Data Science is a powerful tool whose impact has been evident on big companies like Amazon as well as startups like Snapwiz. Data Science has become an integral part of companies such that every company have a dedicated team for it and follow the data-driven business model. Data-driven science or simply Data Science is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in the structured or unstructured form.
Predictive Analytics is a derivative of Data Science encompassing a variety of statistical technique from predictive modelling, machine learning and data mining. Which enables an analyst to analyse current and historical facts to make a prediction about recurring and/or unknown future events.
Tools and Techniques in conjunction lead to an innovative solution and makes a professional ‘Industry Ready’. With tools like Excel, R, SAS(including Proc SQL), and concepts like advanced analytics, predictive modelling and machine learning this training enable the candidate to conquer the art of innovation.
Evolved over last 4 years as per the industry requirements, this business analytics training is aimed to provide a job oriented Data Science and Business Analytics Skills.
Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who want to head start their career in analytics.
R programming language is the stepping stone to the machine learning. The open source R is a GNU Package and provides libraries implementing a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests etc.
Strengths of R includes publication quality static graphs, latex integration and documentation formats and flexible code manipulation with Python, Java, .NET, C++ and C.
Candidates like software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming should consider this training
Python is a multi-paradigm programming language with design emphasis on code readability. The dynamic on the fly processing of python with extensive library support and extensions enable it to be among the most useful driver for Data Science.
The huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.
Candidates like software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming should consider this training
