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.