Genomic Data Science

This course introduces the basic biology of modern genomics and the experimental tools used to measure it. It covers the Central Dogma of Molecular Biology and explains how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. Additionally, it provides an introduction to the key concepts in computing and data science required to understand how data from next-generation sequencing experiments are generated and analyzed.

This class provides an introduction to the Python programming language and the iPython notebook, essential for genomic data science. It equips learners with the knowledge to use Python for data analysis and manipulation in the context of genomics.

Discover computational methods and algorithms for analyzing DNA sequencing data. Gain insights into DNA, genomics, and the utilization of Python to implement key algorithms and analyze real genomes and DNA sequencing datasets.

This module introduces the commands needed to manage and analyze directories, files, and large sets of genomic data efficiently using the command line. It provides essential skills for data scientists working with genomic data.

Learn to use tools from the Bioconductor project to perform analysis of genomic data. Understand how to leverage Bioconductor for genomic data science, including its applications and benefits in data analysis and interpretation.

Gain an introduction to the statistics behind the most popular genomic data science projects. This module equips learners with the statistical knowledge required for effective analysis and interpretation of genomic data.