HighDimensional Data Analysis
Learning Experience  7.4 

Data analysis and interpretation: This course is focused on several techniques that are widely used in the field of highdimensional data analysis.
Introduction
This course focuses on several techniques that are widely used in the analysis of highdimensional data.
About this course
If you’re interested in data analysis and interpretation, then this is the data science course for you. You commence with learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of highdimensional data sets, and multidimensional scaling and its connection to principal component analysis. You 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, it describes the principal component analysis and factor analysis and demonstrates how these concepts are applied to data visualization and data analysis of highthroughput experimental data.
Finally, you will have a brief introduction to machine learning and apply it to highthroughput, largescale data, describing 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 student’s course 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.
These courses makeup two Professional Certificates and are selfpaced:
Data Analysis for Life Sciences:
 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
Genomics Data Analysis:
 PH525.5x: Introduction to Bioconductor
 PH525.6x: Case Studies in Functional Genomics
 PH525.7x: Advanced Bioconductor
 This class was supported in part by NIH grant R25GM114818.
What you will learn from HighDimensional Data Analysis?
 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
Prerequisites
PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra, OR PH525.3x
Syllabus on HighDimensional Data Analysis:

Introduction and Resources

Week 1. Distance

Week 2. Dimension Reduction

Week 3.

Basic Machine Learning: Clustering

Basic Machine Learning: Classification


Week 4. Batch Effects
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Description
Introduction
This course focuses on several techniques that are widely used in the analysis of highdimensional data.
About this course
If you’re interested in data analysis and interpretation, then this is the data science course for you. You commence with learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of highdimensional data sets, and multidimensional scaling and its connection to principal component analysis. You 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, it describes the principal component analysis and factor analysis and demonstrates how these concepts are applied to data visualization and data analysis of highthroughput experimental data.
Finally, you will have a brief introduction to machine learning and apply it to highthroughput, largescale data, describing 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 student’s course 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.
These courses makeup two Professional Certificates and are selfpaced:
Data Analysis for Life Sciences:
 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
Genomics Data Analysis:
 PH525.5x: Introduction to Bioconductor
 PH525.6x: Case Studies in Functional Genomics
 PH525.7x: Advanced Bioconductor
 This class was supported in part by NIH grant R25GM114818.
What you will learn from HighDimensional Data Analysis?
 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
Prerequisites
PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra, OR PH525.3x
Syllabus on HighDimensional Data Analysis:

Introduction and Resources

Week 1. Distance

Week 2. Dimension Reduction

Week 3.

Basic Machine Learning: Clustering

Basic Machine Learning: Classification


Week 4. Batch Effects
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Specification:
 EDX
 Harvard University
 Online Course
 Selfpaced
 Advanced
 14 Weeks
 Free Course (Affordable Certificate)
 English
 Bioinformatics Data Analysis Machine learning Maths
There are no reviews yet.