An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

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An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Statistics, Data Analysis, R Programming, Biostatistics

4.2 (246 ratings)

- 5 stars52.03%
- 4 stars27.64%
- 3 stars11.78%
- 2 stars3.25%
- 1 star5.28%

Mar 25, 2019

Very good course and useful understanding statistical aspects of data.

Mar 04, 2020

Great course as a starting point for statistical genomics!

From the lesson

Module 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.

#### Jeff Leek, PhD

Associate Professor, Biostatistics

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- Introduction to TensorFlow
- Neural Networks and Deep Learning
- Algorithms, Part 1
- Algorithms, Part 2
- Machine Learning
- Machine Learning with Python
- Machine Learning Using Sas Viya
- R Programming
- Intro to Programming with Matlab
- Data Analysis with Python
- AWS Fundamentals: Going Cloud Native
- Google Cloud Platform Fundamentals
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- Learning How to Learn
- Financial Markets
- Hypothesis Testing in Public Health
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- Deep Learning
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