We are living in the era of big data; a decade before it was a huge appertains for the industries. Main purpose was to build a framework and resolution for the storage of data. Data Science is what can help both in big data storage and adding the value to business.
Data Science is the field that combines statics with computer science conceptions like machine learning and artificial intelligence to excerpt sense from complex and big data. Now, the question is what does this big Data actually means; it’s not just 200000 lines of data. Volume can’t define data as big but it is determined by the variety, velocity, variability, veracity, and many other characteristics.
Usually, the data used to be small in size and essentially structured. It was analyzed simply using Business Intelligence Tools. But now, almost all the data is semi-structured or completely unstructured. It is said that by 2020, approx 80% of the data will be unstructured; for that more advance and complex analytical tools and algorithms are used for processing and analyzing big data.
Now, companies uphold in growing data science teams and build open source-centric tech stacks so as to find measurable value in big data. The role of data scientist is getting increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.
One of the benefits of information science is that organization will realize once and wherever their product will sell best. This will facilitate the deliverance of the suitable product at the proper time and can benefit corporations to develop new products so as to fulfill the needs of their customers.
Data scientists collect the data, which is pre-processed according to case specific. After collection, data analysis is done that includes extract information and is present in the form such as metrics, KPI, reports and dashboards. Now is the future of predictive analytics. It assesses the potential future scenarios by using advanced statistical methods. It utilizes artificial intelligence to predict behavior in unprecedented ways.
In the past, to analyze the data analyst used to work with software’s like excel. But now with advancing technology new tools help data scientists to analyze data by using programming languages like Python, R, SQL, MATLAB, C, C++, JAVA script, Scala and more. Analyses are now done on the basis of user experience, fraud detection, sales forecasting, and client retention.
The top tools used in 2018 are Phython, followed by R and SQL.
