Inside a Day in the Life After Post Graduation in Data Science
A lot of mid-career Indian professionals see a data science post-graduation program as the key to a new future. The promise? A job focused on being an expert in AI, analytics and machine learning. What steps come after you have received your certificate? Is everything just about being creative with code? Not quite. The job of a data scientist is not always clear, since it depends on the company’s needs, how it is organized and the level of data experience in the organization. In this article, you learn what happens during the daily routine of a data scientist in India.
Morning: Discuss the Day with the Team and the Stakeholders, not Only the Code
Most data scientists, especially in organizations that follow a structured model, start their day joining a daily stand-up. This isn't meant just for team catch-ups—it is, above all, a tactical meeting. You include what has been accomplished, what is blocking further work and what needs to be done next. What’s really interesting about this? Many times, the main focus of discuss seems to be how your work supports business objectives instead of purely the technical details.
Employees moving from tech jobs to non-tech roles may find this job change is quite different culturally. You are addressing the real question the business has, as usually the decision makers are not concerned with things like confusion matrices.
Late Morning: Cleaning Data Is a Part of the Daily Routine
People in the industry like to joke: “Data scientists are busy cleaning data for eight out of ten days and complaining about it for the other two.” The idea remains valid even if the amount shifts a little. You may have to deal with data that is loose, out of date and less than complete. Disorder in data from ERP systems, missing information and encoding issues are simply normal in data files.
After getting a data science post graduate, you might still find yourself spending a lot of your time tidying up the data before doing anything with it. It’s not because your course is lacking—this happens with enterprise data all the time, mainly in India due to the use of legacy systems by many industries.
Late Morning: Look at Data, Make Models and Adjust Strategies
Usually it’s around this time of day that you’ll be immersed in data science activities: model building, insight generation, running various statistics or writing SQL statements. Don’t imagine you’ll be using neural networks for tasks each week. Many times, simple approaches such as linear regression and decision trees are used, since they are easy to understand and quicker for stakeholders unfamiliar with complex systems.
At this stage, making calculations clever but straightforward is very important. How a data scientist helps make decisions counts more to employers than how they impress fellow data scientists. In banking, insurance and retail, regulators often require explainability.
Afternoon: Focus on Dashboards, Documentation and Making Decisions
Providing value with insights is more important than gathering new insights. Storytelling is one of the least recognized parts of the job. Building a dashboard in Power BI, Tableau or even putting together a presentation slide deck for the senior management team could take an hour. Remember to reform your jargon so that even people with no background in data science can understand—for example, “feature importance” would be “greater influence from some features,” and “precision-recall” could simply be “how many guesses the model makes that might be wrong.”
Individuals with a post graduation in data science frequently expect that their technical knowledge will get them through any tasks. If your analysis is not clearly explained, it might just be left in a deck and not used.
Evening: Learning New Things and Keeping Skills Strong
The field keeps growing fast. After you get hired, you’ll still need to check online tutorials, read related blogs and use new tools. Not due to a lack of teaching—but since the profession keeps evolving with time.
Being relevant as an Indian professional here also involves having knowledge in data science, relevant skills for the field and using tools like Git, Docker and various cloud platforms.
Final Thoughts
If jobs in data science appeal to you after you graduate, remember that you'll be using data to address business issues more than just coding algorithms. There will be many cases where you choose between speed and accuracy, depth of technical knowledge and how useful it is for business and how difficult something is to understand and how clearly it is explained.
Having advance knowledge of this doesn’t make transitioning easier, but it helps you make more thoughtful decisions. So, for Indian professionals who want to grow their skills, this is definitely a benefit to pursue.