BIG DATA IN BIOTECHNOLOGY (2 CREDITS)

Learning Outcomes:

On successful completion of this course, students will be able to: LO1 – Discuss scalable data processing pipelines for handling large-scale biological datasets in various biotechnology applications; LO2 – apply analysis on complex biological big data using advanced computational techniques, machine learning algorithms, and data integration methods; LO3 – Analyze the applicability of cloud computing, distributed computing frameworks, and AI technologies for efficient processing of massive biotechnology datasets; LO4 – identify the effective data visualization and AI-driven predictive models to communicate and interprest complex insights from biotechnology big data; LO5 – assemble the ethical, privacy, and security implications of working with big data and AI in biotechnology and formulate appropriate safeguards.

Topics:

1. Introduction to Big Data;
2. Standard and Regulation;
3. Data Types;
4. Data Integration;
5. Machine Learning & AI;
6. Data Visualization;
7. Big Data in Agriculture;
8. Big Data in Bioindustry;
9. Big Data in Environmental Industry;
10. Big Data in Synthetic Biology;
11. Emerging Technologies in Big Data;
12. Ethical Aspect of Big Data;
13. Project.