From a career standpoint, the roles of a data analyst and data scientist are more complementary rather than one position being at a higher level than the other.

However, if you are currently a data analyst and looking to make the switch to data science, then you have come to the right place. Keep in mind that it’s also possible to make the switch from data scientist to data analyst. While each of the roles has significant differences, there is also a fair bit of overlap.

Firstly, a data scientist needs to be able to work and show value in business terms. This means their knowledge of the business and industry must be significantly deeper than that of the data analyst.

Where they really start to contrast is in terms of technical skills, a great data analyst should be able to look at any set of data from any tool, derive meaning from that set of data and then move beyond that to discover ‘what is happening? ‘

Analysts tend to generate a lot of questions but don’t always have the answer to those questions. The data analyst provides possible hypotheses, provides data to support them, and then validates and tests those hypotheses. It is the data scientist who goes more into details of the ‘why?‘

Data Scientists are more future-oriented. They are focused on discovering what will happen and what the business can do about it based on their findings from various scientific methods. From these methods, the data scientist will disseminate that information so any role in or outside of the business can understand the data findings.

Deep technical/computer science skills are very important in the role of the data scientist. Since building predictive and prescriptive models require accessing multiple data sets, applying complex statistical methods to such data sets and delivering the results in an automated fashion are critical.

Possessing these skills is crucial for any aspiring data scientist. While you can be great at using and developing all the data science tools in the world, if you don’t properly understand the business problem and the underlying methods to solve these problems, you could cause more harm than good!

Making the move

Just to be clear, we are not suggesting that all analysts should strive to become data scientists because for a lot of people, it is not a natural progression. A data scientist can benefit greatly from having a great analyst on their team, whilst an analyst can also benefit just as much from having a great data scientist on their team. However, if becoming a data scientist is your personal goal, then we suggest the following steps when making the move.

1. Learn the technology. 

Once you have math and statistics under your belt, along with the various methods and tools needed, you need to learn to code really well. Python and R are the most popular programming languages for data science that you should learn.

2. Learn the tools of the trade. 

Learn some of the different methods and techniques. A good place to start would be linear regression, decision trees and neural networks. If you understand the math and statistics from step one, you will better understand the various pros and cons of each of those methods and techniques.

3. Hit the books.

Learn the fundamentals of math and statistics. It is critical to understand the underlying calculations that the tools and libraries are performing behind the scenes, before applying them to business problems.

By implementing the steps above, it won’t be long until you are writing models that deliver results you can share with your business. Over time you can measure your predictions and grow your technical skills, so you can work with your development team to integrate your predictions and optimize digital properties.

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