# Upcoming Paid Online Courses (9)

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem 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 alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.

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.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

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

This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory data analysis course, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language.

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.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

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

This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

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 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 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.

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.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

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

This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

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

This class was supported in part by NIH grant R25GM114818.

Linear Algebra: Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets. Students appreciate our unique approach to teaching linear algebra because:

- It’s visual.
- It connects hand calculations, mathematical abstractions, and computer programming.
- It illustrates the development of mathematical theory.
- It’s applicable.

In this course, you will learn all the standard topics that are taught in typical undergraduate linear algebra courses all over the world, but using our unique method, you'll also get more! LAFF was developed following the syllabus of an introductory linear algebra course at The University of Texas at Austin taught by Professor Robert van de Geijn, an expert on high performance linear algebra libraries. Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, and Eigenvalues and Eigenvectors. In addition, you will get a glimpse of cutting edge research on the development of linear algebra libraries, which are used throughout computational science.

MATLAB licenses will be made available to the participants free of charge for the duration of the course.

We invite you to LAFF with us!

**Please note that the verified certificate option is not currently open for this course. Please enroll in the audit track and you will be emailed when the verified certificate option is open for enrollment.**

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.

The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.

We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts of RNA-seq and a first look at FASTQ files. We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing **RNA-seq at the gene-level**: counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover **RNA-seq at the transcript-level**: inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

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

This class was supported in part by NIH grant R25GM114818.

We begin with an introduction to the biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives. Genomic features are generally identified using intervals in genomic coordinates, and highly efficient algorithms for computing with genomic intervals will be examined in detail. Statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are found in packages such as limma, some of these techniques will be illustrated in lectures and labs.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

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

This class was supported in part by NIH grant R25GM114818.

If you’re interested in data analysis and interpretation, then this is the data science course for you.

*Enhanced throughput*: Almost all recently manufactured laptops and desktops include multiple core CPUs. With R, it is very easy to obtain faster turnaround times for analyses by distributing tasks among the cores for concurrent execution. We will discuss how to use Bioconductor to simplify parallel computing for efficient, fault-tolerant, and reproducible high-performance analyses. This will be illustrated with common multicore architectures and Amazon’s EC2 infrastructure.

*Enhanced interactivity*: New approaches to programming with R and Bioconductor allow researchers to use the web browser as a highly dynamic interface for data interrogation and visualization. We will discuss how to create interactive reports that enable us to move beyond static tables and one-off graphics so that our analysis outputs can be transformed and explored in real time.

*Enhanced reproducibility:* New methods of virtualization of software environments, exemplified by the Docker ecosystem, are useful for achieving reproducible distributed analyses. The Docker Hub includes a considerable number of container images useful for important Bioconductor-based workflows, and we will illustrate how to use and extend these for sharable and reproducible analysis.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

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

This class was supported in part by NIH grant R25GM114818.