Using data science and advanced analytics in business
Most of the, time the question of what differentiates a data scientist from a business analyst arises, as both roles focus on attaining insight from data. From a certain perspective, it can be considered that data science involves creating forecasts by analyzing the patterns behind the raw data. Business intelligence is backward-looking and discovers the previous and current trends, while data science is forward-looking and forecasts future trends.
Business decision-making strongly relies on data science and advanced analytics because they help managers understand how decisions affect outcomes. As a result, data scientists are increasingly required to integrate common machine learning technologies with knowledge of the underlying causal linkages. These developments have given rise to positions like that of the decision scientist, a technologist who focuses on using technology to support business and decision-making. When compared to a different employment description known as a “data scientist” or “big data scientist,” however, the phrase “decision scientist” becomes truly meaningful.
Most times, there might be confusion between the roles of business analysts, data scientists, and data analysts. Business analysts are more likely to address business problems and suggest solutions, whereas data analysts typically work more directly with the data itself. Both positions are in high demand and are often well paid, but data science is far more engaged in forecasting since it examines the patterns hidden in the raw data.
