Data Scientist or Data Analyst?
First of all – you have to know that the term Data Scientist recently became a buzzword. Thus, you can find a wide variety of data topics under the umbrella of Data Science – especially in online articles. Because of that, unfortunately there is no clear definition on how data science and analytics are different.
However, you can see a clear pattern in job descriptions, that at least illustrates the small differences between Data Scientists and Data Analysts
For instance, when companies are hiring a Data Analyst, they are usually looking for a person who will be working on research projects, on optimization and on reporting. This person will help the company to understand their customer base and flag possible issues and future opportunities.
When a company is looking for a Data Scientist, it usually wants someone on board who’s good at predictive analytics and who has experience with machine learning and similar advanced methodologies. This knowledge can be useful for managing risk, for building recommendation systems, for optimising resources, for face recognition and many-many more things – depending on the profile of the given company.
As I see it, Data Analytics is usually mentioned as the conservative
part of the data projects and it has a big effect on the business side –
while Data Science is more progressive and it can even have an effect
on the product itself.
Note that – in my opinion at least – both of these roles are equally important.
Let’s say, we have a video sharing portal. Our Data Analytics team
will create reports on how many video uploads we received, and who are
our best users; they might also do A/B tests to find the best placement
of the UPLOAD button.
On the other hand the Data Science Team will build the algorithms behind the recommendation system that autostarts the next video – and they might do churn predictions too.
In terms of required coding skills, business skills and domain knowledge, the difference between a Data Analyst and a Data Scientist is not substantial. It’s good to know though, that a Data Analyst – as it’s closer to the business part – might need better business skills, while a Data Scientist, who has to implement complex methods, might need better coding skills.
However the biggest contrast between the two is mostly in the statistical and mathematical methods that they apply during their data projects