Data Analysis for Life Sciences 4: HighDimensional Data Analysis  Harvard University
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Informazione importanti
 Corso
 Online
 Quando:
Da definire
A focus on several techniques that are widely used in the analysis of highdimensional data.
With an apprenticeship you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.With this course you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.
Requisiti: PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra, OR PH525.3x
SediInizio  Luogo 

Da definire 
Online

Cosa impari in questo corso?
Data analysis  Biology  Mathematical Distance  Principal Component Analysis  
Factor Analysis 
Programma
If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multidimensional scaling and its connection to principle component analysis. We will learn about the batch effect: the most challenging data analytical problem in genomics today and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of highthroughput experimental data.
Finally, we give a brief introduction to machine learning and apply it to highthroughput data. We describe the general idea behind clustering analysis and descript Kmeans and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as knearest neighbors along with the concepts of training sets, test sets, error rates and crossvalidation.
Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
The courses in this series will be released sequentially each month and are selfpaced:
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 Highthroughput Experiments
PH525.4x: HighDimensional Data Analysis
PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays
PH525.6x: Highperformance computing for reproducible genomics
PH525.7x: Case studies in functional genomics
 Mathematical Distance
 Dimension Reduction
 Singular Value Decomposition and Principal Component Analysis
 Multiple Dimensional Scaling Plots
 Factor Analysis
 Dealing with Batch Effects
 Clustering
 Heatmaps
 Basic Machine Learning Concepts