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I believe that these lessons are so important because they are instrumental to having a successful data science career. After reading this, you’ll realize that there’s much more to being a good data scientist than building complex models.

With that said, here are my 3 most important lessons I’ve learned in my data science career!

1. A large portion of time is actually spent in between your projects (before and after).

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One thing that I noticed is that almost all data science courses and boot camps emphasize and elaborate on the modeling phase of the lifecycle of a project, while in reality, that only makes up a small component of the entire process.

If it takes you a month to build a preliminary machine learning model at work, you can expect to spend a month understanding the business problem beforehand and documenting and socializing the project afterward.

It’s not just recommended that you complete these steps prior and subsequent to building your model, but it’s pivotal for the success of your project.

Let’s dive into the importance of each:

  • Business Understanding: understanding the business problem at hand is critical for your success. For example, if you’re building a machine learning model, you should know what the model is supposed to predict, who’s going to use it, how it’s going to be used practically, what metrics you’ll use to assess the model, and so on. It’s essential that you take the time to understand everything about the business objective to create an applicable model.
  • Documentation: While I agree that documentation is less exciting than munging through data and building models, it’s important that you have clear and concise documentation for your code, for any tables that you build, and for how the model was built. This is really important so that you OR someone else can easily refer to these resources when using your models or when fixing them.
  • Socialization: Socialization rarely gets talked about, but your projects won’t be successful if they’re not used by the business. Socializing your projects entails presenting them with relevant stakeholders, explaining their value, and how to use them. The more stakeholders you can sell your ideas to, the more likely they will adopt your data products and the more successful your projects will be.

What do all three of these…

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