The Master of Science in Analytics (MSAN) program at the University of San Francisco is an innovative and challenging training program for developing future analytics professionals. The MSAN Program invites you to find out more about how your organization might benefit working with a group of highly motivated and committed students from this exciting graduate program.
The program seek clients interested in working with one or more teams of students on an Analytics Practicum project. These projects are centered on creating business value from data and extend over several months.
What is the Analytics Practicum?
As a central part of our 12-month curriculum, USF's MSAN program provides training in the leadership, teamwork, communication, professionalism and project execution skills that are vital for analysts and data scientists to be effective in the business world. We teach our students these skills through lectures, readings, workshops, written assignments, and simulations.
But there is simply no substitute for experience. Throughout the program, our student teams work on a real project with real (and usually messy) data from a client organization. Our faculty also manages and mentors the students to ensure client success.
In short, these "Practicum Projects" give our students invaluable first-hand experience as analytics professionals, while providing clients with tangible benefits from their hard work, knowledge and discoveries.
What makes Practicum projects successful?
The MSAN program demands a great deal from its students, including a strong commitment to client success. In addition, we work very hard to make sure clients are committed to the success of their Practicum project. While there are no hard and fast rules, from our experience we believe the most successful projects:
- have a general goal for the client organization that is stated in business terms rather than technical terms
- have a significant volume of data available to support the analysis
- require students to work hard to understand the business context
- do not have an easy solution
- are of serious interest to the client company, and have an internal champion
Learn more about previous Practicum projects
The MSAN program is now in its second year and we are pleased to report many successful and ongoing relationships with Paypal, Thomson Reuters, SurveyMonkey, Cisco, Mozilla and others.
Kicho Yu ('14)
, Spencer Boucher ('14)
and Trevor Stephens ('14)
worked with AutoGrid
to forecast energy use and peak power events for residential and commercial end users. They applied machine learning techniques to predict future demand so that power producers and facility managers could better plan for high load days. They also identified and visualized temporal/seasonal outliers in smart-meter data, and profiled various customer segmentations for a large energy utility client.Matt O'Brien ('14)
and Prateek Singhal ('14)
worked with SimplyHired
to analyse the relationship between macroeconomic and hiring trends in healthcare. They analysed over 3 years of hiring data within the nursing profession, and harvested the corresponding public stock information for a large set of health companies. Their results were used to determine if hiring is a leading or lagging indicator of stock movement.Can Jin ('14)
and Prateek Singhal ('14)
worked with Support.com
to study the chat transcripts that had been acquired for various companies over the years. They applied Text Analysis skills learned in the 'Text Analytics' class over a database which was approximately 4 TB in size.Manoj Venkatesh ('14)
, Dora Wang ('14)
and Li Tan ('14)
worked with Turbo Financial Group
to analyze customer responses to consumer loan campaigns. By applying a range of machine learning techniques, they successfully overcame challenges with noisy, unbalanced data-sets and built classification models to improve the response rate of marketing campaigns. Deeksha Chugh ('14)
, Conor O'Sullivan ('14)
, and Anuj Saxena ('14)
worked with Weather.com
to study the effect of changes in weather variables on the sales of consumer products. Using machine learning methods, the team developed a predictive model to link weather variables with weekly sales trends. This model paved the way for consulting business growth into major retail brands.Ashish Thakur ('14)
and Charles Yip ('14)
worked with Xoom Corporation
to create a production grade statistical algorithm for identifying botnet attacks. They were given millions of records involving user activity on the Xoom website. With the data, they successfully developed and trained two timeseries models that distinguished between botnet and user behavior. Xoom is planning to productionize their algorithms.
William Goldstein ('13) and Justin Battles ('13) worked with Mozilla to determine the significance of crash signatures. Using map-reduce to explore over 500GB of user crash reports, they were able to build an arrival rate model to use over 400k incoming crash signatures and extract trend lines. Not only were they able to provide guidance on the possibilities for crash signature analysis using large volumes of data but they went one step further and developed a dash-board to alert quality assurance team members of potential problems.
Pete Merkouris ('13) and Rachel Philips ('13) worked with Thomson Reuters to study the effect of revisions to company websites and the stock returns. They were able to apply statistical regression models and graphical techniques to search for signals in the stock returns based on revisions of the webpage.
Bingkun Li ('13) and Meenu Kamalakshan ('13) worked with Cisco to study customer data and identify how to effectively spend their advertising budget. They were able to identify profitable customer segments using clustering and make a recommendation on which segments to target.
Barbara Evangelista ('13), Funmi Fapohunda ('13) and Oscar Hendrick ('13) worked with Paypal to analyze the conversion, engagement and churn of mobile app users for the purpose of informing marketing strategy and app improvement. They applied a range of statistical techniques to study the behavior of segmented groups and their work led to the identification of trends.
Tak Wong ('13) and Spence Aiello ('13) worked with Survey Monkey to analyze the customer conversions. They extracted over half a million records to study the conversion rates of users from basic to paid accounts using SQL & R, and developed regression models and applied chi-square tests to predict an increase in conversion. This result was used to target the sending of impressions to basic account users.
How can your organization participate?
Students work from 10 to 15 hours a week for a company throughout their program (actually, 10 of their 12 months). Students can work as paid interns on your payroll or via a mutual NDA and data analysis agreement between your company and USF. A faculty member on our side typically acts as project lead with the students doing the work. We need someone in the client company that acts as a champion of the project, providing answers to questions, data, and so on. We usually work on a granularity of one semester but students have often continued with the same company through multiple semesters.
The first step is to discuss possible projects from your organization and how they might fit with the goals of the MSAN program. Professors Dixon and Intrevado can provide additional information and answer any questions:
Assistant Professor Matthew Dixon
Analytics Practicum Co-Director
Assistant Professor Paul Intrevado
Analytics Practicum Co-Director