Navigating the Early Challenges of a Data Science Internship: Is Patience the Key?

Many data science positions come with a learning curve that requires an intern to start with tasks such as data cleaning and database architecture. This type of work may seem less glamorous, but is it worth sticking it out for the long term? In this article, we explore the pros and cons of sticking with such a role and whether patience is the best approach.

Understanding the Reality of Internships in Data Science

Unfortunately, you may encounter roles that start with mundane tasks such as data cleaning and database architecture. Many companies do not fully understand the essence of data science. Nevertheless, this experience is invaluable and should not be dismissed. Internships provide practical experience that can benefit your future career.

Why Should You Stay?

Situations can vary, and your alternatives to the current internship are the most critical in your decision-making. If I were to simplify, the answer would be to "stay." Here is why:

It's a Crucial Skill

Data cleaning is one of the most fundamental tasks in data science. If the data is incorrect, nothing can proceed. Even senior data scientists spend a significant amount of time on this. In the future, when you have to work with noisy data and build models, understanding the process of data cleaning will be invaluable.

You're Learning Valuable Lessons

Understanding what is and what is not required of a data scientist adds significant value to your experience. Internships are about learning what not to do and why it's important. You are also observing the work of other team members, gaining insights into their roles, and contributing to the overall project. This helps you absorb important knowledge.

You're Out-Shining Your Peers

Recruiters understand the importance of these skills. When applying for a full-time position, you will be better prepared to discuss data science and will have tangible experience that sets you apart from your peers.

Evaluating the Role and Choosing Wisely

However, it's essential to consider that you are asking this question a bit too late. Before accepting any offer, make sure to ask the right questions about the company and the role. Consider the different types of roles that are often under the umbrella of 'data science.' Some might involve more focus on model creation, while others might require a lot more data cleaning.

Demystifying Data Science

Only 10% of what a “data scientist” does is model creation. The vast majority (90%) involves data collection, data storage architecture, data cleaning, and data verification. Once you have completed these tasks, you will proceed to the more exciting part of creating, training, and evaluating models. As an intern, you will likely handle only 5% of these tasks, with senior team members taking the lead for the remaining 95%.

In conclusion, while data cleaning and database architecture might not seem as glamorous, they are fundamental skills that build the foundation for future success in data science. By staying patient and persevering, you will emerge with a well-rounded skill set that sets you apart from other candidates in the job market.