With so much data flowing in every day, there is a huge need for skilled professionals who can derive meaningful interpretations from this data. So much can be done with this data at hand – analysing, visualising, modelling, predictions. Not all of this can be performed by one individual. All of these require different skills in the data and analytics industry. Data analyst, business analyst, data engineer and data scientist — these job titles, to an outsider, might sound very similar — all working with data and analysing it. But in reality, these job profiles are actually very different. Still, there is a lot of overlap that exists in these fields and acquiring and mastering the required skill set might help one enhance their job prospects and enter into a more challenging role.

Often, professionals who enter the analytics space as data analysts desire to move into the role of a data scientist. A data scientist’s job is more challenging and rewarding, which has led to a huge surge in professionals flocking to this field. 

Role of a Data Scientist Vs that of a Data Analyst

Some of the core functions a data analyst performs include:

• Mining data from primary and secondary sources
• Interpreting this data to study its patterns to solve business problems with the help of statistical tools
• Cleaning data to remove information that is not useful 
• Using the information deduced from the data to provide reports that can help in business decisions

A data scientist, on the other hand, has the following responsibilities:

• Building models to solve business problems as per the needs of the business
• Creating algorithms and machine learning methods to test the data
• Using various visualisation methods to present the data and different findings from it
• Syncing the information from the data, deep-diving into it to provide ways to solve the business problem at hand

Data Analyst to Data Scientist. How to make the transition?

Before diving into the ways one can transition to a more challenging role of a data scientist, it should be made clear that this is not an overnight process. Being a data scientist requires a combination of different skills, including a solid grip over mathematical and statistical concepts, a good hold over programming languages, and, most importantly, understanding a particular business problem and how to solve it through data analysis and prediction.

Here are a few steps to take to start your transition journey:

Build up your core domain knowledge

Before even thinking about making the transition, one has to be very clear about what a data scientist does and introspect what has to be done to fill the gaps that are needed to make the transition and the skills the person has now. A data scientist not only handles data but provides much deeper insights from it. Other than gaining the right mathematical and statistical know-how, training yourself to look at business problems with the mindset of a data scientist and not just like a data analyst will be of great help. This means that while looking into a problem, developing your critical thinking and analytical skills, getting deep into the problem to be solved at hand, and coming up with the right way to approach the solution will train you for the future.

Improve your coding skills

A data analyst might not have great coding skills but surely has to know it well. Data scientists use tools like R and Python to derive interpretations from the massive data sets they handle. As a data analyst, if you are not great at coding or don’t know the common tools, it would be wise to start taking basic courses on them and use them then in real-world applications. 

Take introductory courses in data visualisation, ML, deep learning

Along with learning certain tools, getting introduced into the world of machine learning, deep learning, and decision trees would just add to one’s growth. Of course, no one expects you to become a pro from the very start, but developing interest and deploying such algorithms in projects will surely benefit you in your career. 

Sachin Birla, who works as a data scientist at EY, says, “Typically, a data analyst only works with tabular forms of data, but nowadays, we see a surge in image and text data. For image and text data, traditional machine learning algorithms fail, and new deep learning algorithms or models are getting popular. So if you are thinking of making the transition to data science, you should learn machine learning as well as deep learning algorithms. Apart from that, you should have good knowledge of databases, basic maths, algebra, statistics and Python programming. So, the combination of all given skills will make you a good data scientist.”

Explore your skills outside work

Taking part in hackathons, contests, and Kaggle competitions will help you boost your confidence and understand if you can really apply the concepts in real-world scenarios. Even if you do not perform amazingly well initially, keep pushing harder. More and more practice and participation will show effects in the long run.

Learn to develop a “data scientist mindset” at work

A great way to develop this would be to learn from data scientists who work with you. Try to brainstorm with them and also figure out how they approach problems. Getting an idea about their thought process while building algorithms would help you understand the nuances of the job and how to build your thinking capabilities. 

Always stay updated

Data science is an ever-evolving field. One must always keep learning and keep updated to stay relevant here. A great roadmap for an aspiring data scientist would be to follow data science leaders on social media, read about the latest research being done, connect with other data scientists, and attend data science conferences to stay motivated in their transition journey.