Held on 6th September to 12th September 2023 (7 to 8 pm)

Module 1: Introduction to Data Science in Biotechnology

• Understanding Data Science in the Biotech Context
• Overview of Prediction Algorithms in Biotechnology
• Importance of Data Quality and Preprocessing
• Hands-on: Data Preprocessing for Biotech Datasets

Module 2: Fundamentals of Machine Learning

• Supervised vs. Unsupervised Learning in Biotech Applications
• Key Machine Learning Concepts for Biotechnologists
• Database Handling for Biotech Data: Challenges and Solutions
• Introduction to File Formats and Conversions in Biotech Data

Module 3: Machine Learning Algorithms for Biotech Predictions

• Regression Algorithms for Continuous Data Prediction
• Classification Algorithms for Categorical Data Prediction
• Feature Selection and Dimensionality Reduction in Biotech Data
• Practical: Building a Predictive Model for a Biotech Case Study

Module 4: Applying Machine Learning in Cancer Genomics

• Cancer Genomics: Introduction and Significance
• Sequence Alignment Analysis in Cancer Genomics
• Introduction to Machine Learning Tools for Sequence Analysis
• Hands-on: Utilizing Machine Learning Software for Cancer Sequence Analysis

Module 5: Machine Learning in Cancer Genomics and Proteomics

• Naive Bayes Algorithm: Theory and Application in Biotech
• Naive Bayes for Cancer Genomics: Case Study and Examples
• Naive Bayes in Proteomics: Analyzing Protein Data
• Workshop Project: Implementing Naive Bayes for Biotech Data Analysis

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