Research Practicum

Build data chops and solve real world organizational, social, and environmental challenges

Interested in business analytics and digital technologies? Apply for our Advanced Research Practicum and earn elective credits at either the undergraduate or graduate level. Practicums involve corporate partners and encompass contemporary research, industry problems, and wider issues in the social and natural environments. Graduate and undergraduate students from any Northeastern college are encouraged to apply, with selection based upon the capacity to tackle high-impact analytics problems. Teams are curated to ensure interdisciplinary dynamics that are proven to yield powerful insights.

Earn 4 credits as an undergrad or 3 credits as a graduate student

Practicums run as elective courses (MKTG4606 or MKTG6606) during both Fall and Spring semesters. You'll have the opportunity to work closely with business professors on practice-focused research projects using data analytics, machine learning, or other cutting-edge technologies to solve business problems. While faculty provide a foundational approach for investigating research problems in analytics and data science, students are charged with identifying their own strengths and weakness and formulating a plan for training others while also receiving the training they need. Exchange and teamwork are core to the practicum, and you'll share your expertise through hands-on workshops. For example, previous students demoed:

  • Using Python to describe data and access SQL databases
  • Using Jupyter notebook special libraries for analytics and machine learning
  • How to visualize data effectively in analytics

I was challenged to learn new skills, relearn my strengths and unlearn misconceptions. For 4 months, I had the opportunity to work with real-world data and validate my findings together with academic theories.

Karan Desai

Once I started a data science project for another class, I realized how much I learned through this independent study. I was able to make visualizations with ease, I knew what data to analyze, and I felt more comfortable using machine learning models.

Chris Hadler

I gained invaluable leadership experience managing an analytics project that will help further shape my professional goals.

Spencer Levy

Application details for students

Participation in the Research Practicum is competitive and an application is required. Qualified applicants should have demonstrable skills in at least two of the following areas:

  • Python & R
  • Statistics or Econometrics
  • Machine learning
  • Problem solving
  • Creative visualization
  • Collaborative workstyle
  • Consumer behavior
  • Report writing
  • Presentation design
Kwong Chan

Questions? Contact Professor Chan.

Kwong Chan is a Senior Academic Specialist in Marketing and serves as the DATA Initiative's Executive Director.
Contact Professor Chan

Corporate Partners

Finding the Right Features

Ever wondered if you have found all the best features of the software you are using? Hubspot did. They tasked a student team with finding out how the process of customer feature discovery was related to overall feature utilization during free software trials of HubSpot's many products. The team compared a range of machine learning approaches to identify prototypical discovery processes, allowing for deeper understanding of the gaps in feature utility that get in the way of user monetization and retention. Now the HubSpot Product teams will be able to optimize that experience and allow customers to successfully navigate the HubSpot platform.

From B2B to B2C

To help Schneider Electric's customers succeed in a more electric world and ensure they find the most beneficial products, the company gave a team of Northeastern students several years of sales data. The student team created multiple Market Basket Analysis algorithms within Python to generate association rules to describe and predict buying behavior. This allowed the team to build a tool that found businesses who purchase like-minded products and in turn help Schneider provide more suitable product assortment. As a further benefit, potential inefficiencies within the supply chain were also highlighted. Visualizations within Tableau helped distill insights from these analyses. These tools provided proof points that demonstrate the value of customer data and guide further development of B2B and B2C product strategy.

Call for proposals

We invite expressions of interest that provide an opportunity for teams to use analytical skills to solve high-impact problems. Direct engagement with students is a further benefit where companies can get to meet their talent pool directly and where students receive direct exposure to industry professionals. Organizations are expected to cover any costs associated with the project, but there is no fee for working with the DATA Analytics Research Lab. Send all inquiries to

What we're looking for when choosing projects

The most suitable challenges are “problem-centric” with associated data that can be analyzed to explore the domain of the issue. The larger the scale and breadth of the data the greater the range of analytics tools that can be deployed to tackle the problem. Ideal parameters include:

  • Data previously accumulated and readily accessible
  • A problem that will impact the organization
  • A deliverable that can be achieved within 10 weeks.

Practicum Leadership

Practicum Alumni: Become a DATA Fellow

DATA Fellows work with faculty leaders to run the practicum and collaborate on a wide scope of academic and company projects. Fellows are eligible for small DATA research grants for some projects (subject to funding availability).

If you are an alumni of the Advanced Research Practicum, we invite you to apply. (Rare exceptions made for non-alumni.) Appointments are on semester basis and renewable until graduation.