Are you making these mistakes? As a data scientist, it can be easy to fall into some common traps. Let’s take a look at the most common bad habits amongst data scientists and some solutions on how to avoid them.
1. Not Understanding the Problem
Ironically, for many data scientists, understanding the problem at hand is the problem itself. The confusion here often occurs for a couple of reasons. Either there is a disconnect between the data scientist’s perspective and the business context of the situation or the instructions given are very vague and ambiguous. These reasons all lead back to a lack of information and understanding of the situation.
Misunderstandings of the business case can lead to wasted time spent working towards the wrong approach and often causes many unnecessary headaches. Don’t be afraid to ask clarifying questions, having a clear picture of the business problem being asked is vital to your efficiency and effectiveness as a data scientist.
2. Not Getting to Know Your Data
We’re all guilty of wanting to jump right in and get the ball rolling, especially when it comes to a shiny new project. This ties into the last behavioral point, rushing to model your data without fully understanding its contents can create numerous problems in itself. A thorough and precise exploration of the data prior to analysis can help determine the best approach to solving the overarching problem. As tempting as it may be, it’s important to walk before you can run.
After all, whatever happened to taking things slow? Allocate time for yourself early on to conduct an initial deep dive. Don’t skip over the getting to know you phase and jump right into bed with the first model you see fit. It might seem counterintuitive but taking time to get to know your data at the beginning can help save time and increase your efficiency later down the line.
3. Overcomplicating Your Model
Undoubtedly, you will face numerous challenges as a data scientist, but you will quickly learn that a fancy and complicated model is not a one size fits all solution. It’s common for a complex model to be a data scientists’ first choice when diving into a new project. The bad habit, in this case, is starting with the most complex model when a more simple solution is available.
Try starting with the most basic approach to a problem and expand your model from there. Don’t overcomplicate things, you could be causing yourself an additional headache with the time drained into the more intricate solution.
4. Going Straight for the Black Box Model
What’s worse than diving in headfirst with an overly complex model? Diving in headfirst with a complex model you don’t entirely understand.
Typically, a black box is what a data scientist uses to deliver outputs or deliverables without any knowledge of how the algorithm or model actually works. This happens more often than one might think. Though this may be able to produce effective deliverables, it can also lead to increased risk and additional problems. Therefore, you should always be able to answer the question of “what’s in the box?”
5. Always Going Where No One Has Gone Before
Unlike the famous Star Trek line, you don’t always have to boldly go where no man has gone before in the realm of data science. While being explorative and naturally curious when it comes to the data is key to your success, you will save a lot of time and energy in some cases by working off of what’s already been done.
Not every model or hypothesis has to be a groundbreaking, one of a kind idea. Work from methods and models that other leaders have seen success with. Chances are that the business questions you’re asking your data or the model you’re attempting to build have been done before.
Try reading case studies or blog posts speaking on the implementation of specific data science projects. Becoming familiar with established methods can also give you inspiration for an entirely new approach or lead you to ideas surrounding process improvement.
6. Doing It All Yourself
It’s easy to get caught up in your own world of projects and responsibilities. It’s important, though, to make the most of the resources available to you. This includes your team and others at your organization. Even your professional network is at your disposal when it comes to collecting feedback and gaining different perspectives.
If you find yourself stuck on a particular problem, don’t hesitate to involve key stakeholders or those around you. You could be missing out on additional information that will help you to better address the business question at hand. You’re part of a team for a reason, don’t always try to go it alone!
7. Not Explaining Your Methods
The back end of data science projects might be completely foreign to the executive you’re working within marketing or sales. However, this doesn’t mean you should just brush over your assumptions and process to these non-technical stakeholders. You need to be able to explain how you got from point A to point B, how you built your model, and how you ultimately produced your final insights in a way that anyone can understand.
Communication is essential to ensure the business value is understood and properly addressed from a technical standpoint. Though it might be difficult to break things down in a way that non-technical stakeholders can understand, it’s important to the overall success of any project you will work on. This is where storytelling tactics and visualizations can come in handy and easily allow you to communicate your methods.