What Is Edge Computing. An introduction to the concept of Edge computing
The concept of Edge Computing is inspired by CDN technology. CDN stands for Content Delivery Networks. A CDN typically works to bring the content (images, video, script files) on the Internet closer to its users. This helps faster streaming of content with proper load handling. This is how YouTube, Netflix, etc delivery content to different regions without getting overwhelmed by the massive data rates required for streaming services.
Edge Computing brings both data and computations closer to its users!
_In Edge Computing the data and computation are localized so that the response times and bandwidth requirements are significantly reduced. This also supports [Green Computing_](https://en.wikipedia.org/wiki/Green_computing), where the computations are done with minimal use of resources.
With the increase of network-attached devices due to the popularity of IoT, the amount of data generated has increased exponentially. This increase demands massive computing and analytics at data centres which increase the utilization of bandwidth.
In Edge Computing the computational component is pushed towards the ends of the network, i.e. the users’ end. This eliminates the need for servers to continuously work on behalf of each user, but rather invest their time on analytics at a much higher level (e.g. community analytics, community-based recommendations, etc). So how would personal data be processed?
Data is processed at the users' end using the connected devices such as smart phones, smart home hubs, smart TVs, etc.
For example, your sleep data is processed within your phone, where most data for this process is collected either from your phone, watch or using all devices with the same account logged in. Also, iPhone face detection runs completely offline and learns continuously based on your facial changes that take place over time.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
What is the most important thing to do after you got your skills to be a data scientist? It has to be to show off your skills. Otherwise, there is no use of your skills. If you want to get a job or freelance or start a start-up, you have to show off your skills to people effectively.
Understand how data changes in a fast growing company makes working with data challenging. In the last article, we looked at how users view data and the challenges they face while using data.
Find out here. Although data science job descriptions require a range of various skillsets, there are concrete prerequisites that can help you to become a successful data scientist. Some of those skills include, but are not limited to: communication, statistics, organization, and lastly, programming. Programming can be quite vague, for example, some companies in an interview could ask for a data scientist to code in Python a common pandas’ functions, while other companies can require a complete take on software engineering with classes.