There is enough literature to affirm that learning data science skills can put you on a path to a lucrative career. The profound growth in the field has seen a lot of people switching careers and making a professional transition to data science. Unfortunately, despite employment opportunities soaring, you may not necessarily land a job as soon as you acquire the required skills, or graduate from a formal education program.
Since there is no universal definition of a data scientist that every company agrees on, positions that sound the same may actually come with disparate skill requirements across firms. These roles may also be misleading in that sense and not align with your long-term career goals.
To offset this, it will be a good idea to probe deeper and internally visualise what your career in data science should look like first. Explore the question — where can my new skills take me professionally? — and once you have an answer to that, select a role by its description and one that lines up with your goals and interests.
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There are three broad areas in data science — data analyst, data scientist, and data engineer.
While data analysts are mainly involved in gathering data, organising databases, analysing through exploratory and statistical means, and visualising to find patterns accordingly, data scientists draw insights from data with a key focus on neural networks and other machine learning techniques. They also communicate these insights to stakeholders by employing various storytelling techniques.
Data engineers, on the other hand, manage a company’s data infrastructure and create pipelines for data scientists to streamline their analysis processes. This involves working with a lot of teams within organisations to create data collection strategy as well as with data science teams for simplifying analytics workflows.
Of these, the job of a data analyst is typically considered the most basic in the field of data science. However, even here, responsibilities may vary, as would salary ranges.
Although we cannot cover every potential job title and description that you may encounter in your job hunt, we have filtered down some of the major roles in the data science ecosystem that could be relevant for you as you look to make a career switch.
A data analyst encompasses a wide variety of roles, but your primary job would be to access
data, clean it, extract it, analyse how it aligns with key business questions, and develop models that can form the basis of the company’s strategy. As you climb up the ladder, it will entail working closely with various departments and executives in the organisation.
Since this role is open-ended, you can eventually shift towards the role of a data scientist by focusing on machine learning and continuing to build your data science skills. Alternatively, if your interests are aligned more towards working with cloud (both private and public), DevOps tools such as Docker and Kubernetes, and employing programming languages like Python, Java, Scala, etc., you can gradually work towards a data engineering role.
The general progression of a data analyst is to work towards the role of a data scientist. In fact, oftentimes the primary responsibility in both positions will largely be the same. But data scientists additionally have to build huge ML models and analyse past data in order to make accurate predictions about the future. You need to have a knack for problem-solving using creative means to do well in this role. This also makes this role very interesting as they are allowed the flexibility to pursue their own ideas in order to locate unique trends in data.
Business Intelligence Analyst
The primary job of a BI analyst is to scour historical data of a company to analyse and report market and business trends. This includes quantifying observations and calculating KPIs – tasks for which they need to be familiar with software-based tools, as well as programming languages like Python or R.
Data architects essentially create the database of a company from the ground up. They primarily design the way it will be used for a wide range of businesses and solutions. Although this is not core data science, it still involves a lot of work with data, including its design and the way it is processed. They also often collaborate with data scientists in the company to jointly work on common business goals.
To work as a quantitative analyst, you need to have an in-depth knowledge of statistical techniques. This is because they are expected to use advanced statistical analyses to make predictions related to matters of finance. Here, you have a good opportunity to use the data science programming skills you acquired over time. You are also expected to have a good understanding of how to apply ML models to undertake the quantitative analysis.