatomy for learning, support research projects, or create teaching materials that explain imaging results.
AI-Driven Biomedical Imaging
Interactive Segmentation of Medical Scans
What it’s about
Segmentation means dividing images into meaningful parts (e.g., tumor vs. healthy tissue). This module explores how AI assists in precise segmentation.
What you will learn
- Basics of manual vs. AI-assisted segmentation.
- Identifying regions of interest like lesions or tumors.
- Preparing segmented data for further analysis.
What the output will be
You’ll create a segmented image showing key regions of a brain or organ scan.
What you can do after completing it
Support medical studies with annotated images, prepare training datasets, or practice clinical imaging skills.
AI-Driven Biomedical Imaging
Clinical Imaging for Diagnostics
What it’s about
This module shows how AI tools help radiologists and clinicians interpret scans faster — spotting anomalies and patterns in large image datasets.
What you will learn
- Basics of diagnostic imaging workflows.
- How AI flags abnormalities in scans.
- Understanding efficiency gains in clinical practice.
What the output will be
You’ll generate a diagnostic-style report highlighting abnormal regions in a scan.
What you can do after completing it
Apply AI imaging for research on diseases, explore medical diagnostics in class projects, or create mock clinical case studies.
AI-Driven Biomedical Imaging
Open Imaging Platforms for Education & Research
What it’s about
Medical imaging isn’t just for hospitals — open platforms allow researchers and students to access anonymized scans for study and practice.
What you will learn
- How open medical imaging platforms work.
- Handling anonymized datasets for education.
- Basics of sharing imaging findings with teams.
What the output will be
You’ll download and analyze an anonymized dataset, preparing a simple research summary.
What you can do after completing it
Practice clinical-style analysis on real data, join imaging research projects, and use open scans for teaching.
AI-Driven Biomedical Imaging
Advanced Visualization & Simulation
What it’s about
This final module explores high-end imaging tools that allow detailed visualization and simulation of biological structures, combining scans with computational modeling.
What you will learn
- Basics of advanced imaging visualization.
- How AI enhances clarity, depth, and accuracy.
- Simulating organ function or tissue properties from scans.
What the output will be
You’ll create an advanced visualization or simulation of an organ or tissue from medical imaging data.
What you can do after completing it
Develop high-quality visuals for research, explore patient-specific simulations, or apply imaging insights in biomedical engineering projects.
AI in Health Informatics
Analyzing Large-Scale Health Records
What it’s about
This module introduces how massive healthcare databases are standardized and analyzed. You’ll see how AI helps detect patterns across millions of patients.
What you will learn
- Basics of standardized health data models.
- How AI discovers trends across populations.
- Why large-scale analysis is critical for public health.
What the output will be
You’ll produce a population-level analysis report showing trends in a simulated health dataset.
What you can do after completing it
Apply population health insights in research, support policy studies, or contribute to epidemiology projects.
AI in Health Informatics
Clinical Data Warehousing & Research
What it’s about
Hospitals collect huge amounts of patient data. This module shows how clinical data warehouses store and organize information for AI-powered research.
What you will learn
- Basics of clinical data repositories.
- How to query patient records for research.
- How AI links patient histories with clinical outcomes.
What the output will be
You’ll create a structured query to extract patient cohorts based on conditions or treatments.
What you can do after completing it
Assist in healthcare research projects, design cohort studies, or use structured data for medical school projects.
AI in Health Informatics
Extracting Insights from Medical Text
What it’s about
Doctors’ notes, discharge summaries, and reports hold vital information — but they’re unstructured. This module introduces natural language processing (NLP) for healthcare.
What you will learn
- Basics of clinical text processing.
- Identifying key medical terms and concepts.
- How AI turns unstructured text into usable data.
What the output will be
You’ll process a set of clinical notes and generate a structured summary of diagnoses and treatments.
What you can do after completing it
Turn raw text into analyzable data, support medical coding tasks, or explore AI applications in medical documentation.
AI in Health Informatics
Imaging & Informatics Integration
What it’s about
Healthcare data isn’t just text or numbers — it also includes scans and imaging. This module shows how imaging data integrates with health informatics systems for better decision-making.
What you will learn
- Basics of combining imaging with clinical records.
- How AI connects scans with patient data.
- The value of multimodal health informatics.
What the output will be
You’ll generate a combined dataset linking imaging features with patient outcomes.
What you can do after completing it
Support precision medicine projects, integrate imaging with electronic health records, or explore AI-powered diagnostic pipelines.
AI in Health Informatics
Interoperability & Smart Healthcare Apps
What it’s about
Modern healthcare systems must “talk” to each other. This module explores how interoperability standards let AI-powered apps plug into electronic health records seamlessly.
What you will learn
- Basics of healthcare data exchange standards.
- How apps connect securely to hospital systems.
- The role of AI in making health apps more intelligent.
What the output will be
You’ll design a prototype workflow for a healthcare app that pulls patient data and provides smart recommendations.
What you can do after completing it
Develop AI-driven healthcare apps, support digital health startups, or explore innovations in personalized patient care.
AI & Drug Discovery Platforms
Predicting Genetic Targets for Drugs
What it’s about
This module introduces how AI connects genetics with drug discovery. By analyzing genetic variations and mutations, AI can suggest potential targets for therapies.
What you will learn
- Basics of linking genes to diseases.
- How AI predicts which genetic changes cause problems.
- Why targeting the right gene is the first step in drug development.
What the output will be
You’ll generate a list of predicted genetic targets for a sample disease dataset.
What you can do after completing it
Support early-stage research in genetic medicine, explore personalized therapies, or build case studies on disease-target links.
AI & Drug Discovery Platforms
Accelerating Drug Discovery with AI Models
What it’s about
Drug discovery is often slow and expensive. This module shows how AI models speed up the process by predicting how molecules might work as drugs.
What you will learn
- Basics of virtual drug screening.
- How AI models rank compounds for effectiveness.
- How this reduces lab testing costs and time.
What the output will be
You’ll create a shortlist of potential drug molecules for a given target.
What you can do after completing it
Practice building AI pipelines for drug discovery, support pharmaceutical innovation, or showcase how AI reduces time-to-market.
AI & Drug Discovery Platforms
Predicting Drug–Target Interactions
What it’s about
This module dives into AI models that predict how well a small molecule binds to its target protein — the heart of drug effectiveness.
What you will learn
- Basics of drug–protein interactions.
- How AI predicts binding strength and fit.
- Why accurate interaction models save years of lab work.
What the output will be
You’ll generate a ranked report of drug–target interaction predictions.
What you can do after completing it
Support docking studies, contribute to drug development projects, or explore how AI identifies promising compounds faster.
AI & Drug Discovery Platforms
Mining Biomedical Literature for Insights
What it’s about
Drug discovery isn’t just about lab data — scientific literature is a goldmine. This module shows how AI reads and synthesizes millions of research papers to uncover hidden drug opportunities.
What you will learn
- Basics of biomedical knowledge graphs.
- How AI identifies patterns across research papers.
- The role of literature mining in repurposing existing drugs.
What the output will be
You’ll generate a summary linking diseases, targets, and drugs from a text dataset.
What you can do after completing it
Use AI for literature-based drug repurposing, support systematic reviews, or identify overlooked connections in biomedical research.
AI & Drug Discovery Platforms
AI-Powered Phenotypic Screening
What it’s about
This final module explores how AI analyzes images of cells and tissues exposed to different molecules. Instead of just chemistry, it looks at the real effects of drugs on biology.
What you will learn
- Basics of phenotypic drug screening.
- How AI analyzes cell images for drug effects.
- How these insights reveal unexpected therapeutic pathways.
What the output will be
You’ll produce an image-based report showing how a sample compound affects cell morphology.
What you can do after completing it
Apply phenotypic screening to discover novel drugs, support biomedical imaging projects, or explore AI’s role in cell-based therapies.
Capstone Project
Project Planning & Workflow Design
What it’s about
This module helps learners design their own research or analysis project from start to finish. It introduces planning tools to structure objectives, datasets, timelines, and outcomes.
What you will learn
- How to frame a biological/AI research question.
- Structuring milestones, tasks, and deliverables.
- How planning tools keep complex projects organized.
What the output will be
You’ll create a digital project roadmap outlining goals, datasets, methods, and timelines.
What you can do after completing it
Plan capstone projects efficiently, manage group research tasks, and prepare for real-world lab or industry projects.
Capstone Project
Integrating Multiple Tools into One Workflow
What it’s about
Here you’ll combine techniques learned across segments — genomics, imaging, data mining, or simulations — into a single pipeline.
What you will learn
- How to connect different AI tools into one analysis.
- Basics of integrating datasets across formats.
- How workflows improve reproducibility.
What the output will be
You’ll build a multi-step pipeline that integrates at least two AI-driven methods (e.g., combining genomics with protein modeling).
What you can do after completing it
Design full-scale AI biology workflows for research, industry applications, or advanced academic projects.
Capstone Project
Using Cloud-Based Lab Notebooks
What it’s about
Every project needs organized documentation. This module shows how cloud lab notebooks store data, track experiments, and allow real-time collaboration.
What you will learn
- How to record methods, results, and changes digitally.
- Basics of cloud sharing and version control.
- Why documentation is essential for teamwork and publishing.
What the output will be
You’ll produce a structured digital lab notebook entry with datasets, notes, and results.
What you can do after completing it
Maintain professional research documentation, collaborate smoothly, and prepare records suitable for publication or presentations.
Capstone Project
Automated Reporting & Visualization
What it’s about
This module introduces AI-powered report generation — transforming raw data into charts, summaries, and polished reports automatically.
What you will learn
- Basics of automated result summarization.
- Turning data into clear visuals.
- Generating reports for technical and non-technical audiences.
What the output will be
You’ll generate a formatted report of your project with charts, visualizations, and a conclusion section.
What you can do after completing it
Prepare publication-ready reports, create presentations for review, and communicate results effectively.
Capstone Project
Peer Review & Collaboration
What it’s about
Science thrives on collaboration. This module focuses on sharing your project with peers, gathering feedback, and improving through iteration.
What you will learn
- Basics of collaborative platforms for project sharing.
- How to give and receive structured feedback.
- The role of peer review in scientific progress.
What the output will be
You’ll share your project with peers, receive feedback, and refine your final report.
What you can do after completing it
Collaborate like a professional researcher, improve your work through peer insights, and prepare to present your capstone at conferences, competitions, or professional settings.
Learning Tools & Platforms Used
Participants will engage with interactive AI-powered simulations, real-time dashboards, biological data visualizers, image and sequence analysis modules, and multilingual voice-based assistants. These tools create a hands-on learning environment, allowing learners to explore, analyze, and apply AI concepts directly to real-world biological and biomedical scenarios.
Each platform emphasizes accessibility, visual learning, and practical applications, ensuring learners understand how AI supports genomics, proteomics, neuroscience, drug discovery, healthcare, and systems biology.
📈 Learning Outcomes
By the end of this course, learners will:
By the end of each unit, learners will be able to:
Develop a strategic perspective on integrating AI into academic research, healthcare, and biotechnology.
Understand how AI is transforming a specific area of biology.
Identify key AI applications in genomics, proteomics, neuroscience, drug discovery, and more.
Interpret AI-generated biological data for research and decision-making.
Apply AI principles to analyze, model, and optimize biological systems.
Duration:
Each unit is designed to be completed within 2 to 3 hours, making it accessible for working professionals, students, and researchers alike. The structure allows for self-paced progression while offering flexibility to revisit and reinforce core concepts as needed.
• Doubt-Clearing Support:
After the main class, learners can schedule a 30-minute remote session (via TeamViewer, Zoom, or similar platforms) to clarify doubts or receive personalized guidance on their projects.
Detailed Session Flow for Each Unit:
Introduction Video (10 minutes) – Overview of the unit topic and its significance in modern biology and AI-driven research.
Concept Explainer Module (20 minutes) – Animated lessons or narrated slides covering key biological concepts and AI principles.
Use Case Demonstration (20 minutes) – Real-world application with a step-by-step walkthrough of how AI is applied in fields such as genomics, neuroscience, or drug discovery.
Interactive Simulation (30 minutes) – Scenario-based learning activity where learners interact with AI systems to analyze biological data and make predictions.
Case Study Review (15 minutes) – Analysis of a successful real-life project using AI in biology, with key takeaways for research and practice.
Quiz & Reflection (15 minutes) – Assessment to reinforce learning, followed by reflective prompts on how to apply AI to real-world biology problems.
Action Plan Template (Optional) – A downloadable worksheet for planning how to integrate AI tools into research, studies, or professional projects.
Course Price & Structure
Price per Unit: ₹499 only
Each unit is designed as an affordable, standalone module. Learners can choose any unit that aligns with their academic or professional interests — such as Genomics, Protein Modeling, Neuroscience, Drug Discovery, or Health Informatics — without needing to commit to the entire program.
Multiple Enrollments:
You can enroll in multiple modules based on your learning goals. Each unit is structured independently, allowing you to mix and match topics (e.g., Genomics + Neuroscience or Immunology + Drug Discovery) for a customized learning path.
Bundle Offers:
For students looking to explore more, attractive bundles can be introduced:
- 3 Units for ₹1,299 (Save ₹198)
- 9 Units for ₹3,999 (Save ₹488)