Upcoming Paid Online Courses (23)

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Starts : 2017-06-27 in 0 days
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edX Free English EdX HarvardX Health & Safety Medicine Social Sciences

We are at a critical inflection point in global health. We have seen improvements in access to care, but struggle to improve human health. A key component of this equation is quality of care.

Improving access to healthcare is only as useful as the quality of care provided. Many agree that quality is important – but what is it? How do we define it? How do we measure it? And most importantly, how might we make it better?

The course is designed for those who care about health and healthcare and wish to learn more about how to measure and improve that care – for themselves, for their institutions, or for their countries. Each session will be interactive and provide concrete tools that students can use. We will empower you to raise questions, propose concrete solutions, and promote change.

We have assembled leading thinkers from around the globe – not only people who are experts – but people with real, hands-on experience running organizations, hospitals, and ministries of health. So join us – whether you are a physician, nurse, or other healthcare provider, if you are a student of medicine, public health, or health policy, or a patient who simply cares about getting good care – this course is for you.


Honor Code

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.

Research statement

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.

Nondiscrimination/anti-harassment statement

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.

Starts : 2017-06-27 in 0 days
No votes
edX Free English Education & Teacher Training EdX MichiganX

With roots in industry and in health care, improvement science is a disciplined approach to educational innovation that supports teachers, leaders, and researchers in collaborating to solve specific problems of practice. Improvement science brings discipline and methods to different logics of innovation by integrating:

  • Problem analysis
  • Use of research
  • Development of solutions
  • Measurement of processes and outcomes
  • Rapid refinement through plan-do-study-act cycles.

For teachers, school leaders, and system leaders, improvement science moves educational innovation out of the realm of “fad” and into the realm of research-based, evidence-driven continuous improvement, with the goal of increasing the effectiveness of educational practice.

That, in turn, will support schools and systems in responding to calls to improve opportunities to learn and student performance and calls to reduce achievement gaps by improving the day-to-day work students, teachers, and leaders.

In this introduction to improvement science, developed in collaboration with the Carnegie Foundation for the Advancement of Teaching, learners will explore:

  • Problem-specific and user-centered design and analysis
  • Differences in implementation and outcomes as resources for improvement
  • Improving systems to improve practice
  • Driving improvement through measurement, evidence, and disciplined inquiry

This course is part of the Leading Educational Innovation and Improvement MicroMasters Program offered by MichiganX.

Starts : 2017-06-30 in 3 days
No votes
edX Free English Biology & Life Sciences EdX HKPolyUx Medicine Science

How is the human body structured? How are the different body systems interconnected with each other? If you are interested but layman to Human Anatomy, if you find the Human Anatomy textbooks are too difficult to read, or if you want to freshen up quickly your anatomy knowledge, this is the course for you.

Human Anatomy is fundamental to every medical and healthcare professional. However, the science of anatomy and effects of stroke are also extremely useful to anyone interested in understanding more about the human body. In this course, you’ll gain an understanding of the basic concepts of anatomy and learn to ‘dissect’ the human body with a logical approach through a typical clinical case of stroke.

Case-based study:
A real-life severe stroke case is adopted in this MOOC to articulate the application of Human Anatomy knowledge. This case scenario is presented by using a micro movie together with an interactive case summary and interview to arouse learners’ interest.

Module-based design:
In addition to the presentation of a stroke case scenario in Module ONE, two more modules are included. In Module TWO, general knowledge of human anatomy related to the stroke case, including organs of important body systems, anatomical orientation, skeletal and muscular system, nervous system and special senses, and cardiovascular and pulmonary system. And Module THREE is specific for healthcare professionals or learners who want to know more about the health services being provided to stroke patients. It rounds up the course with six healthcare-discipline specific role play videos and lectures given by visiting professors.

Starts : 2017-07-01 in 4 days
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

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

PH525.2x: 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


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.

Starts : 2017-07-01 in 4 days
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

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

PH525.2x: 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

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.

Starts : 2017-07-01 in 4 days
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

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

PH525.2x: 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


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.

Starts : 2017-07-01 in 4 days
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

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.

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

PH525.2x: 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


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.

Starts : 2017-07-03 in 6 days
No votes
edX Free English ACCA Business & Management Economics & Finance EdX

Don’t have a business background but want to understand how people and systems of an organisation interact with the world and each other?

This business and finance course will help you understand business in the context of its environment, including economic, legal, and regulatory influences on such aspects as governance, employment, health and safety, data protection and security.

Upon the completion of the course, you will understand:

  • The purpose and types of businesses and more about how the external environment impact them
  • Business organisation structure, functions and the role of corporate governance
  • How accountancy and audit support effective financial management and communication
  • Leadership and how people are developed within businesses
  • What makes for a high performing team
  • The vital role of professional ethics and professional values in all aspects of business

Completion of this course will also prepare you for the ACCA ‘Financial Accounting’ exam (FAB/F1), which leads to a Diploma in Accounting and Business.

To check availability of this course in your country please click here.

This course does not offer an edX certificate.

Those learners who would like to earn an award will have the opportunity to register with ACCA as a student, take computer based exams and gain the ACCA Diploma in Accounting and Business.

You can find your nearest ACCA exam centre on the ACCA website.

Starts : 2017-07-03 in 6 days
1 votes
Open2Study Free Business

Learn to assess the strength of a business and identify early warning signs of potential future problems.

Starts : 2017-07-03 in 6 days
10 votes
Open2Study Free Health and Welfare

Walk through the basics of nutrition, how eating disorders develop, and learn the benefits of various diets.

Starts : 2017-07-03 in 6 days
5 votes
Open2Study Free Health and Welfare

Considering a career in nursing? This course will introduce you to the role of nurses in Australian healthcare.

Starts : 2017-07-10 in 13 days
No votes
edX Free English Architecture EdX Environmental Studies TrinityX

How can we strengthen sustainability? By empowering individuals and communities to transform and balance dynamic natural resources, economic prosperity, and healthy populations.

In this course, you’ll explore productive and disruptive social, ecological, and economic intersections – the “triple bottom line.” You’ll investigate a spectrum of global, national, regional, municipal and personal relationships that are increasing resiliency. Most importantly, you’ll learn how to effectively locate your interests, and to leverage optimistic change within emerging 21st century urban environments.

This course will describe fundamental paradigm shifts that are shaping sustainability. These include connectivity, diversity, citizen engagement, collaboration source tracing, mapping, transportation, and integrative, regenerative design. We will take examples from cities around the globe; making particular use of the complex evolution of site-specific conditions within the Connecticut River watershed. In addition we will present tools and strategies that can be utilized by individuals, communities, and corporations to orchestrate effective and collective change.

Each week, lessons will highlight the significance of clean water as a key indication of ecosystem, community and human health. Learners will be asked to investigate and share information about their local environment.

Finally, we will note the impact of such disruptive forces as industrial pollution, changing governance, privatization of public services, mining of natural resources, public awareness, and climate change. A fundamental course goal will be to characterize indicators of economic prosperity and happiness that relate to environmental sustainability – and the capacity of individuals to create change.

Starts : 2017-08-21 in 55 days
No votes

This course will provide learners with an overview of the seven steps of evidence-based practice (EBP) in nursing and health sciences.

Starts : 2017-08-28 in 62 days
No votes
edX Free English Biology & Life Sciences EdX Health & Safety Science UTSanAntonioX

Alzheimer’s disease is one of the most financially costly disease in developed countries. Even if knowledge of molecular changes in Alzheimer’s disease is extensive, and new areas of investigation have been explored, the cognitive trajectory of Alzheimer’s disease is still unknown, as it was in 1906, when this disease was described for the first time.

This self-paced course focuses on a recent and innovative approach in the field of Alzheimer’s disease research. Multiple evidences indicate that oxidative stress and free radicals damage the cellular functions. Specifically, oxidative damage is a marker to identify the initial state of the disease.

Starting from a critical analysis of Alzheimer’s disease history, Dr. Perry, a worldwide expert in the field, explains the sequence of events leading to damage, and the source of increased oxygen radicals along with mechanisms to provide more effective treatment.

This course is open to anyone, but will be of particular relevance to professionals and caregivers who deal with patients affected by Alzheimer’s. Considering the multidisciplinary approach, and the importance of a correct lifestyle to prevent and treat Alzheimer’s disease, this course is also targeted to healthcare professionals such as nutritionists or cardiologists.

Starts : 2017-09-05 in 70 days
No votes
edX Free English EdX Social Sciences UC BerkeleyX

"A free eight-week Science of Happiness course that will offer practical, research-backed tips on living a happy and meaningful life."  - The Huffington Post

We all want to be happy, and there are countless ideas about what happiness is and how we can get some. But not many of those ideas are based on science. That’s where this course comes in.

“The Science of Happiness” is the first MOOC to teach the ground-breaking science of positive psychology, which explores the roots of a happy and meaningful life. Students will engage with some of the most provocative and practical lessons from this science, discovering how cutting-edge research can be applied to their own lives. Created by UC Berkeley’s Greater Good Science Center, the course will zero in on a fundamental finding from positive psychology: that happiness is inextricably linked to having strong social connections and contributing to something bigger than yourself—the greater good. Students will learn about the cross-disciplinary research supporting this view, spanning the fields of psychology, neuroscience, evolutionary biology, and beyond.

What’s more, “The Science of Happiness” offers students practical strategies for tapping into and nurturing their own happiness, including trying several research-backed activities that foster social and emotional well-being, and exploring how their own happiness changes along the way.

The course’s co-instructors, Dacher Keltner and Emiliana Simon-Thomas, are not only leading authorities on positive psychology but also gifted teachers skilled at making science fun and personal. They’ll be joined by world-renowned experts discussing themes like empathy, mindfulness, and gratitude—experts including Barbara Fredrickson, Paul Ekman, Sonja Lyubomirsky, and Jon Kabat-Zinn. Health professionals who register can earn continuing education units for their participation.

Consider signing up for this course with a friend or group - tweet about your registration, share it on Facebook, and use the buddy system to stay on track. Join the conversation on The Greater Good Science Center Facebook page, or in the BerkeleyX: GG101x The Science of Happiness Facebook group.

Starts : 2017-09-07 in 72 days
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

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.

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

PH525.2x: 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


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.

Starts : 2017-09-07 in 72 days
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

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.

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

PH525.2x: 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


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.

Starts : 2017-09-07 in 72 days
No votes
edX Free English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

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.

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

PH525.2x: 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


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.

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Starts : 2017-10-01 in 96 days
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edX Free English EdX Humanities Law LouvainX

Are you aware of controversial situations opposing States and foreign investors in relation to the protection of public health and the environment? Do you remember the recent case examples, Philip Morris v Uruguay and Vattenfall v Germany?

In the aftermath of these high profile cases, policy-makers, civil society organizations and domestic constituencies came to realize the societal importance of international investment law. From this realization, came passionate and sometimes ill-informed discussions about the features of international investment law, which grants rights to foreign investors to foster States’ development and reform it. Central to these discussions were the issues of:

  • The promotion of sustainable development in international investment law.
  • The balance between the protection of foreign investors and the right of host States to regulate to protect public welfare objectives.
  • The legitimacy of arbitration tribunals to rule over disputes between host States and foreign investors.

Learning and understanding the features and dynamics of international investment law is not only key for international lawyers, but it is important for being a well-informed citizen.

In this law course, you will:

  • Discover the history of international investment law and understand the dynamics shaping its evolution.
  • Learn the objectives of international investment law and the specific rights it grants to foreign investors.
  • Be able to to evaluate how those rights are interpreted by arbitration tribunals.
  • Master the features and functioning of investment arbitration.
  • Understand why international investment law and investment arbitration are criticized and be able to assess the soundness of these criticisms.
  • Gain insight into the content of treaties newly concluded, for instance the CETA between the European Union and Canada, and be able to assess how they address the issues of the right of States to regulate and of the legitimacy of arbitration tribunals.

Starts : 2017-10-09 in 104 days
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edX Free English ASUx EdX Food & Nutrition Health & Safety

This 3 credit health and wellness course focuses on the latest trends in health, nutrition, physical activity, and wellness. From stress management and sleep to overall wellbeing, we will explore personal health, health related attitudes and beliefs, and individual health behaviors.

Topics include:

  • Assessment of one’s personal health
  • Introduction to population health and national and global health goals
  • Dietary choices for lifelong health
  • Improving personal fitness
  • Achieving and maintaining a healthy weight
  • Assessing health information
  • Managing stress
  • Sleep hygiene
  • Lowering risk of infectious diseases
  • Chronic disease risk reduction 

This course satisfies the Social-Behavioral Sciences (SB) general studies requirement at Arizona State University. This course may satisfy a general education requirement at other institutions; however, it is strongly encouraged that you consult with your institution of choice to determine how these credits will be applied to their degree requirements prior to transferring the credit.