Simplifying the choice- Data Science vs. Business Analytics
Management guru Peter Drucker once said that only things that can get measured can be managed well. This is the main reason why several companies are looking at data-based studies these days, and why terms such as Big Data have gained prominence. In broad terms, it can be said that while data science involves backend activities for business activities, Business Analytics looks at the front end of problems, which enables business analyst to interact directly with the client.
More about data science
A direct look at data to understand the kind of properties that can be drawn from them is Data Science. The approach is actually problem-agnostic in nature here, where business problems are directly handed over to the Data Science team in the form of data. Dividing a massive amount of data into smaller groups and predicting an outcome is essentially what Data Science does. This science has a number of tools, one or more of which can be used to reach a particular outcome. But it does not usually point towards a business answer. Applications here are very diverse, ranging from predicting the demand for a new product to creating genetic codes.
More about Business Analytics
Business Analytics (BA) also looks at a complex set of data to interpret the same and come up with projections. However, here it looks to answer a number of specific business-related questions. Collection and analysis of data with the help of data science tools is done here. Business Analytics does the following activities:
- Collection of data and its analysis
- Use of predictive analytics
- Generation of visually rich reports for custom dashboards
Upon completion of the above activities, predictive modeling is carried out, which helps companies prepare for future business climates. A very powerful aspect of Business Analytics is ad-hoc reporting, which enables companies to carry out ad-hoc analysis of available business data in real-time, resulting in better and swifter business decisions. Predictive analytics helps business solve problems before they have even arrived.
The objective of using data science is simply accuracy, which is not always the case with BA. In fact, BA takes a look at what can be implemented, and what is really suitable for the client.
Technical skills in terms of collection and analysis of data are better developed as a data scientist. The scientists create algorithms in order to segregate and analyze data closely, and then deploy them. However, the job of a business analyst is to locate data trends for improvement of operations at an organization. This analyst acts as the connecting link between the IT department and the entire working community.
Data mining, which involves looking for data, is the main job of the data scientist. Though the business also needs data, he or she makes use of it to provide concrete insights to the product development teams, sales and marketing teams, and others. Analytics can either be prescriptive or descriptive.
In order to be a good data scientist, you need to have a real passion for mathematics and statistics. For BA, mathematics is not always compulsory, but programming knowledge of languages such as Python and R are essential.
Making the decision- data scientist vs business analyst
As a student, if you are looking to decide whether to do data science or business analytics, a fascination with data and a willingness to understand it deeply should pave the way for a data scientist. On the other hand, if your idea is to solve a real-world problem with the help of data, then business analytics would certainly make more sense.