Programme Parameters
Graduate / Intermediate
3 Days (4.5 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
This 3‑day virtual course equips public health, microbiology, and data‑science professionals with hands‑on Python and machine‑learning skills to analyse global surveillance data, predict antimicrobial‑resistance (AMR) risks, and build interactive early‑warning dashboards. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day virtual course equips public health, microbiology, and data‑science professionals with hands‑on Python and machine‑learning skills to analyse global surveillance data, predict antimicrobial‑resistance (AMR) risks, and build interactive early‑warning dashboards.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology deliverable you can defend and extend.
• 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
Module 1 – Foundations of Predictive Epidemiology & Global AMR Surveillance
Explore the global burden and drivers of antimicrobial resistance • Understand infectious‑disease surveillance principles • Identify key AMR data sources and reporting frameworks
Module 2 – Data Preparation & Exploration in Python
Clean and preprocess multi‑dimensional AMR datasets • Visualise pathogen‑antibiotic resistance patterns • Generate temporal trend analyses for surveillance intelligence
Module 3 – Machine‑Learning Models for AMR Prediction
Frame AMR problems as classification and risk‑scoring tasks • Engineer features from pathogen, geography, patient and genomic variables • Build and tune models such as Logistic Regression, Random Forest, Gradient Boosting and XGBoost
Module 4 – Model Evaluation & Responsible AI
Assess performance using accuracy, precision, recall, F1‑score, ROC‑AUC and confusion matrix • Interpret model outputs for public‑health decision making • Address bias, uncertainty and data‑quality issues in healthcare surveillance
Module 5 – Genomic Surveillance & AMR Databases
Integrate clinical, epidemiological and genomic AMR signals • Navigate platforms such as CARD, ResFinder, NCBI Pathogen Detection, Microreact and Nextstrain • Track resistance genes, pathogen lineages and mutation dynamics
Module 6 – Interactive Dashboards & Early‑Warning Systems
Design real‑time surveillance dashboards with Streamlit and Plotly • Visualise geographic hotspots and temporal trends • Implement basic early‑warning alerts for emerging resistance threats
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Statsmodels |
| Covered Tool / Platform | Plotly |
| Covered Tool / Platform | Seaborn |
| Covered Tool / Platform | Streamlit |
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