Data Scientist vs Business Analyst

Difference between Data Scientist and Business Analyst

Data is playing a major role in the growth of any business exponentially. For the data to be understood with its trends, it requires lots of analysis and research. It requires special skills which help in understanding the pattern of data and to come to a conclusion that how will the data lead to a growth of business and how changing functionalities will bring in the necessary change. This job is mutually done by data scientists and business analysts. Though both these roles help in the expansion of any field, they both Data Scientist vs Business Analyst have their own roles and responsibilities which differ in their own ways. Let us understand the differences that are there between a data scientist and business analyst. Although the main motto of both these jobs is business growth, the variance in the actual work that they do will be seen further.

Head to Head Comparison between Data Scientist and Business Analyst

Below is the Top 5 difference between Data Scientist and Business Analyst:

Key Differences Between Data Scientist and Business Analyst

Though both these roles seem to have a similar difference between Data Scientist and Business Analyst differ in following ways:

A data scientist needs to analyze large amounts of data, should be able to manipulate and make necessary changes using mathematical and statistical operations. They also need to discover new patterns and make future predictions. They must have technical knowledge and also should know languages like Python, R, etc. On the other hand, business analysts must have knowledge of end to end business. They should know the impacts of changes with it and try to bring out changes that will increase customer as well as employee productivity. They should collaborate and communicate constantly with stakeholders and have a clear-cut picture of needs. They must also help in designing the IT system from a business point of view and coordinate with them.

The need for data scientists came up when we had an ever-increasing need for synchronization between data and IT industry. All departments in a company require a data analyst these days. They provide a sophisticated analysis through their programming expertise and without waiting for any inputs from IT industry. They just require data and they can go ahead with their analysis which will bring the organization to a new competition level and also unfold hidden trends and patterns which will help the organization lead in the market. Business Analysts are needed to bring a change in the existing functioning of the business. They must analyze the current practices and bring a change which will be more effective and profitable to the organization. They should come up with questions with project customer, the end users and subject matter experts. Next, the total requirements that are gathered need to be documented with the definition and need for the change. Business analysts are the ones who bring precision to estimates in the project schedules.

The duties of data scientists involve data visualization where they need to explore the data and find hidden details from the data which will reveal the current trends and also help them model patterns which in turn help in predicting future recommendations. They must be well versed in machine learning and data mining which will help in building analytics applications for having high profits in the market. They must communicate technical findings to sales and marketing teams. A business analyst needs to identify stakeholders, analyze and document the requirements. They must evaluate the proposed solutions and communicate them with all stakeholders. Once that is done they will be executing the changes with a development team and following up with deadlines. They are also expected to conduct user acceptance test and gain acceptance from a client. After this, they are also responsible for creating user manuals and final documentation.

The main tools that a data scientist uses are data warehousing, data visualization, machine learning and languages like Python, R, and SQL. Business analysts, on the other hand, have commercial software’s like i Rise, Jama, BitImpluse which help in providing solutions across different industries.

Data Scientist and Business Analyst Comparison Table

Following is the comparison table between Data Scientist and Business Analyst

Basis for Comparison	Data Scientist	Business Analyst
Basic Difference	Data Science is all about finding out new things, a revelation of new data which will solve complex problems. Finding conclusions through statistics through mere observation and gradually reaching the perfect optimized solution is the job of a data scientist	Business Analysts are a platform between IT and business stakeholders. They need to have a deep business knowledge and need to be involved in demanding questions to get value for money and bring value to developments done in IT industry.
Requirement	A data scientist needs to have knowledge about all latest tools, SQL and if required they may need to code. They should have in-depth knowledge of mathematics and statistics.	Business analysts may not require any technical knowledge. They must be comfortable in assessing changes, developing business cases and defining new requirements or changes in a project from the functional perspective.
History	Data analysis though seems to be a new rage these days, it dates back to 1962 when John Tukey wrote about ‘The Future of Data Analysis’. Post that there were mentions about this and it started trending from 2006, through 2011 till now where data scientists are the most sought job profiles.	Business Analysts came to the rising in the 1970s when they started documenting all manual processes. They found the need to automate repetitive tasks, identify problems and deliver good quality technology at the expense of business needs. Through 1980s Business Analysts evolved to support business goals and be a mediator more effectively between IT resources and business resources.
Responsibilities	A data scientist has to handle and extract large amounts of data. This requires in-depth knowledge of SQL to segregate datasets. They must have advanced knowledge of machine learning so that they can make changes in data by themselves and get a deeper insight.	Business Analysts need to gather and prepare requirements. They must prepare documents and also analyze and model all requirements. Post analysis they must take over the changes that are required and convey the same to IT team. Once changes are done they must perform acceptance testing to check if the requirements are met.
Tools	Tools of data scientists are none other than Data warehousing, Data visualization, and machine learning.	There are various tools for business analysis like Blueprint, Axure, Bit impulse, etc. which make improve productivity.

Conclusion

Thus, both of them perform the job of increasing the value of a business. The different roles and responsibilities that they perform help an organization knowing its value and they provide a way of improving and increasing its market value. The process improvements by business analysts and the predictions done by data scientists assist the company to have a safe present and a bright future.

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