Many people have always been confused in between Business Analytics and Data Science. Whatever there may be the reason behind this, one thing is for sure that both industries and undergoing skyrocketing growth.
Today, the current market size for Business Analytics is $67 Billion and for Data Science it is $38 Billion. The market size in 2025 is expected to reach $100 Billion and $140 Billion, respectively. It shows the demand and expansion of this industries in future as well.
I have come across many professionals who want to choose Business Analytics or Data Science as their careers. However, they are unsure of the differences between them. Hopefully, this article will give them a head start in not only choosing the right career which aligns with their set of skills but also will help others in transitioning into these extremely exciting new domains that are in demand nowadays.
Business Analyst vs Data Scientist – A Simple Comparison
Let us take an example of an exciting electrical vehicle start-up. This start-up is now big for creating job families. And, they have decided to create three job families, one is a scientist, and the other two are an engineer and a management professional. Now I want you to take time and imagine what kind of role they play in the company.
We can interpret their roles from the general level of understanding:
- Scientist: He work on solving complex technical problems like building an efficient battery, or how to improve the design of the vehicle. His services are primarily based on research and development. Although it does not affect the overall sales and profit of the company, but it has a direct effect on the overall product that the company manufactures.
- Engineer: Primarily, the responsibility is to take the progress in result of the research and developments into production for the vehicle. For example, making the assembly lines for manufacturing of the new and improved vehicles whenever needed and approved from the relevant stakeholders.
- Management: They solve management related issues on a day-to-day basis. For example, to find the right market or locality to open a new store. Additionally, decisions regarding sales and marketing are also under these professionals.
- Data Scientist: He works on complex problems and specific problems in bringing non-linear growth to the company. For example, assessing the problems of the company and coming up with solutions and predicting via certain and relevant statistical methods.
- Data Engineer: These professionals implement the outcomes from the Data Scientists on a larger scale to be implemented within the company. For example, implementing a credit risk Machine Learning algorithm by the Data Scientist within a bank.
- Business Analyst: They act as a liaison between the Management and the Information Technology Department daily.
Now that we have our basic analogy clear, let us see the kinds of problem solved by data scientists and business analysts.
Skills and Tools Required in Business Analytics and Data Science
These professionals must be proficient in presenting simulations of solutions for the business problems and advice on business planning.
Some of the tools used extensively in business analytics are Excel, Tableau, SQL and Python. The most used techniques in the corporate world are statistical methods, Predictive modelling, Forecasting and storytelling.
A data scientist must be proficient in Linear algebra, programming, computer science fundamentals. Some examples of data science projects vary from building recommendation engines to personalized E-mails.
The common tools of a data scientist are R, Python, scikit-learn, Keras, PyTorch and the most widely used techniques are Statistics, Machine Learning, Deep Learning, NLP, CV.
And for both the roles, structure thinking, and problem formulation is a key skill to do well in their respective domain.
Career Paths for Data Scientists and Business Analysts
A Data Scientist’s strengths lie in coding, mathematics, and research abilities whereas Business Analysts needs to be a strategic thinker and have strong skills in managing projects.
Generally, Data Scientists are on the key roles within the organization of they have an entrepreneurship role with a strong technical background. On the other hand, Business Analysts tends to make business roles, strategic roles or roles which decides on which paths an organization should move towards at future date.
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