With the motto ‘Change the World from Here’, the University of San Francisco (USF) is one of the leading programs offering Master of Science in Data Science delivering a rigorous curriculum focused on mathematical and computational techniques in big data.
In an exclusive interview with Analytics Insight, David Uminsky, Director of MS in Data Science and Executive Director of the Data Institute at the University of San Francisco shares how the program covers the spectrum of tools and techniques which are being adopted by enterprises to tackle data challenges, and the different roles that data specialists can fill in today’s forward-thinking organizations.
The industry is seeing a rising importance of Big Data Analytics and AI. How do you see these emerging technologies impact the business sector?
I think this is understated to say the least. In the bay area business sector, Analytics/Data Science/Machine Learning/Artificial Intelligence has moved from an emerging technology to become a core tool to advance and grow a company. Classical quant and business intelligence organizations in companies are augmented or re-born with modern data science teams using machine learning and modern data science methods to extract value from previously “difficult” (aka messy, bigger and unstructured) datasets.
How is the University of San Francisco’s Analytics and Data Science Program contributing to the growth and transformation of analytics and big data education?
Over the past 7 years, USF’s Data Science education program has rapidly grown into a national leader in both academic education as well as how an academic institution should interface with industry. With a rapidly growing undergraduate data science major population, a class of over 80 graduate students in our MS in Data Science program and our portfolio of evening certificate courses that range from the basics of data science such as SQL and Data Science for Product Managers to Deep Learning Part I and II are taught by Jeremy Howard. As a whole, USF and the Data Institute (DI) is at the center of the growth and transformation of Data Science Education.
In addition to offering state of the art education, we are transforming the data science community as a whole. At the core of the Jesuit Mission of USF as well as a central tenet of the DI is to broaden the representation of the Data Science community. In a field that has been estimated at ~75% male and less than 15% underrepresented minorities (including African-American and Latinx) our graduate Data Science student body has been gender balanced for the last 4 years and our recent Diversity Fellows initiative has helped fund over 60 students from non-traditional backgrounds to take our certificate coursework.
What is the edge University of San Francisco’s Analytics and Data Science Program has over other institutes in the industry?
We have many competitive differentiators that set USF’s Data Science program apart but will focus on just two.
1. Our integrated relationship with Bay Area Data Science Industry through our practicum program. Our program places student outcome as the most important focal point of any discussion around curriculum and co-curricular decisions. From the onset, the program replaced a traditional thesis requirement with a 9-month, 2 days a week with a Data Science practicum experience with a company, non-profit or government entity. Our faculty and staff work to curate a list of industry partners with advanced and serious data science challenges that need solving. This past week, we just hosted our pitch days where nearly 60 companies and non-profits pitched projects to our graduate students and included companies like ATT, Eventbrite, Reddit, Ubisoft, PG & E, General Electric, Mozilla, Schmidt Foundation and many more. These projects become the basis of turning graduate ML and DS coursework into real-world experience and insight. They also are the singular differentiator for our students on the job market.
2. We have an outstanding set of multidisciplinary faculties who continuously update our rigorous and technical curriculum to ensure it is training our students to be a state-of-the-art data scientist, analysts and ML engineers. Our faculty have Ph.D.s in mathematics, statistics, computer science and business and every course in our program is designed only for the MSDS students so that each module builds carefully on the previous module’s coursework. We consult with our industry board several times a year to continually assess the relevance of our coursework and continue to innovate and improve our curriculum.
Kindly brief us about your role at University of San Francisco’s Analytics and Data Science Program and your journey in this highly promising sector.
I have been the graduate director for the MS in Data Science program for the past five years and was recently appointed as the Executive Director of the Data Institute. My Journey began by earning a BS in Mathematics from Harvey Mudd College and earned a Ph.D. in Mathematics from Boston University. Before joining USF, I was a combined National Science Foundation and UC President’s Fellow at UCLA, where I was awarded the Chancellor’s Award for post-doctoral research. I was selected in 2015 by the National Academy of Sciences (NAS) as a Kavli Frontiers of Science Fellow. Each year, 100 researchers under the age of 45 are selected by the academy, and the 20% of the current NAS were previous Kavli Fellows.
My research interests are in applied mathematics and begun first in dynamical systems and partial differential equations and have since moved into the area of mathematical data science. It really happened more out of the curiosity of how mathematics can be useful in this new field. It turns out mathematics is a powerful tool and I now mostly work in the area of unsupervised machine learning, data clustering, algebraic signal processing, as well as pattern formation. When I did arrive at USF, I immediately began the work of founding the B.S. in Data Science degree which at the time was only the 4th such degree in the US. Two years later I was appointed the graduate director of Data Science.
What would you advice to aspiring big data and analytics candidates?
Be curious and don’t be afraid to dive right in. Our program’s philosophy has consistently encouraged not just mathematics and computer science majors to succeed in Data Science but also sociologists, business students and even a few literature majors to become leading data scientists in industry.
What are some of the challenges faced by the industry today?
I think underrepresentation and lack of diversity, along with a severe shortage of agreed-upon ethical guidelines for practicing data scientists are the twin, paramount challenges this industry faces today and will continue to struggle with.
Please share some major achievements of the University of San Francisco’s Analytics and Data Science Program under your leadership
I think the program is amazing but of course, there I’m biased. If I had to pick one, I’m most proud of our students and alumni. Over our 7 years, our students have consistently had an outstanding success from our program. In the early years, and when the program was small, we consistently had 100% placement within 3 months. As the program has grown from 13 students in cohort 1 to a graduating class of 80 for cohort 6 we have been able to continue to diversify our student body while increasing the incredible depth and strength of our candidates. This has been possible because the applicant pool has grown exponentially, from roughly three dozen in the pilot year to nearly 700 applications last year. We have continued to hold our 3-month full-time offer rate above 90% with students routinely getting multiple offers and accepting data science positions at Amazon, Google, Facebook, McKinsey, Twitter and the most elite data science teams in the world.
Can you throw light on the latest employment trends in big data and analytics industry?
We have seen continued demand for very technically trained data scientists. Median salary has climbed year-over-year every year and we’ve seen major growth in positions in more traditional industries such as banking, consumer goods and energy that are newer to developing Data Science at their firms.