GCI World September 2026 Session 2 Closing

Main ideas and lessons

• Course wrap-up and key technical concepts (NumPy)

  • The session concludes with a summary of learning:
    • How to create n-dimensional arrays (nd arrays) using np.array
    • How to apply universal functions (ufuncs) for array operations

• Course logistics for the next steps

  • There are two assignment types for this session:
    • Attendance surveys
    • Homework

• Assignment deadlines and submission rules

  • Both attendance surveys and homework are due October 8 at 11:00 UTC (stated as two weeks from the session).
  • Attendance surveys
    • Must be submitted via Omni campus
    • Late submissions are not accepted
    • To complete the course, students must complete at least seven surveys
  • Homework
    • Materials are hosted on Google Drive
    • The materials explain:
      • How to check answers
      • What to submit
      • Where/how to submit it
    • Students are directed to submit their Python code solution through Omni campus (via the homework section for the first session)

• Use of an AI assistant tool: “Curie”

  • The course provides Curie, an AI assistant that can answer:
    • Questions about course materials
    • Questions about lectures
    • Course logistics (e.g., deadline-related questions)
  • Login/usage guidance
    • Students should use the same ID as on Omni campus
    • Enter a question via “New Chat”
    • Example topic given: difference between a Python library vs. NumPy arrays
  • AI limitations and guidance for academic work
    • Generative AI is not prohibited for completing tasks
    • However, it may be incorrect, so students should:
      • Try solving themselves first
      • Ask other students for help
      • Use AI to improve understanding
  • Additional requirement for some tasks/competitions
    • Students may be required to submit the URL of their AI chat history
    • The course will have a protocol in the Q&A section for how this is handled

• Discussion point: how missing values affect statistics

  • When computing mean and standard deviation for temperature and precipitation:
    • A missing value can distort results
  • Specific warning:
    • A precipitation value appears as 999.9
    • This creates a very large average/aggregation, even if the real precipitation should be around a more reasonable value (example mentioned: average rainfall about 64)
  • The instructor states that the next lecture will cover:
    • How to handle missing values (data cleaning)

• Preview of the next session (Session 3)

  • Session 3 overview:
    • Pandas
    • Data cleaning with pandas
    • Specifically: cleaning up missing values
  • The typical session structure is reiterated:
    • Watch a video lecture
    • Practical exercises
    • Q&A session
    • Discussion of tasks

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Methods / instructions (detailed)

Submitting attendance surveys (Session 2 logistics)

• Use Omni campus to submit.
• Find the link in the student handbook.
• Deadline: October 8, 11:00 UTC
• Rule: Do not submit late (attendance surveys are not accepted after the deadline).
• Completion requirement: complete minimum of seven surveys total to finish the course.

Submitting homework (Session 2 logistics)

• Deadline: October 8, 11:00 UTC
• Homework materials are located on Google Drive.
• Follow the homework materials for:
  • How to check your answers
  • What exactly to submit
  • Where to submit
• Submission steps through Omni campus:
  • Log into Omni campus
  • Go to the homework section (e.g., at the bottom of the course page)
  • Click the relevant homework item (for the first session/homework context shown)
  • Upload/enter your Python code solution
  • Click “Send”

Using the Curie AI assistant

• Access Curie using the QR code (mentioned in the session).
• Log in using the same ID as Omni campus.
• Click “New Chat” and type a question.
• Examples of questions:
  • Conceptual: difference between Python library and NumPy arrays
  • Logistics: questions about deadlines (noted that handbook info is authoritative)
• Guidance:
  • AI may be wrong—try to solve first
  • You may ask other students
  • Use AI to help you better understand

Generative AI and competition/work requirements

• Generative AI is allowed (not prohibited) for completing tasks.
• For competitions or specific tasks:
  • Provide the URL of your AI chat history
  • Follow the Q&A protocol that will be provided.

Handling missing values (conceptual methodology mentioned)

• If the dataset includes missing values encoded as a numeric placeholder (example: 999.9):
  • This can make statistics like mean/standard deviation unrealistically large
• Missing value handling is deferred to next lecture (using pandas/data cleaning).

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Speakers / sources featured

• Instructor / course lead (unnamed)
  • Delivers the wrap-up, course logistics, Curie usage instructions, and the missing-values explanation.
• Curie
  • The course AI assistant (tool/source mentioned).
• Omni campus
  • Platform for attendance survey submission and homework upload.
• Google Drive
  • Location of homework materials.
• Student Handbook
  • Referenced as the source for links and authoritative logistics.