Critical Evaluation in Data Science: Data, the World, and You

Overview

Learn how data analytics affects everyone with the University of Adelaide. In an increasingly data-centric world, a working understanding of data analytics and quantitative methods is essential for all members of society.

This six-week course will teach you fundamental quantitative methods for dealing with data. Armed with this knowledge, you’ll be able to interpret and critically evaluate claims you encounter in your day-to-day life.

Through case studies, you’ll explore the foundational concepts in data science and statistical thinking. You’ll also discover machine learning and data science methods, and understand both the possibilities and pitfalls of these emerging sciences.

Learn how to identify misleading statistics When presented with claims in the media that are accompanied by statistics, diagrams, and outputs from technologies like AI and machine learning, how can we learn to not be fooled by these claims? This course will help you understand misleading statistics and learn how to challenge them when they are presented to you.

Delve into sample, variability, and study design Next, you’ll explore the different components of research including research questions, sampling, and variability. Using case studies, you’ll explore how sampling can go wrong as you learn the best practices.

You’ll then move on to study design to understand the types of studies you may encounter. Unpack the rules for analysing data On the final weeks of the course, you’ll explore how bias can infiltrate surveys.

You’ll also discover basic rules for analysing data to ensure a fair result is achieved. Armed with this knowledge, you’ll have the confidence to accurately read and interpret the data you are presented with in everyday life.

Syllabus

• **Week 1: Introduction to Data Science**
Overview of data-centric world
Importance of data literacy for everyone
Explore foundational concepts in data science
• **Week 2: Fundamentals of Quantitative Methods**
Basics of statistical thinking
Introduction to quantitative analysis
Case studies: Real-world applications
• **Week 3: Identifying Misleading Statistics**
Common types of misleading statistics
Tools and techniques for critical evaluation
Interpreting data from media and AI outputs
• **Week 4: Sampling and Variability**
Understanding sampling methods
Exploring variability in data
Case studies: Sampling pitfalls and best practices
• **Week 5: Study Design and Analysis**
Types of research studies
Designing reliable studies
Analyzing different study designs
• **Week 6: Data Analysis and Bias**
Recognizing and mitigating bias in data collection
Basic rules for data analysis
Ensuring fair interpretation of data
• **Conclusion: Applying Critical Evaluation Skills**
Real-world applications and exercises
Developing confidence in interpreting data
Strategies for ongoing learning and development in data analysis

---

Subjects

Data Science