While people often use both the terms synonymously, this creates confusion due to a rising amount of opportunities for data scientists and data analysts. Conducting a business today without a data management function is unheard of. Infinite amounts of data are interpreted by the system of data science. Those deciphered data can be used in both businesses and other organizations. The analysts cover the processing of particular data. They identify, capture, and then organize the data in a manner that helps the businesses gain significant knowledge.
What is data analytics?
In data analytics, the data is mined and classified into categories which are later examined to draw meaningful insights. In simple terms, it is the analysis of data in both qualitative and quantitative terms to gain improved productivity in a business. These interpretations assist in enhancing the efficiency and profitability of a business. Industries then find taking crucial organizational decisions as much less complex.
According to a survey conducted by Harvard, data analytics is one of the highest demand careers today, and in the upcoming years it might scale to a startling new height globally.
Predominantly, data analytics refers to an array of programs, ranging from business intelligence at the basic level and OLAP to a myriad of advanced analytics.
Who are data analysts?
Analysis is not a simple task of mere programming. Analysts need several tools like Visual r, R, and Tableau languages which are necessary for performing analytical functions. Data Analysts are professionals who perform these activities to identify the camouflaged truths from within the chosen data.
Industries employ analysts chiefly to understand customer behaviors and their requirements and demands, and in turn provide them with lucrative products to satisfy those demands.
Analysts have a broader focus. They can serve to identify opportunities to gain an increase in business revenues, improve operational adeptness, hone in on campaign strategies to develop while being aligned with market trends, and to gain competitive advantage – everything leading to a stupendous boost in business performance.
Difference between data scientist and data analyst:
A data scientist’s roles involve predicting the future based on past data patterns and generating his own questions while addressing the problems which might have a high return value. A data analyst looks at the data patterns from a different perspective. He is responsible for solving the answers to predefined questions and addressing only business problems. Although both the roles demand almost equal qualifications, a data scientist is required to possess a stronger business acumen than a data analyst.
The analyst analyzes the data from one particular source; hence well-developed visualization skills are not mandatory. Machine learning might not come as an important part of data analytics, as is building statistical models, which are not only a core objective for a scientist, but they have to be polished in specific knowledge and communication. Owning a degree in software engineering might be one of the preferred qualifications, but the applicant also has to have a firm hold in mathematics and algorithms.
An interest in the field of data analytics has a bright future if the correct training is acquired. If you believe you possess a potential in the analytical department, data analytics is the place.360DigiTMG is considered as the best data analytics course institute in the country.
Address: 360DigiTMG – data analytics, IR 4.0, AI, Machine Learning Training in Malaysia
Level 16, 1 Sentral,, Jalan Stesen Sentral 5,, KL Sentral,KL Sentral 50470 Kuala Lumpur, Malaysia
phone no: 011-3799 1378