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  • Writer's pictureCurtis Thompson

What is a Data Scientist?

Data science has been described as the sexiest job of the 21st century. Terms like big data and machine learning get thrown around when the discussion moves towards data science, but what exactly is data science, and what does a data scientist do?


What Is A Data Scientist?


The term “data scientist” is not actually perfectly defined, and you can get a different answer depending on who you ask. It is generally considered to be a new category of jobs for analytical data experts who are responsible for processing data and creating data models. Data scientists then use these models to interpret the data and make insights or predictions for their business.


They can be involved in many parts of data in a business such as the collection of data, processing data, and communicating results. Data scientists will need to be able to analyse and work with big data, and then use machine learning to create the models they need for the business.


Are There Any Roles Similar To A Data Scientist?


There are several roles that are like a data scientist, and the lines between them can often be blurred.


A data analyst is a role that predated data scientists and is simply a role for someone who analyses data. In theory, these roles are less focused on creating models with big data and more focused on finding insights through graphs and statistics. In reality, the line between them is very blurred. It is increasingly required for data analysts to create machine learning models, and it is a necessity for data scientists to create graphs and charts to display their results.


A data engineer is someone with a lot of programming experience and often does some of the code-heavy jobs for data scientists. In a large company, the data team will contain several data scientists and data engineers who work together. They typically build the data pipeline that prepares the data for data scientists to use. In smaller companies, these roles can also blur a lot.


There are many more roles linked with these. There are data architects who create the blueprints for data management systems. Statisticians have a strong background in theory and mathematics, which gives them a unique viewpoint to find insights into data. Database administrators oversee databases and make sure they are available to all users. There are many roles like a data scientist, and “data scientist” can sometimes be used as a catch-all term.


What Skills Do Data Scientists Need?


Since a data scientist can encounter many different problems, they often need a wide skill set to perform well.


Many data scientists have a strong background in either computer science or programming. As such, they have experience in programming languages such as Python or R. Python is the leading programming language for machine learning, so data scientists use it to create their complex models. R is a popular language for statistical work, particularly in the academic industry.


Since data is usually numerical, data scientists have a good mathematical understanding. Finding insights in data is done through statistics, and data models are built using the theory around statistics, so data scientists need to have a good background in statistics.


These technical skills are not the only skills that data scientists need. They must also be able to communicate their results to other employees in the business. Therefore, data scientists need good communication skills. They need to be able to take a technical and complicated topic and explain it in a way for anyone in the business to understand. Not only that, but they need to be able to explain it in a way where the other people can use it in their jobs to make decisions. Good communication and storytelling skills are very useful to data scientists.


Is Data Science The Sexiest Job Of The 21st Century?


Data is becoming more important for businesses. Businesses can use data to find new customers, find ways to retains customers, and get an edge over their competitors. Most large businesses will now have whole teams dedicated to data, and data scientists are at the centre of these teams.


Businesses providing content for customers, like Netflix, rely on data scientists to create the programs that recommend new content. New films and series are recommended to users with machine learning models and can be achieved by collecting lots of data on the users. This improves the product that Netflix offers and creates value for the company; data scientists are at the centre of improving this product.


Advertising giants like Facebook and Google have a huge need for data and data scientists. The more data they have on people, the better they can target ads. This is good for customers, so they get more relevant ads. This is good for clients of Google and Facebook, as it means their ads will be more effective. And for Google and Facebook, they will make more profit from more ads, and more successful ads.


Data is being used more and more by smaller companies too. Smaller companies can use data to predict how much sales they will have in the next few months, influencing how much stock they need. They can determine the best products or shop layouts to maximise sales. They can also use data to advertise their company. As data products become cheaper, data scientists will become more important to these businesses.


Data is so readily available nowadays and is becoming more accessible every day – partially due to the amount of data that our society now creates. Data scientists will not disappear any time soon.


To conclude, data scientists are very important to businesses. While their job description is not always well defined, there is a clear need for someone to collect and use data for a business. This is what data scientists do and will continue to do for a long time.

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