A day in the life of a Data Scientist

The position of data scientist is one of the most lucrative careers that you could pursue today – it’s highly in-demand in most major industries, and this trend is poised to continue well into the next decade. But what exactly is a data scientist?

Well, the position requires a person who can take a large volume of raw data and extract key elements, discovering patterns and trends that can be used to make accurate predictions for the future. Because the huge increase in the demand for data scientists is very new, as it only happened in the last few years, there’s still a lot of confusion regarding the exact nature of the profession.

So, the question is – what exactly does a data scientist do on a day to day basis? Well, in this article, we’ll explore some of the most critical areas of work that a data scientist focuses on during his day.

Problem Solving
With technological advances allowing to automate numerous tasks that previously required manual labor, the most coveted employees of today must have skills that cannot be replicated using software or artificial intelligence. And one of those skills, that’s absolutely critical to data scientists, is problem-solving.

A data scientist that wants to grow and achieve success in his profession must have very strong critical thinking skills and must be adept at solving complex problems creatively.

This requires to maintain a finger on the pulse of the industry and remain ahead of all the newest developments and trends in order to apply the best practices and breakthroughs to their own work. Some of the problems that a data scientist is likely to face won’t have easy solutions – since the profession itself is relatively new, there aren’t yet clear guidelines and best practices that can be used to solve a particular problem.

Instead, the data scientist needs to be able to clearly identify and define the problem, figure out its most challenging aspects, and establish a goal that needs to be achieved to solve the problem. To solve problems effectively, a competent professional in the field must also be able to communicate and collaborate with others, as different specialties and strengths of co-workers can play a vital role in coming up with new angles on how to approach a problem.

Programming
Almost any data science position requires sufficient coding skills, including solid, workable knowledge of at least one of the major programming languages, most commonly Python and/or C++. Programming is an integral part of day-to-day activities because a data scientist will need to perform data cleaning and develop custom algorithms for specific data processing and analysis tasks.

Basically, you will need to customise almost all the tools that you work with, so having the ability to understand the best ways to program is required to make that possible.

What’s more, as a data scientist, you will need to tweak the data analysis models constantly, so having a functional knowledge that allows you to track the changes and see what’s working is essential. Finally, the reason why programming is important lies in the way that the data is presented – rarely, if ever, will a data scientist receive neat data that is ready for processing. Instead, the data will often need to be retrieved, formatted, and prepared for analysis.

Otherwise, trying to sort through the data and gain any meaningful insights would simply become too complicated and time-consuming.

Mathematics and Statistics
As you might have already guessed, math plays a vital role in the field of data science – having academic-level knowledge of the subject is essential if you are going to work with complicated algorithms that allow you to draw actionable insights from raw data.
Often, a data scientist is required to apply foundational mathematics because merely using an API may not work in all situations when analysing data.

But even though math is essential, statistics are even more important – you need to not only have in-depth knowledge of all the fundamentals and core concepts, but also be able to apply them to machine learning applications.

Communication
Finally, as a data scientist, you need to be able to communicate with your colleagues to solve the problems that you are facing and do your job effectively.

In today’s tech world, nothing happens in isolation – different fields are intertwined in countless ways, so it’s almost impossible to expect to be an expert in all of the areas that come up during data science projects.
That’s why you need to be able to share ideas and collaborate with your colleagues and your superiors to maximise the chances of achieving the desired result.

You must also be able to take complex ideas and insights from data analysis and translate them to a language that can be understood by people outside of the scientific community.

Being able to tell a story that resonates with decision-makers and makes the message easy to understand is crucial.

The inability to present the insights and predictions gained by data science will defeat the entire purpose of the process – for it to be meaningful, the business has to be willing to act on those insights and trust their validity.

At the same time, the data scientist must be able to interview the stakeholders of the business to gain a deep understanding of the problem so that he gets a better idea of where to look for its solutions.

If you are interested in a job or career in Data Science or AI, please contact one of our team.

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