6 things I learned working as a data science beginner

Photo credit: Samuel Zeller

Having been in data science for almost half a year, I’ve made a lot of mistakes and have learned the hard way. I realized that there are a few common pitfalls that beginners like me tend to encounter.

I hope these lessons can guide you through your own journey.

There is no such thing as failure, only feedback.

1. Business domain knowledge

To be honest, this lesson hit me right in the face. When I first started, I did not put much emphasis on the importance of domain knowledge. Instead, I spent too much time improving my technical knowledge (i.e. building a sophisticated model without really understanding the business needs).

Without understanding the business thoroughly, chances are your model won’t add any value to the company. It simply would not serve the purpose, regardless of how accurate your model is. Only by understanding the business needs and adding relevant features can you significantly boost your model’s performance.

So, as a data science employee, be really interested in your company’s business. Your job is to help them solve their problems through data. Ask yourself if you’re really passionate about what they’re doing and show empathy.

Another thing is to always know what you’re talking about.

Make sure you can articulate your ideas and present them to the stakeholders in a way they can understand. Never use strange or self-defined words.

Despite achieving correct findings or impactful insights, your credibility may be questioned. So, before showing how data can be used to solve your company’s problems, I suggest showing first that you understand the business as a whole (yes, including technical terms commonly used in your day-to-day work). Subsequently, identify a problem statement that the available data can answer.

2. A detail-oriented mindset and workflow

Be like a detective. Carry out your investigation with laser focus on details. This is particularly important during data cleaning and transformation. Data in real life is messy, so you must be able to pick up signals from the ocean of noise before you get overwhelmed.

Having a detail-oriented mindset and workflow is of paramount importance to be successful in data science. Otherwise, you might get lost.

You may be diligently performing exploratory data analysis for some time but still not reaching any insights. You may be consistently training your model with different parameters to hopefully see some improvement. You may have completed a tough data cleaning process but then realize that the data is not clean enough to feed to your model.

3. Design and logic of experiment

4. Communication skills

5. Storytelling

6. Risk management