Amal Bhatnagar, a double major in data science and economics, reflects on his experiences working for the Data Science Education Program's External Pedagogy team, including presenting at the SciPy conference (above).
Working at Berkeley’s Division of Data Science and Information over the past year has allowed me to take on a variety of data science projects, both technical and non-technical. My primary project has been building a national community for data science instructors. Creating such a platform entails sharing Berkeley data science resources, helping to host the annual National Workshop on Data Science Education, and finding other ways to encourage professors to adopt data science practices. My job constantly excites me, because I get to work with smart people driven towards the same mission, connect with faculty around the world, and help shape the next generation of data science education. Last summer, I traveled to an international data science conference, Scientific Computing with Python (SciPy), in Austin, Texas, and presented on how other institutions can adopt data science practices in their curricula.
Presenting to the 800 attendees from 34 countries and talking with researchers, PhD students, and data scientists allowed me to realize the bigger picture of the exciting and growing field of data science.
For SciPy, I co-published a paper and presented a poster called “Accelerating the Advancement of Data Science Education,” which analyzed factors that make Berkeley’s undergraduate data science program successful and explored how other undergraduate institutions can implement data science programs themselves. Presenting to the 800 attendees from 34 countries and talking with researchers, PhD students, and data scientists allowed me to realize the bigger picture of the exciting and growing field of data science. Before then, much of my data science knowledge was based on conversations with fellow data science students and a few recently-graduated data scientists and reading online resources.
Many of the recruiters I met at SciPy were actively seeking data scientists and were impressed that undergraduates are learning complex skills that traditionally have been taught at only the graduate level. SciPy also had many workshops, ranging from data visualization and machine learning to ethics and inclusivity, that taught cutting-edge technologies and management techniques for audience members of all types of backgrounds. They even had a panel where lead developers and founders of Python packages, such as matplotlib, Plotly, and NumPy gave talks on how to leverage their respective packages to maximize the potential of projects.
Through the Division, I have worked on other innovative, flexible, and impactful projects. For example, I co-founded Data 88: Economic Models, Berkeley’s first and only class that teaches a range of economic concepts, from basics such as supply and demand to advanced topics such as econometrics and financial economics, using Python and real-world datasets. Creating the course materials from scratch, working with other individuals passionate about economics, and teaching a class of 30 students has been an extremely rewarding experience.
Thanks to the opportunities the Division of Data Science and Information provides for its employees, I can confidently say that working here has been one of the best decisions I made in college.
The Division has a dynamic, high-impact startup culture that provides its student employees with numerous opportunities to grow and work with supportive teammates. It is quickly expanding, and working with self-driven individuals makes the experience even more dynamic and exciting. Student teams are the foundation of the Division, creating a relationship built on trust and collaboration, as exmplified by my experience traveling to and presenting at SciPy. Thanks to the opportunities the Division of Data Science and Information provides for its employees, I can confidently say that working here has been one of the best decisions I made in college.