Workshop_1_1_

Page 1: Workshop Introduction

  • Title: STAT1004 Introduction to Scientific Data Analysis Workshop 1

  • Instructor: Unknown Author

  • License: CC BY-NC

Page 2: Course Overview

  • Unit Coordinator: Dr. Roger Collinson

    • Room: 314.351

    • Phone: 92662127

    • Email: r.collinson@curtin.edu.au

  • Workshop: 1 x 2-hour session per week

  • Laboratory: 1 x 2-hour session per week

  • Admin Contact: Curtin Connect, building 102

    • Phone: 1300 222 888

    • Website: https://students.connect.curtin.edu.au/app/ask

Page 3: Student Resources

  • Essential Contacts

  • Unit Outline

  • Useful Resources

  • Computer Labs / Workshops:

    • Weekly teaching notes, lab exercises, and solutions

  • Assessments

  • Feedback

  • OASIS - BlackBoard

Page 4: Student Responsibilities

  • Constructive participation in learning experience

    • Attend classes and engage in activities and discussions

    • Spend time outside class reviewing material and applying techniques to various datasets

    • Importance of practice with knowledge application

  • Reference: Student Charter at https://students.curtin.edu.au/essentials/rights/student-charter/

Page 5: Quote on Success

  • "The only place success comes before work is in the dictionary."

    • Author: Vince Lombardi

Page 6: Academic Integrity

  • Importance of maintaining academic honesty

  • Plagiarism Monitoring can be found at: http://academicintegrity.curtin.edu.au/

Page 7: Program Calendar - Semester 1 2025

  • Breakdown of topics by week, including:

    • Week 0: Orientation Week

    • Week 1: Data collection and analysis

    • Weeks 2-12: Various topics including Excel basics, research study design, data analysis, project planning, and hypothesis assessment

    • Important dates noted for assessments and project submissions

Page 8: Assessments

  • Continuous practical assessment: 5 submitted worksheets worth 8% each

    • Due by 11:59pm on the Sunday after each lab

  • Research project: Group work (3 or 4 students) requiring problem framing and data collection

    • Assessments based on project plan (5%) and final report (35%)

  • In-class practical test (20%) in Week 12

Page 9: Importance of Statistics in Science

  • Statistics as a research tool:

    • Helps discover new things and prevent mistakes

    • Insight: 70% of graduates wish they had learned more statistics

  • Related fields include: Biometrics, Analytics, Data Science, Machine Learning

Page 10: Unit Objectives

  • Introduction to statistics for data thinking: production, visualization, analysis, and inference

  • By the end of the course:

    • Grounding for advanced units

    • Statistical literacy in evaluating claims in media

Page 11: Example of Statistical Discovery

  • Discovery of 25 genes causing 7 common diseases

  • Importance of statistical methodologies in research conclusions

Page 12-15: Case Study on Diseases and Genes

  • Analysis of common diseases:

    • Bipolar Disorder

    • Coronary Heart Disease

    • Hypertension

    • Type 1 & 2 Diabetes

    • Rheumatoid Arthritis

    • Crohn's Disease

  • Study methodology and statistical findings presented

Page 16-19: Data and Statistical Importance

  • Statistics as the science of drawing conclusions from data

  • Historical context given on various discoveries and mistakes in data interpretation

  • Examples include:

    • Universe expansion by Edwin Hubble

    • Sun exposure and cancer correlation

    • Misunderstanding of brain cell loss

Page 20-24: Use and Misuse of Data

  • Highlights famous mistakes made in data interpretation

  • Importance of sound methodology in developing drugs and research

  • Importance of data analysis for informed decision-making

Page 25-29: Descriptive Statistics

  • Role in summarizing and organizing data through:

    • Graphs and Tables

  • Case studies:

    • Pizza topping preferences

    • Rainfall data analysis

Page 30-32: Carnaby Cockatoo Case Study

  • Data related to Carnaby's Black Cockatoo populations

  • Display of statistical results:

    • Mean, Median, Mode, Standard Deviation

    • Frequency distribution

Page 33-37: Inferential Statistics

  • Utilizing sample data to make inferences about larger populations

  • Importance of context and correct statistical tools

  • Data inference principles highlighted

Page 38-40: Research Process

  • Steps in conducting scientific research, including:

    • Defining question/hypothesis

    • Data collection and ethical consideration

  • Importance of clearly defining populations during research

Page 41: Key Terms

  • Definitions of respective data types and observational units:

    • Continuous, Discrete, Nominal, Ordinal

  • Classifications in statistical research using practical examples

Page 42-46: Data Classification Types

  • Different scales of data (ratio, interval, etc.)

  • Classification of data as cross-sectional or time-series

  • Bivariate vs multivariate data

Page 47-50: Project Ideas and Examples

  • Examples of potential project topics related to environmental awareness and behavioral studies among students

Page 51: Workshop Conclusion

  • Closure of Workshop 1 and preview of Workshop 2: "Data Exploration and Description"

  • Emphasis on understanding the value of statistics in making sense of data.