Written by Emilie Brunet
This week we’re going to dive into what all of these terms mean to help you figure out the path that’s best for you.
Data Science is an umbrella term often used to talk about various jobs in Data, from Data Engineer Analyst, Architect, Scientist, etc.
Every role in Data Science works with Data. But it’s the additional skills, responsibilities and how that data is being used that demonstrates the differences between these different roles.
The primary skills involved in the field of Data Science can include:
But while these are general skills required, depending on the career path you take, some of these skills will be more relevant than others.
The Data Scientist is someone who has a handle on all of the skills outlined above. They usually have a broad role that allows them to handle data from start to finish.
Much like all scientists, Data Scientists make a hypothesis and aim to prove that hypothesis through collection and analysis of data and through predictive models.
Let’s say you own a business that hosts and streams videos of cats and dogs doing cute things. Your business is called Petflix (get it? 😉)
Petflix wants to find ways to keep their users on their platform for longer.
You hire a data scientist. Looking at your business needs, the data scientist predicts that they can keep users on the Petflix platform for longer, by recommending other videos for their users to watch.
First, the data scientist would pull data from users (age, gender, previously watched videos, etc).
They would then clean their data by removing irrelevancies, irregularities and inaccuracies.
Using that data they could create an algorithm that would generate videos that they think you might like. This would be based on what other people of your age, gender, time on the app had previously watched, and based on what you’ve previously watched.
The Data Scientist’s model keeps the users engaged on the Petflix platform for longer.
Data Scientists build models that predict their users movements and patterns.
Using the same example as above a Data Analyst wouldn’t create the model or algorithm to generate recommended Petflix videos.
They might, however collect, organize and analyze data to then present their findings on the various demographics both before and after the Data Scientists have created their predictive models. Their role is more around making business decisions based on the data.
The Data Analyst could possibly present the number of users by gender, their most watched video genre, their location, their time on the app, etc. They would then provide valuable insights on the data they collected.
While both roles are working with data, the Data Scientist is problem-solving and developing tech solutions with their data, the Data Analyst is visually representing the data they’ve found and providing business insights.
Data Analysts use the models created by the Data Scientists to make business decisions. They provide actionable insights.
Note: This isn’t always the case and may very well depend on the company, the experience of the employee and the business needs.
Data Scientists work with data from start to finish. They provide solutions to business challenges by developing a hypothesis and creating a predictive model to prove said hypothesis. They collect, organize and clean data.
Data Analysts are more reflective and visual. They don’t create predictive models or provide solutions but are rather more involved in identifying patterns and insights. They observe whether or not the models that the Data Scientists have created are providing the necessary insights. They also will collect and organize data (sometimes they work with already cleaned data, but not always). They then provide data visualizations.
The primary difference between data scientists and analysts are their technical know-how. Data scientists usually have more technical knowledge. Whereas Data analysts work more on the business than on the technical side.
Data Engineers are usually in charge of the architecture or the systems used to collect data. They ensure that the data is readily accessible for the Data Scientists and Analysts to use. They might design and implement specific data collection structures or systems. Their role is more tech-based than business based.
Continuing with our Petflix example, the Data Engineer would be the one creating the systems to collect the data on its users. They ensure the accuracy of the data being collected.
If you’re looking to break into the realm of Data Science, getting a certification from a Data Science Bootcamp is a great way to start.
Most Data Science Bootcamps offer an overview of everything in the field of Data Science to kickstart your career, including Data Analytics, Math, Statistics and Machine Learning.
If however, you’re less interested in the tech side of Data Science and more interested in learning about business analysis, data insights, etc, then studying Data Analytics might be a better place to start.
Most people starting their careers in Data Science get entry-level roles such as Data Analysts or Junior Data Scientists. From there, you can increase both your soft skills (communication, business acumen, project management) and your hard skills (software programming, coding, etc) to figure out what path you want to take.
If you find yourself being drawn to more of the business side of Data, you might consider moving from Data Analyst to Business Analyst.
If you find yourself being drawn to more of the tech side of data, you might consider pursuing a role in Data Engineering.
If you enjoy problem-solving and predictions, you might pursue a Senior Data Scientist position.
Join our upcoming info session on Tuesday, Sep 19 at 11am ET to learn about our Data Science Bootcamp and kickstarting your career in tech.
Click here to register for the upcoming info session!
I'm the People & Culture Manager at Journey Education. I have always had a passion for writing, organization and finding creative solutions. I aim to be personable, empathetic and compassionate and believe that kindness can go along way in both business and life.
Having worked and organized with anti-capitalist, feminist and queer organizations, I strongly believe that EVERYONE deserves, not just a living wage, but a thriving wage and that it should be the priority of every business to create an inclusive, caring and diverse work environment that doesn't just ensures the work happens, but allows people to be people while the work is happening!
My approach to everything I do reflects my training in trauma-informed practices, active listening and harm reduction as well as my interest in understand the way people work, behave and exist as their full human self. I want to create safer spaces for people to explore, create and excel in a supportive environment - whether that's in life or at work.
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