Course description
High-Dimensional Data Analysis
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 of high-dimensional data sets, and multi-dimensional 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 high-throughput experimental data.
Finally, we give a brief introduction to machine learning and apply it to high-throughput, large-scale data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates and cross-validation.
Upcoming start dates
Suitability - Who should attend?
Prerequisites
PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra, OR PH525.3x
Training Course Content
- 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
Course delivery details
This course is offered through Harvard University, a partner institute of EdX.
2-4 hours per week
Expenses
- Verified Track -$149
- Audit Track - Free