Undoubtedly, there are many similarities between business analytics and data science.
Importantly, each of these fundamentally data-driven processes draws on the findings obtained through the analysis and evaluation of statistical data to help inform strategic decision-making. The skills required for each of these professions are also very comparable. From mathematical abilities to critical and analytical thinking – the foundational requirements to perform these roles are almost the same. There are, however, some key differences – which lie mostly in the way each of these processes is executed.
Stay with us as we explore the main differences between business analytics and data science, as well as the key characteristics of each concept.
What is Business Analytics?
Essentially, business analytics is the process by which business data, statistics, and internal processes are analyzed, assessed, and evaluated. A skilled business analyst’s ultimate goal is to assist a business in making better, more strategic decisions, as well as solving issues and problems that can be avoided by making data-informed choices.
Business analytics can be both a lucrative and rewarding career. If you are interested in becoming a business analyst, however, you will need to complete a tertiary qualification – such as an online Masters in Business Analytics, for example.
Completing this type of degree can prove to be well worth the effort, though. With more and more businesses today becoming increasingly data-driven, the services of an individual who can skillfully perform business analytics are currently in high demand. As such, if you complete a qualification as a business analyst, the career outcomes that are available to you will be abundant! Of course, you also will need to possess the necessary skills and knowledge required to be a business analyst. Some of these skills include analytical and critical thinking, mathematical skills, and of course, problem-solving abilities.
What is Data Science?
Data science leans on computer programs and software to analyze data and statistics. As such, it is heavily focused on computer science and programming. For this reason, data scientists need to be highly computer literate and adept at using programming software.
So, how to become a data scientist? As well as completing a tertiary qualification in the discipline of data science, it is also important to build a portfolio of your work. This will enable you to become professionally employed as a data science consultant. Of course, just like a business analyst, data scientists must also have strong mathematical abilities – including skills in algebra and calculus. Being a data-driven role, a successful data scientist must also have strong analytical and critical thinking capabilities, as well as the ability to accurately analyze and assess statistics.
There are however some key differences between the two professions. To learn more about the differences between business analytics and data science, just keep reading!
What are the Differences between Business Analytics and Data Science?
Admittedly, there are some significant similarities between the role of a business analyst and that of a data scientist. These similarities mainly relate to the skills required of an individual who chooses to pursue a career in either field. That is to say, data scientists and business analysts must both be competent in math, and skilled in data and statistical analysis. There are, however, some differences – although, these can be somewhat difficult to define! For this reason, entire schools of thought have been dedicated to discussing data science versus business analytics.
Essentially, the main differences between the two lie in the outcomes of each of these processes. For instance, business analytics uses data analysis to identify and resolve business-orientated problems and issues. Data science, on the other hand, draws on algorithms to pre-empt and predict these issues ahead of time. For this reason, data science is considered by some experts to be more effective than business analytics.
Furthermore, data science is a somewhat more modern approach to data analysis and evaluation. Data science, as a concept, was only founded in 2008. Business analytics, on the other hand, has been around since the late 19th century. For this reason, data science draws on more advanced technology for its approach and leans heavily on computer software and analysis of digital algorithms to execute its methods.