How Data Scientists Can Make Their Workload More Manageable

Data scientists are part-mathematician and part-computer scientists, with a mind for IT and the perception of a trend spotter. That doesn’t mean they can do it all though. Data scientists have become some of the most sought after professionals around, leading to a mass increase in their average workloads whether freelance or working in-house.

As the skill set emerges as one of the most sought after in the world and pressures to get the job done as quickly as possible become greater, it’s vital that data scientists find a way to make their increasing workloads more manageable.

Set micro-goals

Data scientists are being expected to change the way the world works, from helping retailers find the perfect customer bases to taking us a step closer to all having driverless cars. With such a heavy workload it’s important for them to condense their work into smaller, more manageable chunks in the form of micro-goals.

Setting micro-goals is the first step to making your workload more manageable. If you list your tasks simply as projects they will loom over you for the days, weeks or months they take to complete. Micro-goals add little victories along the way. Break down your tasks into basic steps and give reasoning behind why that needs to be done. This will be useful when you come to explain your process and break down your findings to stakeholders.

Become the kind of person who enjoys ticking tasks off a list, and you’ll find all of your work more manageable.

Manage your time

Data scientists have a knack of underestimating how long a task is going to take. To make sure you’re not frustrating your colleagues, your business partners and yourself it’s important to learn some time management skills.

Like with micro-goals, it helps to break things down to a granular level. You should look to break things down into manageable time slots. Estimating how long a task is going to take will help you create a clear and precise schedule, which helps when you’re relaying to a superior how long you expect the fast to take.

When you’re working with a huge collection of data, it can take a lot longer than expected. It’s easy to get lead down tangents or be overwhelmed by the sheer volume you’re working through. If you’re constantly overestimating or underestimating how long it will take to complete a task people will be less inclined to work with you in the future.

If you’re curious about your time management skills at the moment and where you can make improvements, install an app such as RescueTime and check your current workflow.

Have the right tools

You wouldn’t expect a tradesperson to be able to do their job properly without the right tools, so why how can a data scientist perform to a high-level without suitable equipment?

As a data scientist, you should have access to all the tools you need to analyze data and build projection models. Without these programs, you can’t do the basics of your job. However, if you find your workload becoming uncontrollable switch to a different data management tool.

Your tools shouldn’t be limited to just the software you use, hardware is just as essential to keeping your workload manageable. Say you need to present your findings to a client or you want to work on them from a larger screen, yet you only have your mobile to hand. Using a micro USB to HDMI cable can help you get a clearer picture of the data you’re working with even though you aren’t at your usual set up.


Replacing the work of a data scientist isn’t straight forward, but automation can go a long way to reducing your workload and allowing you to focus on the more demanding tasks.

Data science projects are complex and we shouldn’t expect, or aim for, automation to be able to completely take control of repetitive processes.

However, it can assist with speeding up remedial tasks and democratize the process so less experienced data scientists can begin to see results in their work at the same speed as industry leaders. AI machine learning can be used to analyze vast quantities of data, whereas AI-driven automation tools such as DataRobot can help in developing accurate prediction models.

These tools benefit data scientists by allowing them to test for scenarios they would otherwise not have thought of in the background.

Become proficient with Python

We’ve already discussed why the right tools are so important to helping data scientists manage their workload, and few are as vital to master if you want to reduce your workload than Python. Not technically a tool, but a coding language, it’s essential that a data scientist is proficient with the industry-standard Python.

Sometimes referred to as the “Swiss Army knife of the coding world”, the flexibility and popularity of Python mean you’re lagging behind the rest of the industry if you don’t know how to use it.

Python has a huge following amongst data scientists in part for its massive database of libraries and machine learning. You don’t need to know it inside out, but its simplicity means you won’t be slowed down while script-writing. It’ll speed up your work process and motivate you to get through your workload.

Reaching for answers in the Python community is a great way to get some extra help and advice when you’re overwhelmed with your workload.

Teamwork (at every level)

Teamwork is the key to any enterprise succeeding. Even though it can be a solitary profession, data scientists benefit from having a team they can have frequent discussions with around them, even if they don’t fully understand what they’re doing.

Have an informed team around you who can assist on more menial tasks and produce clear and concise briefs. At both ends of your role, you should have a team to assist you in how they explain the queries they want to be solved and then explain what they want to see from the data. As a data scientist, you’re going to be needed throughout the process, even after your initial work is done, so it’s important to build good teamwork relationships.

Of course, teamwork comes from any level, from direct contacts to the stakeholders. Teamwork needs to come from your superiors in the form of them having realistic expectations of what you can do. Without that pressure from above, you can manage your workload in your own way. If the time comes to do so, be comfortable saying no to additional tasks that will slow you down.