A day in the life of a data scientist

A data scientist is a fantastic mix of art and skill. The skill here we are referring to is digging (not literally) — it’s pure exploration. For a business organisation that knows how to analyse the data and work on the problems, an explosive growth is on the way. That’s why there is an ever-increasing demand for data scientists these days. Glassdoor named data scientist as the number one job in the U.S., with a job score of 4.8 out of 5. Also, Harvard business review has declared data scientist as one of the sexiest job of the 21st century.

To begin your career in data science, you need to be familiar with statistics, data science, Big Data, R programming, Python, and SAS. Hands-on experience with these tools/technologies will set you on the right track.

Talking about a typical day in the life of a data scientist, it won’t be like everyone else, spent doing the same job every day. If you are someone who is flexible and likes daily exploring, then this is the job for you. Every information that travels in the company is valuable and is considered as data with which a data scientist can foresee future problems and should have the ability to solve it before it even arises. For data scientists, it’s crucial to see the bigger picture from the company’s point of view and build a strategy to leverage the data available for the benefit of the organization.

The whole day of a data scientist revolves around data, which is obviously mentioned in the title. Working with what you already have and bringing out your creative side to solve an unknown issue can be highly rewarding. Data sets are often puzzling and can be mysterious enough to surprise you but finding the actual meaning and getting answers is another one of the valuable returns you can get. It’s about finding out the solution to a problem or just finding something useful which can later benefit the company.

Going with the trends:

Other than spending time with pulling, merging and analysing data and developing or testing new algorithms, a major part of the day of a data scientists includes reading industry related blogs, getting together concepts, building data visualizations, writing different conclusion to share, attending seminars/conferences and basically keep up with the trends. New information comes out every day as data science is one of the most researched fields today. And if it is useful you definitely would want to know.

Here’s what an actual day of a data scientists look like:

Almost 40% of the time is spent on research and development. It also includes checking the social media blogs/vlogs on data science. After knowing all the revised and updated trends, now is the time to focus on developing and testing new algorithms with proofs to solve actual statistical problems. Generally, if a new problem related to data has been solved, it is shared or published in webinars and conferences.

A large chunk of the day will be spent building relationships within the company in order to be aware of all the new projects to come your way. This can lead you to discovering newer problems and being ready with predictive models of the solution. This might just be the most important part of the day as it opens a whole new door to communicate with all the employees and come to the conclusion sooner. As a data scientist, proper communication with a wide range of stakeholders is a very important aspect to simplify the explanations of algorithms to a layman level.

Each project is unique so you can try the project’s initial discoveries to lead you to the next step. Projects can usually be simplified using tools from topology, real analysis and graph theory, which really helps you to speed up rather than coding from scratch. If you don’t have a big team with you, this hack can help you level up and cover more projects than usual.

At last you can always help your team to develop or improve new models by testing the previous ones. Identify the false positives/negatives and emerge new examples to fix the problem. For some cases you should know when to exactly stop as it totally depends on the project for how long you should work on it.


If the job role we discuss above excites you and makes you want to build a career in the same — bravo! The demand for data scientists is only increasing as we said earlier, and there couldn’t have been a better time than now to learn and explore. If you wish to start, we recommend you check out our data science course.

To read more about Machine Learning and Data Science, click here.