A Day in the Life of a Data Scientist

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If you’re considering a career in data science or you've just accepted a junior role, then you’re probably wondering what a day in the life of a Data Scientist typically involves. The answer is, it depends!

Every job is different and each day brings new challenges. You might be working for a large multinational with strict procedures and hierarchy or a startup with a flatter structure and looser rules, so a lot depends on the type of environment you find yourself working in, but there are certainly some similarities across different organisations.

After grabbing a coffee and catching up on emails, it is time to get started with the real business of the day which usually involves meetings, presentations, and a bunch of coding.



Planning always comes first. In order to solve a business problem, a Data Scientist needs good data to work with and this can only be obtained by asking the right questions. After analysing and understanding the pain points that they uncover, a Data Scientist can then frame the problem and set about solving it.


Data Collection

After a problem has been defined, a Data Scientist will then collate the data they need to fix it, but only after identifying suitable data sources. If the data doesn’t already exist, it’s collected using methods such as customer feedback, satisfaction surveys, or from websites using cookies for example. Once this raw data has been gathered, it needs to be cleaned to ensure all records are complete and there are no duplicates.


Modelling and Analysis

Now that a Data Scientist possesses the right questions and data, they need to decide the best and most efficient way to answer these questions. There are several different algorithmic and machine learning approaches available, and the best method usually involves some form of trade-off which relies on personal judgement.

Once the way forward has been decided, a Data Scientist will run in-depth analysis to extract the information that they need using data science tools. This provides the necessary insight into problems that results in better business decision-making. It usually takes a degree of experimentation with different models and approaches to achieve best results.


Presenting Results

Once the analysis stage has yielded results, a Data Scientist has the important task of communicating new found insights to the relevant stakeholders. There is a real skill in effectively presenting this information to initiate positive change within the organisation. The best way to do this is by using visualisation tools to give datasets a compelling narrative that everyone can follow and understand.


Keeping Skills Relevant

After a busy day grappling with business problems, gleaning new insights, and presenting findings, it’s time to head home, but there’s no harm in reading a couple of articles on the latest data research on the commute home. Data science is a fast-paced field, so it is vitally important to keep your skills and knowledge up to date with additional learning and networking and industry events.


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