Applied Analytics Practicum
Every MSA student is required to take the Applied Analytics Practicum course (CSE/ISYE/MGT 6748) before graduating. This six-credit course gives all of our students an opportunity to do an important applied analytics/data science project with a company or organization that really cares about the results.
Most of our students use an analytics/data-science internship as their project for the course. US citizens and Permanent Residents may also use a full-time permanent analytics/data-science job for the course. Companies also bring projects to us for our students to work on in teams each semester. One way or another, before graduating every MSA student gets experience working with a company/organization applying the data science and analytics skills they’ve gained through the MSA curriculum to an important real-world problem.
The Applied Analytics Practicum course also includes lessons on issues of professional analytics and data science practice from experts in things like ethics, leadership, change management, etc. Students also have access to faculty, TAs, and alumni mentors to help guide them through the course of their projects, as needed.
- CSE 6242 Data and Visual Analytics
- MGT 6203 Data Analytics in Business
- A total of at least 7 (preferably 10) courses toward the MS Analytics degree
- A company or organization’s advanced analytics or data science project, approved by the MS Analytics program
The course is designed to give students experience in how to:
- properly define and scope an advanced analytics or data science project to fit the needs of an organization
- apply appropriate analytics ideas, methodologies, and tools
- create value, insight, and knowledge using data science skills and principles
- manage a project
- present results professionally in writing and orally
There are three types of projects that students may do in this course:
- Employer Project, Internship, Graduate Research Assistantship, etc.: Individual students working at a company or other organization for the semester on a data science project that has been approved for use in this course. Students who wish to pursue this option will receive career search support from MSA Career Services but are responsible for finding their own internship. Internships are typically paid at industry standards for graduate data science talent. For international students, this employment must meet visa status requirements; international students are advised to contact Georgia Tech’s Office of International Education.
- Sponsored Practicum Project: Small teams of ~3-4 students working with a company or other organization on a data science project they have submitted to Georgia Tech. Teams will work remotely from the company site (e.g., at Georgia Tech). Sponsored Practicum Projects will be provided by MSA Career Services each semester and are always available to students upon request. Students will get to choose among submitted projects, space permitting (with the constraint that, whenever possible, only one team may work on each project). Students will not be paid for remote practicum projects. Teams will meet regularly with the sponsoring company and will also be paired with an MSA alumni mentor.
- Full-time Role (NOTE: This option is only available to US Citizens and Permanent Residents): Individual students working full-time in a permanent position at a company or other organization for the semester on a data science project that has been approved for use in this course. Students who wish to pursue this option will receive career search support from MSA Career Services but are responsible for finding their own full-time position.
There are three main requirements of an applied analytics practicum project:
- Methodology: The project must require the use of advanced analytics/data science skills and knowledge learned in the MS Analytics curriculum, or built upon that knowledge. The purpose is for students to get significant project experience using what they have learned.
- Value: The project’s goal should be to create significant value, insights, and/or knowledge for the company or organization.
- Magnitude: The project should be significant enough to require the full semester allotted.
It is expected that the student(s) and employer will agree on a communication schedule, and that students are responsible for making sure communication is timely and smooth. A supervisor on the company/organization side will be asked to submit an evaluation form at the end of the semester; this evaluation can be a significant contributor to the student’s grade.
Additionally, students must submit the following reports during the semester:
- Certification Form: Students must complete a certification form at the beginning of the semester confirming participation in the practicum and summarizing the work they’ll be doing.
- Midterm Report: A midterm progress report must be submitted in the form of a slide deck. The midterm progress report should explain the purpose of the project, what the student has completed thus far, and what they plan to accomplish by the end of the semester.
- Final Written Report: The final report should explain the purpose of the project, the student’s approach to solving the problem, and what the results, insights or recommendations are.
- Final Presentation: Students must give a final presentation to company representatives. This is the student’s opportunity to present their approach and findings and answer any questions the company may have about their work.
Sponsored Practicum Project Questions
We encourage students to be in Atlanta for the Sponsored Practicum Project. We have found that this provides for a more robust practicum experience because teams can meet in person throughout the semester. However, it is permitted for students to be remote for their Sponsored Practicum Project as long if they meet the following requirements: (A) they are located somewhere in the US and (B) they agree to be present for in-person meetings with the sponsor (i.e. kick-off meeting, final presentations, plant/factory tour, etc.) It is not permitted for students to live abroad during the project.
Students will have the chance to review each project proposal as well as meet the project sponsors during the practicum kick-off meeting, which is held during the first week of the semester. Students will then have the opportunity to rank their project preferences, which we use to form teams. We do our best to give students one of their preferred projects, if possible.
Sponsored Practicum Projects are provided by MSA Career Services and are available in the Fall, Spring, and Summer semesters. Students should let their career advisor know if they’re interested in doing an on-campus practicum project.
Teams of 3-4 MSAs work on a real-world data science problem provided by the corporate partner for 10 weeks over summer semester or 15 weeks over fall or spring semester. Teams do not work at the company site but may be required to attend occasional on-site meetings over the course of the project including the project kick-off, site visits and final presentation.
Each student is expected to spend approximately 20 hours/week working on the project. Your day-to-day schedule may differ from your teammates, but you should expect to have a weekly conference call with the corporate sponsor as well as weekly team meetings.
We typically have a wide range of organizations who sponsor our on-campus practicum projects including Fortune 500 companies, mid-sized companies, start-ups and nonprofits. Past sponsors have included Delta, NCR, NASA, Habitat for Humanity, The Home Depot, Accenture, The Baltimore Orioles, Credigy, Wells Fargo, US Olympics Committee, and Emory Healthcare.
Here are some examples of past practicum projects:
- Analyze and predict timeline risk associated with clinical studies for a pharmaceutical company
- Perform social media sentiment analysis for a telecommunication company
- Analyze and predict likelihood of injury for players based on their training schedule, for a college sports team
- Predict paper breaks in paper manufacturing process in advance and identify potential causes
- Develop a predictive model and risk calculator to identify patients as high risk for being readmitted to the hospital
- Apply neural network to time-series data from consumer payment history to predict future payment delinquency