Introduction to Business Analytics


The word analytics has come into the foreground in the last decade or so. The proliferation of the internet and information technology has made analytics very relevant in the current age. Analytics is a field that combines data, information technology, statistical analysis, quantitative methods, and computer-based models into one. 


Data Scientist, Emphasis on Analytics

Data Analytical scientists deal with math, statistics, understanding the trends, solving complicated data models, etc.  A major task in the job is the data mining and analyzing of the data extracted.

Quantitative Analyst / Modeler

A quantitative analyst/modeler helps in understanding the data models to support the organization in taking the major decisions in the organization. Quantitative analysts generally belong to the financial industry where the analysts need to undertake and manage risky situations and decisions.

Business Analyst

Business Analyst or the Data analyst need to build up the visual representations of the data and provide appropriate information to decision-makers in the organization. A business analyst works on different tools and techniques to understand complex data models.

Business Analyst (Manager / Consultant)

A Business Analyst Manager should have the knowledge of technical tools, data analytics foundation methods, data gathering, and processing which ensures appropriate analysis of the data models. Good communication skills, leadership qualities, strategic thinking are the major skills required for a Business Analyst Manager or consultant.

Pricing and Revenue Analyst

A revenue analyst’s primary job is to analyze a company’s finances. Their insights on financial data will help boost revenue growth.

Difference Between Data Science vs Business Analytics

Difference Between Data Science vs Business Analytics

Both Data Science and Business Analytics involve data gathering, modeling, and insight gathering. The difference between the two is that Business Analytics is specific to business-related problems like cost, profit, etc. whereas Data Science answers questions like the influence of geography, seasonal factors, and customer preferences on the business. In short, Data Science is larger or superset of the two.