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--- atmos:data_skills_pathway:home [2026/02/09 18:55] – marwa
+++ atmos:data_skills_pathway:home [2026/09/04 21:15] (current) – [Workspace Links] marwa
@@ Line 6: / Line 6: @@
 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:** [[https://blogs.und.edu/und-today/2026/01/und-launches-data-skills-pathway-to-prepare-students-for-ai-age-careers/|UND Today press release: UND launches Data Skills Pathway to prepare students for AI-age careers]]
+**Media:**
+  * [[https://blogs.und.edu/und-today/2026/01/und-launches-data-skills-pathway-to-prepare-students-for-ai-age-careers/|UND Today press release: UND launches Data Skills Pathway to prepare students for AI-age careers]]
+  * [[https://drive.google.com/file/d/121WGE89vhB0Va0-kAzyQsY9fnPO29bTj/view|WDAY TV Story Clip (January 24, 2026): Launch of Data Skills Pathway]]
 ===== Why is the Pathway unique? =====
-  * **You work with real data (not textbook-perfect data).** You learn how to check data quality, document your process, and make defensible conclusions.
-  * **You learn the full workflow end-to-end.** From organizing files and logging metadata → to analysis → to clear communication.
-  * **You connect to live systems and real events.** You’ll interpret measurements and patterns tied to actual conditions (not made-up examples).
-  * **You produce a portfolio-ready mini-project.** You finish the semester with deliverables you can show to an employer or internship mentor.
-  * **You can bridge into paid research + internships.** Strong performance can lead to paid research work and future opportunities.
-===== What you will do (in 4 stages) =====
-  * **Weeks 1–2 | Orientation**
-    - Learn what “data jobs” look like and how this pathway maps to real careers
-    - Set your goals and choose a project direction
-  * **Weeks 3–4 | Tools + programming workshop**
+  * **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.
-    - Learn the core tools used across projects (data handling, plotting, basic scripting)
+  * **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.
-    - Build good habits for clean, reproducible work
+  * **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.
+===== What You Will Do: Four Stages =====
-  * **Weeks 5–10 | Guided data work**
+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.
-    - Work on a **mentor-suggested project** using real datasets (instruments and/or model output)
-    - Keep a clear data log + weekly progress log
-    - Get mentor feedback each week
-  * **Weeks 11–15 | Capstone mini-project**
+==== Stage 1: Explore Your Interests ====
-    - Answer a focused question with data
-    - Create your final deliverables (report + slides + reproducible workflow)
-===== What is expected of Fellows? =====
+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.
-  * **Commit weekly time** and make steady progress.
-  * **Meet with your mentor regularly** and come prepared with updates + questions.
-  * **Communicate early** if you are stuck or your schedule changes.
-  * **Document your work every week**:
-    - Weekly progress log
-    - Data/model log (what data you used + what you changed)
-    - Reproducible scripts/notebooks + labeled figures
-  * **Complete the pre- and post-surveys** to help evaluate and improve the pathway.
-  * **Finish the required deliverables**: short report + short slides + a complete project folder that reproduces your results.
+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.
+==== Stage 2: Define a Data-Based Problem ====
+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.
+==== Stage 3: Build Data Skills Through Your Project ====
+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.
+==== Stage 4: Share Your Results and Plan Your Next Step ====
+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.
+==== Student Outcome ====
+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.
+===== What Is Expected of Fellows? =====
+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.
 ===== Interested? =====
   * [[atmos:data_skills_pathway:students|For Students]]
@@ Line 49: / Line 144: @@
 ===== On-boarding Documents =====
   * {{:atmos:data_skills_pathway:Undergraduate-Expectations-Guide.pdf|Expectations Guide 2026 (PDF)}}
-  * {{:atmos:data_skills_pathway:DataSkillsPathwayParticipantGuide.pdf|Participant Guide 2026 (PDF)}}
+  *{{:atmos:data_skills_pathway:data_skillspathwayparticipantguide.pdf|Data Skills Pathway Participant Guide - Fall 2026}}
 ===== Pathway Fellows/Mentors =====
 ==== Workspace Links ====
-  * [[atmos:dsp:workspace|The Student- Mentor Workspace Organization & Structure]]
+  * [[atmos:dsp:workspace|The Student- Mentor Workspace]]
   * [[atmos:data_skills_pathway:workspace:projects|Data Skills Pathway Projects]]
-  * [[atmos:dsp:Students Workspace|Data Skills Pathway Fellows (Students)]]
+  * [[atmos:dsp:List of Data Skills Pathway Student Participants]]
-  * [[atmos:dsp: Mentor Workspace |Data Skills Pathway Mentors]]
 ==== Other Links ====
   * [[atmos:home|Atmos Home]]