Subscribe to DSC Newsletter

5 Steps from Business Analyst to Data Scientist

In the past, the terms business analyst and data scientist have sometimes been used interchangeably, and indeed, in a small company, the lines between the two sorts of jobs may blur.

But as more and more companies look to big data for business insights, they are shifting from relying on business analysts to predict what the future of a business might look like, and moving towards using data scientists and machine learning to interpret data and predict trends.

What’s the difference, you might ask?  While the end result of these two jobs is often similar, a business analyst and a data scientist use different tools to get there. In general, data scientists have much greater technical expertise, especially in computer programming, systems engineering, and statistics.

Business analysts, by their very nature, rely on intuition and have human biases that are starting to be seen as flaws that put them at a disadvantage compared to the cold hard facts that data scientists can produce.  In addition, business analysts are often concerned with the single truth of what did happen in the past, while data scientists are working in a much more fluid version of what might happen in the future.  Wired magazine, among others, has predicted that data scientists will supplant business analysts in the coming years.

So what’s a good business analyst to do?  Transitioning to become a data scientist is a definite possibility.

Compared to other professions, business analysts do have some distinct advantages if they want to transition to become a data scientist. For instance, a business analyst often:

  • has expert knowledge of their industry, which is vital to understanding and analysing data
  • is already familiar with data analysis using spreadsheet and database tools
  • and has strong communications skills and the ability to relay complex information to a layperson.

However, to transition successfully, a business analyst will need to follow a few steps to upgrade her skills and resume:

  1. Brush up on your statistics. Data science requires a good deal of math, especially statistics, so if it’s been a while since your university level statistics course, make sure you get a refresher.
  2. Get a crash course in machine learning. Understand what it is, how it works, and how people are using it. Machine learning is the engine that drives much of the data analysis happening today.
  3. Learn to code. If you don’t already have a coding language under your belt, it’s time to learn. If you have some coding experience, take your skills to the next level with another relevant language. Data scientists who can build their own systems and algorithms are vital.
  4. Get some real-world practice. Whether that means developing a side project at your current job or putting together a passion project in your spare time, most companies want a data scientist with experience.
  5. Join the community. It’s OK to be the new kid at the table, but you definitely want to join the conversation. Join some websites and forums and follow some of the thought leaders in the industry to stay on top of new trends and ideas.

In my opinion, a business analyst is the perfect candidate to transform her skills into a data scientist.  Those who take a proactive approach to improving their existing skills and going after any they may be lacking won’t have to worry about layoffs or being replaced; their industry expertise will go a long way to making up for a learning curve acquiring new skills.

What do you think? Will data scientists replace business analysts? Or will both professions be needed in the coming years?  I’d be interested in your thoughts in the comments below. 

Read other Bernard Marr articles.

DSC Resources

Additional Reading

Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge

Views: 16533


You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by Mohammad ElSofany on April 12, 2017 at 12:21am
@Szymon, you are mixing up Data Scientist with Data Engineer or Data Stewart.
A Data Scientist must have the Domain knowledge of any business before dealing with it.
He must listen to people along with Data as well
When it comes to his product endpoint.. A Data scientist must also know how to deliver his findings wrapped with what business people can understand.
On another note, A data scientist must understand Deep technical ML, Stat and acquire some Programming tools as well.. so it's not only programming language who needs to have under his belt .
Comment by Christoph Tertel on March 15, 2017 at 7:39am

Thanks for your Post. You describe a Little my Situation. But how would you define a Dataminer?

Comment by Defne Sekkeli on March 9, 2017 at 2:53am

Thank you for this interesting article. I am a business analyst for almost four years, and now I plan to switch my career to be a data scientist. It helped a lot to develop myself. And also, I agree with you, most of the business analyst positions will be replaced by data scientists in the future.

Comment by Szymon Drejewicz on May 17, 2016 at 1:16pm

Well, it looks that the author doesn’t understand the role of BAs and Data Scientists. As a business analyst I have never been asked to predict the future. Business Analysts are much more connected with people, especially business people. So, replacing BAs by Data Scientists sounds for me like replacing System Analyst by Coders (Developers). On the other hand, many of BAs are coming from IT world, they often start from Programmer, then System Analysts and finally they decide to become Business Analysts. And it has nothing to predicting trends or future. 

In my opinion, many of BAs (including me as well) have very strong technical background. Because the have collected the experiences during a lot of project going last 10-20 year. And to be honest most of the projects in this time were and are related to digital transformation (implementation, deployment and integration of Information Systems). I doubt typical Data Scientist with even 5-years experience knows anything valuable about SOA or ESB (including low level things). So, it is completely different role with totally different responsibilities.

Business Analyst works mainly with people, Data Scientists works with… well “data”. So, it sounds like a yet-another-programmer. A guy who prefer not to have problems with humans. 

AFAIK many of BAs have Master of Science grade in Computer Science. So the math and statistics are one of many skills already “available”. I can agree that applied version of ML is today very interesting, but please consider that typical student of Computer Science knows ML as a one of many metaheuristics (algorithms). “Learn to code” sounds like good advice for Data Scientists too. Knowing R, Python or SAS doesn't mean that you know a lot about programming.

With real-world practice is another problem and I think is the main problem of the Data Science at all. Data Science is really interesting thing but there are problems with valuable data and experienced people. And that is something what you can hear from Business Analyst. Because BA knows that some ideas even if sound very good, and “numbers are fine” with such low level of commoditizaiton are useless. 


Ask a Data Scientist to organise a workshop with non-technical people and then you will understand why you need BAs in almost each organisation.

Comment by Kening Ren on November 2, 2015 at 2:43pm
Thank you for pointing out the difference between business analyst and data scientist. This also applies to transforming from sw engineer to data scientist at certain level.

Follow Us


  • Add Videos
  • View All


© 2018   Data Science Central   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service