9 Mistakes Data Scientists Must Avoid

In data science, like many other fields, you learn more by doing than by reading or merely studying the theoretical aspects of the field. Early in your data science career, you’ll spend a lot of time learning coding, mathematics , statistics , algorithms, visualization and business basics.

Although all these concepts and topics are extremely important, knowing a field’s theory doesn’t mean you’ll have a grasp on putting that theory into practice. Sometimes, as a beginner, you tend to make mistakes that are, in reality, easy to avoid because you lack experience or because you just weren’t taught to avoid those particular mistakes.

Once you start building more projects and working on real-world problems with different teams and data sets, you’ll develop intuition about how to approach any problem, plan specific steps to reach the solution and solve any problem that comes your way. So, although you will find your own way to avoid mistakes through trial and error, why wait?

I’ve been where you are and one of the best decisions I made was talking with many data scientists about what they wish they’d known earlier in their career that would’ve helped them progress faster and better. What I heard over and over was: You learn better by doing. But how can you make the doing even more productive?

In this article, we’ll walk through nine common mistakes often made by early career data scientists or data science students (and sometimes experts) that lead to false results or cause the project to take a much longer time to finish.

9 Common Mistakes Data Scientists Need to Avoid

1. Not Having a Plan
Let’s start things off with the most commonly made mistake: launching into a project without having a plan of attack. Often, when we are given a data science problem, we need to answer why the data behaves the way it does and what story it’s telling us. To answer those questions, we need to be clear about our methodology. In other words: What are the questions we’re trying to answer and how will we go about answering them? Jumping into a problem without some kind of strategy or roadmap is a recipe for getting lost pretty quickly.

2. Choosing the Wrong Visualizations
Choose your visualizations wisely. Visualizations are important in all stages of the project. For example, they’re critical in data exploration and help you either spot patterns or trends. On the other hand, bad visualizations can make you miss those trends completely. So, make sure you know what visualization tools are available, what graphs and charts you can use, and which one will best describe your data to help you understand it better.

3. Failing to Consider Bias in the Data
In data science, there’s a famous saying: Your results are only as good as your data. Unfortunately, we don’t often don’t have a say in how or where the data is collected. That’s why, when we set up steps to solve a problem using a data set, we need to consider whether there’s inherent bias present in the data or whether it’s a good representation of the entire population. Doing so helps us avoid ending up with skewed models.

4. Neglecting to Optimize the Model for Your Data
Your model has to be optimized for the data you have and follow the change in data over time.