E.g., 2016-06-07
E.g., 2016-06-07
E.g., 2016-06-07
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Public Health focuses on the health of entire populations, fighting disease on a massive scale.

Average: 5.6 (5 votes)
May 15th 2016

This course explores why primary health care is central for achieving Health for All. It provides examples of how primary health care has been instrumental in approaching this goal in selected populations and how the principles of primary health care can guide future policies and actions. Two of the most inspiring, least understood, and most often derided terms in global health discourse are “Health for All” and “Primary Health Care.”

Average: 1.3 (3 votes)
May 9th 2016

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Average: 7.3 (3 votes)
May 9th 2016

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

Average: 7 (1 vote)
May 9th 2016

Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.

Average: 1 (2 votes)
May 9th 2016

Did you ever want to build a web application? Perhaps you even started down that path in a language like Java or C#, when you realized that there was so much “climbing the mountain” that you had to do? Maybe you have heard about web services being all the rage, but thought they were too complicated to integrate into your web application. Or maybe you wondered how deploying web applications to the cloud works, but there was too much to set up just to get going.

No votes yet
May 9th 2016

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Average: 10 (1 vote)
May 9th 2016

This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.

Average: 5.5 (2 votes)
May 9th 2016

You already know how to build a basic web application with the Ruby on Rails framework. Perhaps, you have even taken Course 1, "Ruby on Rails: An Introduction" (we highly recommend it) where you relied on external web services to be your “data layer”. But in the back of your mind, you always knew that there would come a time when you would need to roll up your sleeves and learn SQL to be able to interact with your own relational database (RDBMS). But there is an easier way to get started with SQL using the Active Record Object/Relational (ORM) framework. In this course, we will be able to use the Ruby language and the Active Record ORM framework to automate interactions with the database to quickly build the application we want.

No votes yet
May 9th 2016

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

Average: 2.5 (6 votes)
May 9th 2016

We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.

Average: 7.9 (7 votes)
May 9th 2016

Do you realize that the only functionality of a web application that the user directly interacts with is through the web page? Implement it poorly and, to the user, the server-side becomes irrelevant! Today’s user expects a lot out of the web page: it has to load fast, expose the desired service, and be comfortable to view on all devices: from a desktop computers to tablets and mobile phones. In this course, we will learn the basic tools that every web page coder needs to know. We will start from the ground up by learning how to implement modern web pages with HTML and CSS.

Average: 8.5 (4 votes)
May 9th 2016

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Average: 5 (6 votes)
May 9th 2016

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 9 (1 vote)
May 9th 2016

In this course, we will explore MongoDB, a very popular NoSQL database and Web Services concepts and integrate them both with Ruby on Rails. MongoDB is a used to handle documents with a pre-defined schema which will give the developers an ability to store, process and use data using it’s rich API. The modules will go in-depth from installation to CRUD operations, aggregation, indexing, GridFS and various other topics where we continuously integrate MongoDB with RailsRuby.

No votes yet
May 9th 2016

This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 2.9 (7 votes)
May 9th 2016

Get an overview of the data, questions, and tools that data analysts and data scientists work with. This is the first course in the Johns Hopkins Data Science Specialisation. In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, Github, R, and Rstudio.

Average: 3.3 (11 votes)
May 9th 2016

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

No votes yet
May 9th 2016

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Average: 4.7 (12 votes)
May 9th 2016

An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 8.3 (3 votes)

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