The big data phenomenon gives a spotlight on those who perform in-depth analysis and can link it to quantitative or statistical modeling expertise with business acumen in finding patterns that foretells the performance, growth and future endurance of a company.
Data scientists rely on analytics, data mining, sentiments, and predictive models in drawing insights from given raw facts which are meaningless. In a structured format, data scientists cleanse the raw facts using appropriate tools, build models, draw graphs and tables, and then attach elaborations to these outputs to give meaning or add value.
Data Scientist module accelerates data science with advanced analytics to extract valuable insights from Hadoop. Stable machine learning algorithms are optimized for Hadoop.
Text analytics extract insight from unstructured data with existing tooling so analytic applications don’t have to be developed from scratch.
Big R statistical analysis and distributed frames allow data scientists to use the entire Hadoop cluster, not just a limited sample. Data that isn’t accurate, secure and available isn’t useful.
Data scientists must be able to quickly and efficiently sort, structure, categorize and present data from internal and external sources, and they need to know the data is trustworthy so they can be confident in their findings and recommendations.
In Sirmirt Data, we select and address the business problems that have the most value to the organization. Armed with data and analytical results, we, present their informed conclusions and recommendations to technical and nontechnical stakeholders, with hopes of relying the most needed statures that can calm challenging scenarios.