Through smartphones, consumers have the flexibility to continuously track their heart rate or blood glucose, rather than occasional checks in clinical settings. This is an example of the incredible opportunity Digital Biomarkers provide for fluid data transport with ownership of data shifting from the industry to the consumer.
Where is the data coming from?
A real-time, digital data network of 15,000+ unique, pre-integrated health systems, hospitals, clinics, EHRs, labs, pharmacies, devices, and mobile health applications. Models as Applications and interfaces are built on top of the platform core that represent new ways to interact, understand, and make sense of health data.
How to Analyze Big Data?
This visual explanation introduces the statistical concept of Hierarchical Modeling, also known as Mixed Effects Modeling or by these other terms. This is an approach for modeling nested data.
All code for this project is on GitHub, including the script to create the data and run regressions (done in R).
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world!
At the end of this course, you will be able to:
* Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors.
* Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting.
* Get value out of Big Data by using a 5-step process to structure your analysis.
* Identify what are and what are not big data problems and be able to recast big data problems as data science questions.
* Provide an explanation of the architectural components and programming models used for scalable big data analysis.
* Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model.
* Install and run a program using Hadoop!
This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments.