Big Data, Business Intelligence, Data Science – the digital revolution is here, and it’s evolving steadfastly.
Soon, data analytics is becoming the life-source of IT. The range of technologies is varied, and the way data is expanding, we are fast moving towards a juncture where analysis of vast volumes of data will be done in a jiffy.
Now, gain deep insights about customer behavior, better revenue possibilities, determine systems performance with effective data analytics strategy. Here’s a pried-out list of top data analytics tools and techniques that are heating up this year, and if you are seeking how to leverage data analytics for actionable intelligence, this need to be your last-minute guide.
Data scientists empowered with strong statistical skills explores a number of options for data analysis. Amidst all, R Programming skills is one of the most effective ways to create best quality, reproducible analysis – in contrary to spreadsheets, R scripts can easily be audited and re-run.
R is a free open source, and is found embedded in numerous commercial products, like SQL Server and Microsoft Azure Machine Learning Studio.
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Deep Neural Networks are the most dynamic deep learning algorithms developed from several layers of alternating linear and nonlinear processing units. The alternating layers are considerably deep, and are cultivated using exhaustive algorithms and colossal amounts of training data. As a matter of fact, while a typical neural network contains 4-5 hidden layers, a deep neural network boasts of 10-20 hidden layers.
The higher the number of layers, the more characters it will be able to recognize – but unfortunately the increasing number of layers in the network will demand more time to perform calculations, thus resulting into a bigger complication of training.
Some of the common packages for building deep neural networks are Caffe, MXNet, Theano, Neon, Microsoft Cognitive Toolkit, TensorFlow, and Torch.
The potent rise of self-service BI tools is evident. Tableau, Power BI, Qlik Sense and Domo aids in transforming confidential business information into graphical form that is easily comprehensible. Though a certain amount of effort may be needed at the time of initiation, later on business analysts can freely work on cleaning data and doing analysis seamlessly, while updating the systems automatically with the latest data.
Following this, managers study the analysis graphically and identify key issues. Mind it, these discoveries are important as they guide decisions about future product sales and marketing, discovering powerful trends and coming up with plans of creating additional stores in some never-explored areas.
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In a nutshell, there are several ways to use the power of data to boost company operations. It largely lies on the part of professionals and businesses to make good use of this power, while it’s still in play. So, better buckle up, put on the right shoes and stride away on the data path, before it gets too late!!
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