Data Analysis for the Life Sciences Series
HarvardX, United States
Language course is: English
Focus: Math/Statistics
Target audience education / assumed knowledge:
anyone with minimal R skills
Educational certification/accreditation status:
None
Professional certification/accreditation status:
None
Degree Offered:
certificate (1-3 mths)
Key characteristics of program:
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals. We will provide examples by programming in R in a way that will help make the connection between concepts and implementation. Problems sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternative when data do not fit assumptions required by the standard approaches. We will also introduce the basics of using R scripts to conduct reproducible research.
This class was supported in part by NIH grant R25GM114818.
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals. We will provide examples by programming in R in a way that will help make the connection between concepts and implementation. Problems sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternative when data do not fit assumptions required by the standard approaches. We will also introduce the basics of using R scripts to conduct reproducible research.
This class was supported in part by NIH grant R25GM114818.
Program Goals:
Topics:
Distributions
Exploratory Data Analysis
Inference
Non-parametric statistics
These courses make up 2 XSeries and are self-paced:
PH525.1x: Statistics and R for the Life Sciences
PH525.2x: Introduction to Linear Models and Matrix Algebra
PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
PH525.4x: High-Dimensional Data Analysis
PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays
PH525.6x: High-performance computing for reproducible genomics
PH525.7x: Case studies in functional genomics
Topics:
Distributions
Exploratory Data Analysis
Inference
Non-parametric statistics
These courses make up 2 XSeries and are self-paced:
PH525.1x: Statistics and R for the Life Sciences
PH525.2x: Introduction to Linear Models and Matrix Algebra
PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
PH525.4x: High-Dimensional Data Analysis
PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays
PH525.6x: High-performance computing for reproducible genomics
PH525.7x: Case studies in functional genomics
Additional Information
Year Program Established: 2014
Course/tuition costs: Free
Scholarships/assistanceships may be available: No
Average number of applicants per year: 15000
Average number of applicants accepted per year: 15000
Average number of incoming students per year: 15000
Average number of graduates per year: 0
Program URL: http://www.edx.org/xseries/data-analysis-life-sciences
Contact Person: edX ( [javascript protected email address])
Last updated: Dec 04, 2017
Course/tuition costs: Free
Scholarships/assistanceships may be available: No
Average number of applicants per year: 15000
Average number of applicants accepted per year: 15000
Average number of incoming students per year: 15000
Average number of graduates per year: 0
Program URL: http://www.edx.org/xseries/data-analysis-life-sciences
Contact Person: edX ( [javascript protected email address])
Last updated: Dec 04, 2017
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