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Course Overview
Course Title: ZPEM2312 Fundamentals of Data Analysis
Week 1 Topic: Course Overview and Basic Statistical Processes
Lecturer: Dr. Leesa Sidhu
Email: l.sidhu@unsw.edu.au
Affiliation: Senior Lecturer, School of Science, UNSW Canberra
Resource: Adapted from "Mind on Statistics" PowerPoint slides
Introduction: About the Lecturer
Background:
Senior Lecturer & Student Experience Coordinator in School of Science
Fellow of UNSW Scientia Education Academy
6 years as a high school teacher in Queensland (Years 8-12) focusing on maths and science
Lecturing at UNSW Canberra since 1997, full-time since 2007
Teaching Philosophy:
Passionate about teaching and student welfare
Encourages student questions and feedback
Course Information
Moodle Site: Access for course outline and resources
Assessment Structure:
Quizzes and final exam (online with face-to-face invigilation)
Prerequisites: Laptop/device with Excel and Moodle access required for labs
Lab Assessment: Check the 'Lab Work Assessment' handout for details
Student Performance: Historical data indicates the course positively impacts WAM (Weighted Average Mark)
Additional Assistance
Support Classes: Weekly optional support classes available to negotiate with students
Hybrids and Blackboard Collaborate from previous years
Maths and Physics Support: Additional resources available via Moodle
FDA Peer Tutors: Looking to establish a tutoring program with volunteer students to assist peers during labs
Student Engagement Opportunities
Support Structures:
SSLC (Student-Staff Liaison Committee) for real-time course feedback
SWAG (Student Wellbeing Action Group) to promote student wellbeing initiatives
Volunteering for SSLC/SWAG: Contact Dr. Sidhu if interested in representation
Tips for Success
Strategies:
Be organized and proactive about learning
Balance life commitments based on student feedback
Advice from Former Students:
Complete weekly textbook questions and past quizzes for exam preparation
Utilize lab time effectively and participate in extra lessons offered
Theoretical Framework
Self-Determination Theory:
Autonomy: Choices in learning
Competence: Providing scaffolding and feedback
Relatedness & Belonging: Encouraging interpersonal connections
Student Support Services
Equitable Learning Services:
Academic adjustments for disability or health conditions
Academic and Study Skills: Additional support services available on request
Mental Health Resources: Includes 24-hour support links and local chaplain contacts
Introduction to Statistics
What is Statistics?
Scientific study of learning from data; involves understanding variation and uncertainty
Relevant across disciplines requiring data interpretation
Case Studies in Statistics
Example 1: Identifying a new subspecies of dolphin through morphological data analysis
Example 2: Resident views post-2011 earthquake in Christchurch using surveys
Statistical Data Collection
Importance: Planning is crucial for effective data collection to answer statistical questions accurately
Exploration of Types of Datasets: Understanding raw data and what constitutes a variable relevant to investigations
Investigative Framework
Data Types: Discrete vs. continuous variables; qualitative vs. quantitative distinctions
Survey Methodology: Understanding population and samples in statistical investigation
Bias Awareness in Sampling
Common Biases:
Selection, nonresponse, and response bias can skew results
Designing Effective Experiments
Experiments: Assessing the effects of manipulated variables
Importance of randomization in treatment groups to avoid biases
Graphical Information in Statistics
Exploratory Data Analysis: Using visuals to present data
Different graph types for categorical and continuous data representation
Outliers: Identify and analyze outliers' effects on data conclusions
Topics for Further Study
Chapters 2-3: Gathering and preparing data, translating data into graphical information.
Focus on frequency analysis, types of graphs, and effective data presentation.