The world is constantly changing & shaping around trends and one such trend is Data Science. It is one of the most sought-after career options for today. I have come across innumerable clients who want to enter into this career but have little clarity on what to expect and what is expected.
So let’s understand the basic difference between data engineer, data analyst, & data scientist roles:
➡️Data Engineer – A data engineer makes sure that the right data is in the right hands. They design and maintain the infrastructure and pipelines that converge terabytes of raw data from a variety of sources into a central location where the organization can access clean & relevant information.
➡️Database Administrator – Ensures that the databases are available to all the required users, are maintained properly & function with no hiccups whenever new features are added.
➡️Data Architect – lays down the foundation for data management systems to ingest, integrate and maintain all the data sources. This role requires knowledge of various tools and techniques.
➡️Data scientists – Investigates, extracts & reports meaningful insights from the data & communicates these insights to non-technical stakeholders. They deeply understand the machine learning workflow and can spot its applications across the company. They work almost exclusively with coding tools, conduct analysis, and often work with big data tools. However, data scientists and data analysts’ biggest differentiator is that data scientists are tasked with building data products. These could be dashboards to be accessed within the organization, machine learning models that automate a business process, or other related data products.
➡️Data Analysts – Do what’s described in their job title they analyze the data. They are responsible for analyzing data and reporting insights from their analysis. They use a combination of coding and non-coding tools and report their insights to drive the business agenda.
For eg, in the case of a food-delivery application, a data engineer would extract raw data from the application database, transform it into analysis-ready data, and load it into a database. A data scientist would be tasked with developing a predictive model to predict the supply of delivery resources needed at any given moment to allocate incentive planning. In contrast, a data analyst would be tasked with analyzing historical food-ordering data to answer critical business questions.
However, as data roles mature, it’s important to note that there will be even more distinction and therefore choosing the right path is all the more important.
DM me for a professionally built Résumé, Cover Letter, and LinkedIn Profile that resonates with Data Scientist, Data Analyst, or Data Engineer roles.
#careercoach #jobsearchstrategy #resumewriters #resumewriter #cvwriter #resumewriting #resumewritingservice #resumetips #careerfaktor