To Analysing Data for Genomics is designed to provide you with basic knowledge and skills to independently design, execute and explain the results of data analysis in the context of a genomics/proteomics experiment. See More: https://bit.ly/2KWDgBG

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Analysing Data for Genomics
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To is designed to provide you with basic knowledge and skills to independently design, execute and
explain the results of …
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LEVEL 3 - DIPLOMA
COURSE BADGE
COURSE CERTIFICATE
32NUMBER OF UNITS
0NUMBER OF QUIZZES
2 HOURS, 6 MINUTES
HOME CURRICULUM REVIEWS
To Analysing Data for Genomics is designed to provide you with basic knowledge and skills to
independently design, execute and explain the results of data analysis in the context
of a genomics/proteomics experiment. Through this course you will learn advanced techniques to
analyze genomic data, how to structure, annotate, normalize, and interpret genome-scale assays,
how to bridge diverse genomic assay and annotation structures to data analysis and research
presentations via innovative approaches to computing, how to analyze data from several
experimental protocols, using open source software, including R and Bioconductor.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to
complete the course, the answers will be in the form of written work in pdf or word. Students can
write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers
are submitted, the tutor will check and assess the work.
Certification
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assignment of the course as part of the assessment. Upon successful completion of a course, you can
choose to make your achievement formal by obtaining your Certi cate at a cost of £49.
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Course Credit: Harvard University.

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Introduction
Introduction to statistics and R for the Life Sciences 00:02:00
Lesson 1
Getting Started with 00:06:00
GitHub 00:02:00
RStudio 00:01:00
Using the Textbook 00:02:00
RStudio for Organization 00:06:00
Introduction to dplyr 00:09:00
Lesson 2
Motivation 00:05:00
Introduction to Random Variables 00:04:00
Probability Distributions 00:03:00
The Normal Distribution 00:07:00
Populations, parameters, and sample estimates 00:05:00
Central Limit Theorem (CLT) 00:07:00
CLT in Practice 00:04:00
COURSE CURRICULUM

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T-test 00:06:00
T-test in practice 00:03:00
Lesson 3
Introduction to Inference 00:02:00
Con dence Intervals 00:08:00
Power Calculations 00:07:00
Monte Carlo Simulation 00:06:00
Association Tests 00:02:00
Lesson 4
Histogram 00:04:00
qq-plot 00:02:00
Boxplot 00:02:00
Scatterplot 00:07:00
Symmetry of Log Ratios 00:02:00
Plots to Avoid 00:05:00
Avoid Pseudo 3D 00:02:00
Median, MAD, and Spearman Correlation 00:02:00
Mann-Whitney-Wilcoxon Test 00:03:00

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Assessment
Submit Your Assignment 00:00:00
Certi cation 00:00:00
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