As we know that, “Data is the new oil” and we’re surrounded these days with data everywhere!
Using this huge corpus of data to derive insights and develop products demands skills such as analyzing, visualizing, modelling and predictions at the final stage. It is difficult for one team or individual to perform these tasks.
Hence, the roles that we come across these days came into existence.
Be it Data Engineers, Data Analyst, Business Analyst, or Data Scientists. All of these profiles have “data” in common but the profile and day-to-day responsibilities for these roles are very different.
Though there are many overlapping skills in these roles but on a granular level, these are very unique to each. Moreover, we see that people start their data journey by mostly entering into a role of “Data Analyst” where, they learn to play with data to generate insights by making visualizations such as charts and graphs.
Usually “Data Analyst” is a great starting point for people who want to have their career grow in Data space and along with that, most Data Analyst aim to transition their role from “Data Analyst” to “Data Scientist”.
The role of “Data Scientist” has gained a massive popularity since last 5 years. Professionals are flocking to this field and trying to grab the role for advancing their career profile and also to maximize the financial rewards from the role.
But, Is there any specific pathway to jump on from Analyst to Scientist?
Maybe not, but let’s first understand the roles of Data Analyst and Data Scientist
The role of Data Analyst involves process where practitioners collect, analyze and take out insights from the structured data to draw out data-based concrete decisions. This is kind of specialized role that spans across the data industry one is working in.
If we jot down the rough tasks of a Data Analyst, It can be :
1) Data mining from the sources
2) Studying data patterns for solving the business problem through statistics
3) Data cleaning to eliminate trivial data
4) Making reports/dashboards for business stakeholders to enable better decision making
However, the role of Data Scientist is in a broader spectrum where data analytics falls to be a single aspect. The data scientists usually work on building the machine learning models for predictions. The approach with which Data Scientist works is kind of a high level work right from data analysis to predictions whereas data analytics is one part from that whole process.
Few of the tasks that Data Scientist perform are:
1) Machine learning model building to solve business problems
2) Developing machine learning algorithms to have better predictions
3) Collecting the information from the data to solve the problem at hand for business
To make a successful transition, one must start developing the skills needed for data science.
A) Develop your programming skills
— As the data scientists are involved in model building, languages such as R and Python would come very handy to them every time One must have core skills around these languages to understand the model building part
B) Develop statistics/maths skills
— Maths and statistics are at the center of Data Scientist’s role. Starting with the basics of these subjects would help a lot while gradually transiting from data analyst to scientist
C) Stay updated and develop business mindset
— One must be aware of the technological advancements around Machine Learning and AI as a data scientist. Along with that, there should also be a mindset where business is to the center of all the technologies that are built.
So, these are some ways which can help you in this transition!
