Fellowship Guidelines

Nebula Schedule

Learning Resources

Undergraduate Research Opportunities

Previous Nebula Cohorts

Fellowship Guidelines

Nebula Fellows will be paired with a lab mentor (a graduate student or a postdoctoral researcher) who will provide training and guidance on their data analytics project, but Fellows are also expected to be proactive about learning new skills necessary for their project’s success. We expect Fellows to be present in the lab and dedicated to their project so that they will fully reap the benefits of this learning experience. 

1) Fellows and mentors must attend the Nebula fellowship kickoff meeting to discuss program logistics, requirements, and goals. During this meeting, fellows and mentors will jointly complete a Project Commitment form to establish clear expectations of both parties for the project period. 

2) Nebula fellows are expected to work ~10 hours per week (split between 2-3 days) on their data analytics project. They should conduct this work in-person or remotely as agreed upon with the lab mentor. 

3) Fellows should coordinate with their mentor to ensure both parties are present in the lab at approximately the same time so that mentors are available to provide training, answer questions, and provide feedback and direction as needed.

4) Fellows and mentors should have regular weekly meetings to set assignments with clear deliverables and deadlines.

5) Fellows must complete all appropriate lab safety training.

6) Data analysis resources are provided by the Dean’s office to support Nebula fellows in their training on the Learning Resources page. 

7) Fellows must track and submit their biweekly work hour logs through Workday.

8) Nebula fellows must give a final presentation of their data analytics project to the cohort. Mentors should provide guidance and feedback on their fellow’s presentation. 

9) Fellows and mentors are asked to respond to feedback surveys from the Dean’s office at the beginning, middle, and end of the fellowship period.

10) Fellows and mentors are expected to communicate with Nebula program managers via email. Please check email daily and respond promptly to requests (within 24 hours). 

Suggested Best Practices for Nebula Undergraduates and Mentors: 

• Meet at the beginning each week to plan activities

• Meet at the end of each week to review what was accomplished

• Create and maintain a shared google docx to track and log weekly progress

• Remain flexible, but set a weekly schedule

• Communication is very important to set expectations for any given week. 

• Mentors/mentees should agree on a communication method (e.g., email, Slack, WhatsApp, etc.) and appropriate response times.

If any problems are encountered during the fellowship, mentors and fellows can contact Dr. Alex Brozena (abrozena@umd.edu) for additional guidance. 

Nebula Schedule

The Nebula Fellows meet regularly throughout the semester on Tuesdays at 4 pm, including online and in-person events. 

Fall 2026 Programming

Nebula Kickoff: September 8, 4-5 pm

Industry Seminar: September 29, 4-5 pm

Mentor Check-in: October 13, 4-5 pm

Fellow Check-in: October 20, 4-5 pm

Science Communications Seminar: November 17, 4-5 pm

Closing Meeting: December 4, 4-6 pm

Learning resources

Data Analysis Basics:

Foundations of Data Analysis:

Basics of Data Analysis 

basics of statistics

Understanding data

General introduction to R, Python & SQL and choosing right tool for your data

Data Collection & Storage

Data Collection Methods in general

Data Collection in Research Projects

Data collection Methodologies

Data Cleaning and Preparation:

Basics of Data Cleaning

Data Cleaning using Python

Data cleaning using R

Analysing the Data / Exploratory Data Analysis (EDA)

Understanding EDA

Steps to follow for EDA

Data Interpreting / Statistical Analysis & Modeling:

What is Statistical Modeling

Predectice Analytics

Data Visualization and Communication

Basics of Data Visualization

Tool Specific Learning Resources:

R:

Hands-on Programming with R

Basics of R for Data Analysis

Hands on Exercise

R for Data Science

Data Mining Using R

Python:

Basics of Python

Data Analysis using Python

Python library functions for Data Science

Image Processing using Python

Python for building Predictive Models

MATLAB:

MATLAB Essential Training

MATLAB Training

Excel:

Data analysis using excel

Statistical analysis using Excel

Excel basis for data analysis (Coursera)

Introduction to data analysis using excel (Coursera)

Linux

Basics of Linux command Lines

Undergraduate Research Opportunities

We encourage Nebula Fellows and applicants to continue their research experiences through the variety of programs and opportunities available at UMD:

Undergraduate Research Programs at UMD

Nebula Research Data Fellowship 

Office of Undergraduate Research (OUR)

ForagerOne - OUR’s tool for connecting undergraduates to research opportunities on campus

MTech ASPIRE - A Scholar's Program for Industry-Oriented Research in Engineering

Vertically Integrated Projects (VIP)

Training and Research Experiences in Nonlinear Dynamics (TREND) Research Experiences for Undergraduates (REU)

Undergraduate Research Program in IREAP

Immersive Research Internship Experience (IRIE) 

Student-Proposed Innovation & Research Experience (SPIRE)

Research for Intelligence & Security Challenges (RISC) Initiative internship program 

Ronald E. McNair Post-Baccalaureate Achievement Program

Undergraduate Research Immersion Program (URIP) 

Independent Research Study for Credit Hours

Research for credit is individually managed at the department level. Reach out to the Department Director of Student Affairs/Undergraduate Studies for more information or talk to your advisor about how to earn credit hours for research.

Electrical and Computational Engineering (ENEE488/499/499L)

Bioengineering

Aerospace

Fire Protection

Chemical and Biomolecular Engineering (CHBE468)

Civil and Environmental Engineering

Nebula Cohorts