This repository contains the answers for coursera 's "Statistics for Genomic Data Science"
- week 1 : This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.
- week 2 : This week we will cover preprocessing, linear modeling, and batch effects.
- week 3 : This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
- week 4 : In this week we will cover a lot of the general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.