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Data Science Trends To Look Out For In 2017

Data Science Trends

Data grows at a rate of 2.5 billion gigabytes (GB) per day, and this number is constantly growing. Data has become an integral part of our lives, where everything runs on data. In the coming years, this number is Expect to grow by four fold.

The need for data Science and big data is crucial, as all of the data we acquire is in an unstructured mess. In order to make sense of the data and analyze it, we need some awesome mathematicians and scientist that can organize and actually find trends among the mess. While, Big Data and Data Science go hand in hand, do not confuse them for being the same. They have some significant differences.

Big Data vs. Data Science
Big Data and Data Science are quite similar as both of these terms deal with data. However, they differ from each other in many ways. It can almost be said that Data Science is the umbrella term under which Big Data can be found.

The term Data Science is defined as dealing with structured and unstructured data, a field that comprises everything related to data cleansing, preparation, and analysis. On the other hand Big Data mostly deals refers to refers to large volumes of data that cannot be processed effectively with the traditional applications that exist. This often has to deal with processing and storing of data, while data science takes it a step further and also includes extracting knowledge and insights from data.

As data continues to grow in importance in our lives, there are a few trends that we can see growing. Here are a few Data Science trends you should look for in 2017 and coming years:

AI and Machine Learning
We are not that far off where AI becomes integrated into everything, at one point our phones will understand our needs better than us. And in data science, this becomes crucial. So, it’s really not a surprise where AI and Machine learning will become more integrated into data science. We expect to see more real world applications of both, AI and ML, such as deep learning, Natural Language Processing and even neural networks across multiple industries. We also expect to see AI and ML, get integrated into the algorithms that are written for analyzing data, which will allow us to get more realistic data, quicker.

Data Science Grows Closer to IoT
Data Science and IoT are often considered as two sides of the same coin. Although, they already work hand-in-hand, they are expected to become closer and grow together. As IoT offers more data to the stream that is already available on the internet, data science must definitely be there to help sort and analyze and make sense of the data to make decisions, quickly and in real-time.

We definitely expect to see many trends in this year, where innovations will show some of the best combinations of IoT and Data Science, including items such as self driving cars, self-locking and unlocking doors, and so much more.

Rise of Big Data
We already have seen a big list of so many different big data Technologies that are already on the market and with the coming year, we expect more and more big data technologies to emerge as this year goes on. More companies are definitely already planning to start integrating big data into their business decisions and more companies will definitely get on board in this year. The big data technologies such as Hadoop, HPCC, Cloudera, Spark, etc. are also expected to get smarter and better. You can read more about big data technologies here.

Hiring Becomes Competitive
Hiring in this stream is going to become competitive as data science becomes more complex. We also expect to see more hiring on data scientists that are from different fields such as mathematics, analytics, science, etc. The market for data science is growing and expanding at a tremendous rate, and to fill these posts with specialists is going to be a near impossible task. This is why it is predicted that companies might start bringing in people from different backgrounds such as mathematics and analysts, and train them in-house for the post. Salaries for data scientists are also expected to increase greatly.

APIs and Integrations Rise
A big questions that software and programming companies have been asking themselves over the years is, “better to build in-house or buy”. The old mantra included building in-house as this gave your more control over your product, design and resources. However, the trend is shifting today, many companies are now looking to buy rather than waste the time and energy into building things from scratch.

As the market is growing to also incorporate non-technical users into its fold, there is now a need for integrations, which is why WordPress raking in a lot of money. We will also see a rise in APIs, which are basically plug-and-play extensions that require little to no coding, something like wordpress plugins.

Coder, no coding!
Another trend that we expect to see is more data science specific technologies that are codeless, similar to the plug-ins above. More data science technologies are being democratized and more languages will have easier importable libraries and may also introduce drag-and-drop GUI tools. This is simplify the process of analyzing data, allowing even non-techies to step into the field.

There are some of the different trends that are already budding and we expect to see them bloom sometime in the future. As we progress and data becomes an integral part of our world, we will definitely see some of these technologies happening.

The post Data Science Trends To Look Out For In 2017 appeared first on Eduonix.com | Blog.



This post first appeared on How And When Should You Use HBase NoSQL DB, please read the originial post: here

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Data Science Trends To Look Out For In 2017

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