Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative qualitative data analysis and interactive data visualization. Orange. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative qualitative data analysis and interactive data visualization. Orange.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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 demonstrable biotechnology project for your research or industry portfolio.
• 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
Visual Programming for Data Analysis
• Widgets, channels and building a workflow without writing code
• Loading data, assigning target and meta attributes correctly
• Where a visual tool is genuinely faster and where it becomes a limitation
Preparing an Immunogenicity Dataset
• Peptide and antigen features, encodings and derived descriptors
• Missing values, duplicates and near-duplicate sequences across splits
• Class imbalance, since immunogenic examples are typically the minority
Seeing the Data First
• Distributions, scatter plots and correlation before modelling
• PCA, t-SNE and MDS for structure, and the over-reading they invite
• Outlier detection and deciding whether a point is error or biology
Training and Comparing Learners
• Logistic regression, random forest, SVM and naive Bayes side by side
• Test and Score with cross-validation, and stratification that must be set
• ROC, precision-recall and confusion matrices read correctly
What the Result Supports
• Feature importance, and treating it as a hypothesis rather than a mechanism
• Sequence similarity leakage between training and test as the standard trap
• Exporting a workflow reproducibly and knowing when to move to code
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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