What to consider when applying for a data science internship

So you’re thinking about applying for a data science internship? There are a few things you need to consider when picking the perfect internship for you: Do you have the right mindset? Do you know in which industry you would like to work? What would you like to learn and what type of projects would you like to work on during your internship? Do you want to do a Structured Internship or an Integrative Internship? In this blog post, I discuss the different aspects to consider when searching for an internship that fits your goals and expectations best. 

Are you open to new experiences?

Your first goal should be to receive exposure to data science, the industry and the types of jobs within the field. The mindset you should have is to be open to experiencing as much as possible. 

Which industry do you want to work in?

Secondly, consider what types of organisations you are interested in. If you have a keen interest in the financial industry, research organisations in the financial industry that have a data science department and look for internships there. If you are uncertain what industry you would like to learn more about, consider interning at a data science consultancy like Praelexis. Interning at a consultancy will give you exposure to many different industries and how they utilise data science. 

What do you want to learn during your internship?

Before taking on an internship, ask yourself: What do you want to learn during your internship? Different data science departments and companies make use of different tooling, coding languages, and cloud platforms. The types of projects they take on might also differ. So before choosing an internship consider the following questions: 

• What tooling do I want to upskill in?
• What coding languages am I comfortable with or want to gain more practice in? 
• Which cloud platforms am I interested in working with? 
• What types of projects pique my fancy? 

Ideally, you would choose an internship that aligns with the above-mentioned goals. 

What different aspects of the data science life cycle are you specifically interested in?

There are many different aspects of the data science life cycle. The data science life cycle is a process data scientists follow to ensure that they can extract meaningful insights from data. The process is as follows: 

1. Problem identification: Clearly define the business problem and the objective of the solution.
2. Data Acquisition and Collection: Identify and collect the relevant data.
3. Data Preparation and Cleaning: This is the preprocess that ensures that the data is in a usable state.
4. Exploratory Data Analysis (EDA): This is the visualisation of the data to identify hypotheses for further analysis. 
5. Feature Engineering: Selecting and crafting features to better the performance of an ML model. 
6. Model Development and Training: Selecting and fine-tuning predictive of prescriptive models.
7. Model Evaluation and Validation: Assess the performance of the model.
8. Model Deployment: The model is put into production in a way that it can create value. 
9. Monitoring and Maintenance: After deployment data scientists check on the model’s performance. 
10. Feedback Loop and Iteration: The model is refined according to stakeholder feedback. 

Anamika Singh has a wonderful blog on the topic of the data science life cycle that you can read here. 

Different companies focus on some aspects of the life cycle more than others. When you apply for an internship, you have to consider what type of work the organisation expects from their data scientists. At a consultancy, for example, there would often be different individuals working on different aspects of the life cycle giving interns an overview of all the different jobs within the data science field. 

What will you be doing during your internship?

There are two types of data science internships: Structured Internships and Integrative Internships. 

With a Structured Internship, interns are expected to complete a set list of tasks and learning opportunities. For example, working on a project earmarked for interns. One of the perks of a structured internship is that the expectations are clearly mapped out.

With an Integrative Internship, interns are given the opportunity to join an existing team and work on a “real” project. This is real-life exposure that includes witnessing the full cycle of a project: From client interest to solution implementation. You will get the opportunity to witness the data scientist’s day-to-day. 

Conclusion

Choosing a data science internship might be more complicated than you originally envisioned. When choosing the best internship for your goals, consider answering the following questions first: 

1. What mindset do you have concerning the internship? 
2. Which industry do you want to work in? 
3. What do you want to learn during the internship?
4. What part of the data science life cycle do you think you’ll enjoy the most? 
5. On what type of projects would you like to work during the internship?

*Illustrations: Generated by AI

*Infographics: Aletta Simpson