Recommendations for Undergraduate Students Interested in Statistics and Data Science

Internships, conferences, and other opportunities for undergraduate students

for students

April 22, 2024


I serve as the Vice Chair of undergraduate studies in my department and I oversee our Bachelor of Science in Data Science program. Due to my role, I often get asked to give talks targeted at undergraduate students studying statistics and data science. During these talks, and through questions I get asked, I share a few recommendations and opportunities with students on a regular basis. I wanted to summarize a few of these in this blog post. This is mainly targeted towards US-based students but some tips may apply to other students around the globe.

Making the Most of Undergraduate Years

College life is a busy time. It is busy socially as it is a great time to build life-long friendships. For some, it is a busy time to make money to live. It is also a busy time while juggling taking multiple courses and many other activities. The most important skill one can build early on is time-management. Without intentional time-management methods demands will be overwhelming.

Three types of apps may especially be helpful in time-management:

  1. Digital calendar
  2. Task manager
  3. Social media blocker

For calendar, it might be practical whatever your university system is using Google, Outlook, etc., When using calendar, be mindful of how much time is allocated to classes, work, studying, social life. You can do this by using different colors for different types of events. Task managers will help you not to forget assignments and help you manage repeating tasks easily (e.g. do laundry every Saturday). Lastly, many people have social media addiction. If you have hard time controlling the time you spend on social media then there are blocker apps that can help block social media logins for certain hours of the day.

There are many opportunities on campus that might also be helpful as you build your career. This can include guest speakers. These speakers often are interested in student-engagement and that is why they are coming to your class/seminar etc. If you can overcome shyness, you can get to talk them as well.

Utilize career office and writing offices as much as possible. These offices provide great services and training programs that will help with career readiness. Writing is one of the most important skills you can develop during your studies.

Perhaps the most important service on most campuses is Counselling!! There is nothing more important than one’s health. Mental health is as important as physical health. Seek all the help you need. Without any hesitation.

Graduate school and the Job Market

It is never too early to start thinking about what comes after college. One of the decisions you have to make is what comes immediately after college. Graduate school or job market? Try to gather as much information as possible from everyone about the different paths. Different people with a variety of career paths can help you in the decision making process. Be mindful of who you are talking to and their potential biases. For instance if you get a professor’s opinion, they might be more inclined to support the graduate school path as they are in academia themselves. After all, the most important thing is knowing your own self: knowing your dreams, desires, and life goals.

The Electronic Undergraduate Statistics Research Conference (eUSR) takes place annually online and for free. This conference has two panels: one on graduate school and another one on statistics careers. The second panel often has speakers who work in industry and government. These panels and the recordings of panels from previous may be helpful in demystifying graduate school and the job market. If you attend the live event, you would be able to ask your questions.

For graduate-school curious students Dr. Ray Bai’s blog post on Demystifying Graduate Admissions for Statistics PhD Programs might be helpful. In addition, A First-Gen’s Guide to Grad School: How to Get in, Survive, and Thrive is a great resource. Remember that when it is time to apply for graduate school, you’ll need to ask for recommendation letters. Make sure to read Tips on Recommendation Letters for Students and Instructors on this blog. If you are a US-person then you may consider Applying to the NSF Graduate Research Fellowship (GRFP).

Summer programs or internships can also help you decide the type of path you’d like to take. You can consider one of the Research Experiences for Undergraduates (REU) programs or one of the Summer Institutes in Biostatistics and Data Science.

For jobs, if you consider government jobs make sure to take a look at USA jobs as well as American Statistical Associations Internships and Fellowships website. You might also like the Build a Career in Data Science podcast.

While searching for jobs, make sure to think about your application area interest. You can widen your search with many keywords including but not limited to “Statistics”, “Data Science”, “Biostatistics”, “Bioinformatics”, “Psychometrics”, “Econometrics”.

Networking Connecting with people

Human connections are very important. That is not because someone will help you find a job. As humans, we learn from each other, our strengths, weakness, struggles, how we overcome struggles. When you make connections you learn some, teach some.

One important place for making connections is on campus student groups. For instance on our campus Data @ UCI is the main group for students interested in data science. This group did not exist three years ago. This is all to say, even if you don’t have a group yet, you may consider taking the lead to start one to build your own data community. You may even consider building a student chapter of the American Statistical Association on your campus. is an online website that has groups of people with similar interests. You can consider joining a local meetup group and attending their events. R Ladies, PyLadies, R User Groups are just some of the meetup group examples.

You may also consider becoming a member of a professional organization to connect with other members. Some organizations that have data science communities includes American Statistical Association (ASA), Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers(IEEE).

Conferences and seminars are also great places to connect with others. Keep an eye out for ones especially in your area.


Regardless of the path you are taking, building a data science portfolio is an important part of learning. Your portfolio can include class projects as well as projects that you do outside of class for fun. These projects do not necessarily need to have groundbreaking scientific knowledge. They can be learning opportunities for you while also showcasing your knowledge to others.

One way of making projects public might be using GitHub and/or hosting/linking them on your website. I cannot emphasize importance of GitHub (and alike) on showcasing your work and data skills. Having a personal website can really show who you are, your interests, and your projects.

If you are looking for projects outside of class, TidyTuesday provides a dataset every Tuesday. On social media, especially on X, you might find many others sharing their work using #TidyTuesday. The community members publicly sharing their work also serve as an inspiration for the rest of the community members.

If working on a class project, then you may consider submitting your project to the Undergraduate Class Project Competition (USCLAP. The submissions happen semiannually in December and June. The Electronic Undergraduate Statistics Research Conference (eUSR) is also a great place to submit your projects to. If accepted, you can present your work through a video presentation.

Closing Thoughts

You might hear things like

  • You are not a data scientist because you are not strong in math/stats/computer science.

  • What you do is not data science because it does not involve advanced computing.

  • What you do is not data science because it does not involve advanced probability.

  • You cannot be a data scientist because you took courses on X and Y but not Z.

You might hear others say these or you might be saying these to yourself. Even though it is good to know math, stats, and computing, there is no data scientist that knows it all. Focus on your strengths and work to improve your weaknesses.

Last but not least, as you navigate to make a permanent place in the data science world, always remember to consider the impact of your work on humans, other livings, and the planet. Ethics is perhaps one of the most important part of data science you’ll need to learn.

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