Units
3.0 QUARTER UNITS
Course Description
This course explains where large data sets come from and how they are stored and managed. It also examines data sizes, accessibility approaches, and how data are transformed and used for AI consumption. You will examine the challenges and considerations when choosing data for training sets.
By the end of course, you will understand the types of data used in bioinformatics, how the data are collected, stored, managed and searched, and how the data are transformed for further processing and analysis. You will also develop skills on how to aggregate and normalize the data to be used for machine learning and/or AI training sets.
Topics
- Pipeline Design
- Workflow management systems and workflow analysis with open-source tools
- Documentation skills / proof of concept with foresight
- Using SQL for bioinformatics data
- Data lakes (e.g, Databricks, Redshift and/or Snowflake)
- Large data sets
- Databases - how to store, move, and learn what AI models to use
Additional Information
AI* - This course teaches students how to write bioinformatics programs by using AI for parsing and normalization of biological data.
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This course applies to these programs:
