Shalini Kunapuli: Making Your Work matter As An Intern In Data Science
In a sea of numbers, how to stay afloat as an intern in data science
A few months ago, I was interviewed for the Tech Girl Thursdays series run by UC Berkeley Data Science student, Shalini Kunapuli. We spoke about our experience, love, and pain of navigating through the sometimes-rough waters of data analysis. Today, I’m so excited to switch the role and interview Shalini for my journal to get her thoughts on common data challenges, skills, and Data Science internship projects at UpLift, LinkedIn, and Quizlet.
1. What do you like the most about data analysis or data science?
The potential of discovering insights from the sea of random text and numbers is what excites me about data science. I love the problem-solving skills involved with trying to find these insights and interpreting the data. Data science is definitely a technical field with the coding, statistical methods, and modeling involved but it is also very much a creative field. Looking at data and answering questions based on analysis requires both technical skills and creative skills. I also love the versatility of data science. Data is everywhere in the world and data science can be applied to many domains, like linguistics, computational biology, social sciences, and much more. Over the past few years, I have had the opportunity to be a data science intern for three companies (UpLift Inc, LinkedIn, Quizlet) all of which are in different domains (travel fintech, social network, edtech). Even with these three experiences, I got to see the breadth of data science and how it can be used similarly and differently based on the company and domain. There is so much potential for data and the field of data science in general, and I can't wait to tap into it more in the future.
2. What was your first analytical project?
In my first few years of college, there were various computer science and statistics classes that I took with introductory projects integrated, but my first real analytical experience and "real world" project exposure was during my first technical internship.
For my first internship, I got the opportunity to intern at UpLift Inc as a Data Science intern. Being an intern at a startup was really interesting, I got to wear many different hats and work on different stages of the data science lifecycle (from building out data pipelines and making ingesting datasets efficient all the way to querying the data and building dashboards for the executive team and partners). As part of these projects, I got to work with lots of tools and technologies, including Python, SQL, AWS, Snowflake, Tableau, Looker, and more. Overall, getting to work on these analytical projects was a really rewarding experience, and I learned a lot from it including being able to connect my classwork to real-world problems. I love how internships can give you exposure to a certain company and role, and you can learn so much from mentors and the project work.
3. What was/is the most difficult or challenging thing for you to learn?
Something that can be challenging at first (as a student new to the industry) but also extremely interesting is defining KPIs and metrics for a project. In industry, there is a lot of data and the problem that you need to solve can be quite vague. A lot of metrics may not be defined and it's up to the data scientist to look at the data and think through the product/business in order to create and analyze them. It can be challenging because the problem is vague and there's a lot of data to look through to see how relevant it might be to what you're trying to define. But that's also the fun part of it. I love that creative side and finding ways to define things based on what you're trying to measure. What I've learned is that there isn't one definite answer or way to define a specific metric in some cases. Another challenging part about data science (and arguably the most important part) is thinking about the ethics behind the data. Data scientists can create models, get predictions, and analyze patterns and trends but those are only going to be as good as the data that is being used for those. Figuring out a way to get unbiased data and build models/algorithms that are fair across all domains, is a challenge in the field in general but something that is extremely important to think about.
4. I love your YouTube channel! Can you share why you decided to start it?
Thank you! So far it's been a way for me to help others learn more about technology, data science, and computer science and hear about my journey as a woman in tech as I go through undergrad, grad school, and industry. I first got into coding and technology after doing a program called Girls Who Code back in high school. Being a part of the Summer Immersion Program was a life-changing experience for me with learning technical concepts, working on projects, shadowing female engineers and mentors, and more. Before doing the program, I had no idea what working in technology or studying computer science/data science meant. Over the past few years in college I've delved deeper into the tech field, there are a lot of things that I've learned so I wanted to start a YouTube channel to give back and share what I've learned so far to help others navigate the space.
Currently, I make different types of videos on my channel related to technology, data science, and professional development advice. I also recently started a new series called Tech Girl Thursdays where I highlight a different woman in tech on Thursdays (shoutout to Olga for being a part of it)! Through the series, I’ve been able to talk with many women about their journey and experiences in technology and each experience is so unique from what they do in tech, to how they started, where they are in their school or career, and what their goals are.
Technology is a versatile field and affects us in many ways in our daily lives and we don’t often get to see behind the scenes of people working in this field, especially the stories of women. I have learned so much through all of their experiences! Feel free to check out the series and other videos on my channel and connect with me :)
https://www.youtube.com/c/shalinik
5. Anything else you can advise or recommend to other students learning data science?
Data science is a very practical field so to really understand it, and I would recommend working on projects. There are a lot of great resources online, like tutorials, articles, and publicly available datasets. Pick a dataset or project that sounds interesting to you and start doing some analysis of the data! If you have the opportunity, I would also do a data science internship. Internships are great, you get to work on impactful projects in a company environment surrounded by mentors. I also made a video with advice for starting out in data science and another video about cool data science projects you can do, check it out on my channel for more tips, and feel free to reach out to me if you have any questions or want to chat!
Tips for Starting out in Data Science:
Cool Data Science Projects Ideas:
My YouTube Channel: https://www.youtube.com/c/shalinik
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