From a career standpoint, the roles of a data analyst and data scientist are more complementary instead of one position being at a higher level than the other.
However, if you have the goal to become a data scientist, 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 they are in must be significantly deeper than that of an analyst.
Where they really start to diverge is in terms of technical skills. A great 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. Instead an analyst can provide possible hypotheses, provide data to support them, and then validate and test those hypotheses. It is more the role of a data scientist to dive 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, a data scientist will disseminate that information, so any role in or outside the business can understand the data findings.
Deep technical/computer science skills are also something that a data scientist needs. Since building predictive and prescriptive models typically require accessing multiple data sets, applying complex statistical methods to such data sets and delivering the results in an automated fashion, software development skills are critical.
Possessing these skills is crucial for an aspiring data scientist. However, 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 arrive at an answer, 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 a data scientist 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 and an analyst can benefit just as much from having a great data scientist on their team. But if becoming a data scientist is a personal goal of yours, then we suggest the following steps to get you there:
1. Learn the technology.
Once you have math and statistics under your belt, along with the various methods and tools needed, learn to code really well. A couple of popular programming languages for data science are Python or R.
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 with 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 problems
By implementing the steps above, you will be off to writing models that deliver results you can share with your business, stating what you think will happen. 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.