Lecture Notes

Introduction

This lecture introduces the instructors, Floris and Jennywa, and provides essential information about the course ENVX1002, focusing on data science and statistics with real-world applications.

Course Logistics

  • Contact: Floris (unit coordinator) for any major issues.
  • Slides: Available on the Canva site.
  • Safety: Instructions for emergencies include checking for immediate danger, closing laptops, and following green exit signs to the assembly area.
  • Student Charter: Details rights and responsibilities; report breaches to student affairs.
  • Canvas: The primary online resource. Students should access it to stay organized with the study dashboard.
  • Support: Academic and personal well-being services are available via the service and support portal.
  • In-Person Hubs: Assistance available at Fisher Library, Susan Wakele Building, and Belinda Hutchison (location unspecified) from 9-5 on Mondays.
  • Student ID: Obtain from the Jane Russell Building by week three, required for the first assessment.

Academic Integrity and AI

  • AHEM: Completion required by the census date (March 31) to avoid penalties.
  • AI Usage: Generative AI is allowed in some assessments with a detailed journal required, but is prohibited during invigilated exams.
  • Stat Bot: A university-provided AI tool for learning, accessible without needing a personal AI account.
  • Recommended AI Tools: Microsoft Copilot Chat is preferred; ChatGPT and Deep Seek are discouraged due to privacy concerns.
  • Mandatory Canvas Modules: Respect at Sydney, Engaging with Academic Honesty (AHEM), Engaging with Civility, and Anti-Slavery Awareness.

Instructors and Course Content

  • Floris: Associate Professor in Stochastic Hydrology with a background in data science.
  • Jennywa: Lecturer in Biostatistics, previously a marine ecophysiologist.
  • John Wah: Will lecture on data visualization.
  • Course Structure: Four weeks each on describing/visualizing data, making decisions with data (hypothesis testing), and modeling relationships in data (linear regression).

Resources

  • Lectures & Tutorials: Recorded and available online.
  • Labs: Two-hour labs every week.
  • Ed Discussion: Online discussion board.
  • GitHub: Course code and resources are available; students can post issues or pull requests.
  • ENVX Resources: Accessible on GitHub and Canvas.

Practical Information

  • Practicals: Held at the South Everly campus; travel time should be factored in.

Course Content Overview

The course covers:

  • Reproducible science and statistical programming using R and RStudio.
  • Central Limit Theorem and Hypothesis Testing.
  • Linear and nonlinear functions.
  • Focus on examples from life and environmental sciences.

Assessment

  • Early Feedback Quiz (EFQ): A quiz to ensure students are engaged by week three.
  • Project 1: Exploring data (individual written report).
  • Coding and Data Skills Evaluation: In-class test (15%).
  • Group Work Task: Presentation (5 minutes).
  • Final Exam: 50% of the grade.
  • Attendance: Monitored via QR code at practicals.

Software and Tools

  • R and RStudio: Used for statistical computing and data visualization. R is the engine and RStudio is the user interface.
  • Quarto: Used for creating reproducible documents; integrates code and results seamlessly.