The Data Skills Pathway is a 15-week, hands-on experience where you learn how to turn real-world data into real-world decisions. You’ll build practical skills using raw datasets from instruments and models. The pathway is designed to build skills aligned with roles such as data analyst, meteorologist, operations analyst, and environmental/aviation data technician.

Media:

• UND Today press release: UND launches Data Skills Pathway to prepare students for AI-age careers
• WDAY TV Story Clip (January 24, 2026): Launch of Data Skills Pathway

• You start with a topic that interests you. You work with mentors to turn your interest into a real problem that can be explored using data.
• You work with real data, not textbook-perfect data. You learn how to check data quality, organize files, document your process, and make careful conclusions.
• You learn the full workflow end-to-end. From defining a question and finding data → to analysis and visualization → to clear communication.
• You connect your work to real research and applications. You may work with data related to weather, aviation, aerospace, sensors, engineering, cybersecurity, Artificial Intelligence, or other applied research areas.
• You share progress during weekly meetings. You present short updates, figures, papers, questions, challenges, or early results and learn from other students.
• You learn useful tools from mentors. Mentors may provide short sessions on Python, Excel/CSV files, data cleaning, visualization, reading papers, Artificial Intelligence, sensor data, or scientific communication.
• You produce a portfolio-ready mini-project. You finish the semester with something useful, such as a figure, table, script, notebook, dataset summary, short report, poster, or presentation.
• You can bridge into paid research and internships. Strong participation can help prepare you for paid research work, future internships, and other research opportunities.

The Data Skills Pathway is organized into four stages. These stages help students move from an initial interest to a real data-based project, while building research, technical, and communication skills.

You will begin by thinking about topics that interest you. These may include aerospace, weather, aviation, sensors, engineering, cybersecurity, Artificial Intelligence, data science, environmental science, or another area.

During this stage, you will:

• Learn about the Data Skills Pathway.
• Review available project areas.
• Meet instructors, mentors, and other students.
• Complete initial orientation steps.
• Share your interests and goals.
• Begin thinking about what kind of problem you may want to explore.

Goal of this stage: Identify a topic or general area that interests you.

After identifying your interest area, you will work with a mentor to turn that interest into a focused project question.

For example:

• “I am interested in aerospace” may become “How can weather data support safer UAS operations?”
• “I am interested in sensors” may become “How can sensor data be used to detect changes in visibility?”
• “I am interested in cybersecurity” may become “How can data patterns help identify unusual system activity?”

During this stage, you will:

• Discuss project ideas with mentors.
• Choose or refine a project topic.
• Identify possible data sources.
• Prepare a short project brief.
• Define what you want to produce by the end of the pathway.

Goal of this stage: Turn your interest into a clear project question that can be explored using data.

Once your project is defined, you will begin working with data and learning the tools needed for your project.

Depending on your project, you may learn how to:

• Organize files and folders.
• Work with Excel, CSV files, Python, or Jupyter notebooks.
• Clean and check data.
• Create figures, plots, maps, or tables.
• Read and summarize research papers.
• Use basic Artificial Intelligence or machine learning concepts.
• Work with sensor, weather, aviation, engineering, or cybersecurity datasets.
• Document your steps and results.

During Thursday meetings, you will also hear short tool sessions from mentors and share updates on your progress.

Goal of this stage: Learn practical data skills while making progress on your own project.

At the end of the pathway, you will prepare a short final project update. This does not need to be a finished publication-level project. The goal is to clearly explain what you worked on, what data or tools you used, what you found, and what the next step could be.

Your final product may include:

• A figure or table.
• A short script or notebook.
• A cleaned dataset or data summary.
• A short written report.
• A poster or presentation.
• A project reflection.
• A next-step plan for continuing the work.

Goal of this stage: Communicate your work clearly and leave with something useful for future opportunities.

By the end of the Data Skills Pathway, students will have practiced how to turn an interest into a small data-based project. They will work with a mentor, explore a real problem, use data to make progress, and communicate what they learned.

Students will be able to:

• Identify a topic or question that interests them.
• Work with a mentor to define a realistic data-based problem.
• Find, organize, and begin working with project data.
• Create a useful project output, such as a figure, table, script, notebook, short report, or presentation.
• Share weekly progress, questions, figures, papers, or challenges with the group.
• Explain their results, limitations, and next steps clearly.
• Learn practical data skills while making progress on their own project.

This experience can help students prepare for undergraduate research, paid internships, graduate school, technical careers, and future work in data-driven fields.

Data Skills Pathway Fellows are expected to participate actively, communicate clearly, and make steady progress on a data-based project.

Fellows do not need to begin the pathway as experts. The goal is to learn by doing, with support from mentors, instructors, and other students.

Fellows are expected to:

• Attend Data Skills Pathway meetings when possible.
• Share their interests, project ideas, questions, and progress with the group.
• Work with a mentor to turn an interest into a realistic data-based problem.
• Complete or update their Student-Mentor Workspace.
• Prepare a learning plan, work schedule, and short project brief.
• Communicate with their mentor and ask questions early when they are stuck.
• Bring evidence of progress to check-ins, such as a plot, table, script, notebook, paper summary, error message, or draft output.
• Document their work regularly, including tasks completed, methods used, files created, results, quality checks, and next steps.
• Keep project files organized using clear file names, approved storage locations, and good version-control habits.
• Follow research integrity rules, including honesty with data and results, proper credit, and team rules for sharing project materials.
• Participate in short tool sessions and apply those skills to their own project.
• Prepare a final project update, presentation, poster, short report, figure, script, notebook, dataset summary, or other useful project output.

Fellows should come to weekly meetings prepared to answer three simple questions:

• What did I complete this week?
• What question, challenge, or blocker do I have?
• What is my next step?

By the end of the pathway, each Fellow should have made progress on a real project, learned practical data skills, and created something useful that can support future research, internships, graduate school, or technical career opportunities.

• For Students
• For Mentors

• Expectations Guide 2026 (PDF)
• Participant Guide 2026 (PDF)

• The Student- Mentor Workspace Organization & Structure
• Data Skills Pathway Projects
• Data Skills Pathway Fellows (Students)
• Data Skills Pathway Mentors

• Atmos Home
• DokuWiki Syntax
• UND launches Data Skills Pathway to prepare students for AI-age careers
• David Delene's Home Page
• AtSc 494 - Spring 2026