Seven career options you can consider with a data science degree
Data science and analytics is a fast-growing field, and one that looks set to become even more important in the future.
People who understand how to compile, process, analyse, and apply vast amounts of data will be needed at every level, whether it’s business, public health and safety, governance, or law.
The demand is also outgrowing the supply, and this is especially true when it comes to emerging markets and countries. This is a prime opportunity for the people with the skills necessary to work in this field and make a difference.
Here are seven career options open to people with a data science degree.
1) Data analyst
One of the most common and easy to access positions for new graduates is data analyst. Data analysts will often be called by various organisations, more commonly corporations, to mine and analyse data and give recommendations on how they could be implemented to improve processes.
One common example is when data analysts are asked to track sales data following an advertising campaign to assess its effectiveness. They could also help tweak some aspects of the campaign and identify certain blind spots, like missed demographics for instance.
Data analyst may also be asked to work with various departments within an organisation. They might be asked to assess the performance of their sales team one day, help with growth forecasting the next, and work on gathering data from the shop floor to streamline operations and identify bottlenecks – a great position to get a solid foundation, and a well-rounded set of expertise.
2) Data scientist
While data scientists often perform the same functions as a data analyst, one of the main differences is that they’ll often be asked to perform predictive based on past data models using tools like AI and machine learning. These will usually require higher qualifications.
For a person to work as a data scientist, they’ll usually need to have a masters in data science. However, colleges like UNSW allow students to pursue their masters in data science from the comfort of their home while keeping their positions. UNSW online courses are just as recognised as any other program, and will give students the foundation they need to access data scientist positions.
Data scientists will usually also have much more leeway in how they use data and can experiment more in order to identify interesting trends and patterns that may have gotten past management. For instance, a data scientist could be called to find out exactly how much a marketing plan could affect the bottom-line using machine learning models to perform advanced forecasting.
3) Data engineer
Data engineers will be called to build and maintain an organisation’s data infrastructure. Their work will involve much less statistical work, and they’ll also need some specialised skills in areas like programming and software development. They will need to be able to navigate programming languages like Python and have a deep understanding of SQL and systems such as Postgres for instance.
Data engineers could be called to build data pipelines that will allow them to gather and relay information on things like revenue, marketing, latest sales, etc. to data scientists and analysts fast and in a format that will be easily usable. They will also have a much more hands-on approach and will be responsible for maintaining the actual hardware used to stockpile the data.
4) Machine learning engineer
Machine learning engineer and data scientist jobs often have overlapping skills, and in some areas, the jobs are interchangeable. In some cases, this simply means a data scientist with a specialisation in machine learning.
But in other cases, a machine learning engineer will have more of a software engineering role and may be asked to take information from a data scientist and turn it into a deployable piece of software. In all cases however, these positions will require that you have advanced knowledge of machine learning, and programming skills.
Another common name for this position is “machine learning specialist”. This will usually be the case when a company is searching for a data scientist with a background in machine learning rather than someone to engineer software for them.
5) Data warehouse architect
This is actually a sub-field of data science and is for people who would be more interested in the hardware side of things. The data warehouse architect will be responsible for looking after an organisation’s data storage systems. In this case, only people with a thorough knowledge of SQL will be considered.
You will also usually need to have additional skills depending on the position. While a data analyst won’t usually be considered for this role, those with a masters will usually have a better chance as they’ll have advanced data management and SQL skills.
6) Business intelligence analyst
A business intelligence analyst is basically a data analyst, but with specialised knowledge in business and market trends. A business intelligence analyst, for instance, will be able to forecast the commodities market and influence buying decisions.
In most cases, these positions will require that you have experience with tools such as Microsoft Power BI or other software-based analysis tools. Other skills needed for the position include an understanding of machine learning, and an understanding of R programming and Python among other things.
7) Systems analyst
Systems analysts will be called to come in and identify some issues with a certain organisation’s system. They will also be called to fix problems at the organisation level, and see how changes to certain systems could end up affecting the organisation as a whole.
These people will also need to have some advanced programming skills, be good problem solvers, and have great statistical and data analysis skills in order to quickly identify problematic trends and what’s actually working. They’ll also be able to identify issues that are outside of an organisation’s tech system scope.
Photo by Annie Spratt