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The data science job market is soaring as organisations look to turn their masses of data into tangible information. A report by IBM estimated that there will be a 28% increase in demand for data scientists by 2020. That demand has and is continuing to spawn a new breed of tech and tools that assist in the data science process. Here are the key technologies you need to know about:
Please let us know where you are, or where you would like to be in the world so we can point you in the right direction.
Rapids
Rapids is a new open-source platform for data science and machine learning.
Launched this month by Nvidia in collaboration with IBM, HPE, Oracle and others, it promises to assist in quickly analysing big data by using the power of GPUs. But rather than rely on traditional machine learning to recognise complex patterns, Rapids works with the open source platforms and libraries data scientists use and amplifies their power.
This means more powerful analytics, machine learning and soon data virtualisation. The platform is designed to accelerate data science end-to-end and Nvidia found it delivers a 50x speedup compared with CPU-only systems.
R language
R is a free language and environment for statistical computing and graphics. Data scientists use it to develop and create reproducible, high-quality analysis.
It supports machine learning and its repositories support a wide range of statistical and manipulation techniques. R is growing in popularity and is used for statistical computations, data analysis and graphical representation of data. It’s an important tool in data science and due to its skillset is also attracting people from biosciences.
Microsoft Cognitive Toolkit 2.0
Microsoft Cognitive Toolkit 2.0 is the underlying technology for Microsoft Cortana, Bing search and Skype live translation. It’s a free, open-source toolkit for deep learning that’s powerful, scalable, fast and accurate.
It trains deep learning algorithms and allows for the implementation of neural network models. It’s an easy-to-use architecture that works with C++ and Python and has compatibility with many of the algorithms used by data scientists.
DNNs
Deep neural networks (DNNs) are among the most powerful deep learning algorithms and data scientists are increasingly using them.
These networks can be configured and modified to recognise an immense number of characteristics, and in turn, produce huge results. The challenge is the more layers there are in the network, the slower it is. To train a DNN at rapid speed, supercomputing is needed as university researchers recently found when they trained a deep neural network for image recognition.
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