Learn How to Think With Data
Data is everywhere, but having access to data does not automatically mean we understand the problems around us. Learn to ask meaningful questions, investigate information, identify patterns, evaluate evidence, and communicate findings.
- Structured learning pathway
- Practical industry projects
- Study Buddies and collaborative learning
- Portfolio development
Go Beyond Data, Learn to Understand Problems.
Organizations, researchers, governments, and businesses generate and collect large amounts of data. However, the real challenge is understanding what that information means.
Why are customers leaving? What factors influence crop production? What explains changes in patient waiting times? Which patterns exist in public service delivery?
These are not questions that can be answered by looking at numbers alone. They require structured thinking, appropriate analytical methods, and careful interpretation.
Data Science helps us approach these questions systematically.
Define problems
Understand the problem before attempting to analyze the data.
Ask meaningful questions
Translate broad problems into specific questions that can be investigated using data.
Work with data
Understand how data is collected, structured, prepared, and made suitable for analysis.
Explore patterns
Use exploratory data analysis to investigate distributions, trends, relationships, and unusual observations.
Apply statistical thinking
Use statistical concepts to understand variation, relationships, uncertainty, and evidence.
Communicate findings
Present evidence clearly so that other people can understand the results and use them to make informed decisions.
What will you learn to do?
Define problems
Understand the problem before attempting to analyze the data.
Ask meaningful questions
Translate broad problems into specific questions that can be investigated using data.
Work with data
Understand how data is collected, structured, prepared, and made suitable for analysis.
Explore patterns
Use exploratory data analysis to investigate distributions, trends, relationships, and unusual observations.
Apply statistical thinking
Use statistical concepts to understand variation, relationships, uncertainty, and evidence.
Build analytical explanations
Interpret findings and explain what the data reveals about the problem.
Introduce predictive modeling
Understand when machine learning may be useful and how to approach introductory predictive tasks.
Communicate findings
Present evidence clearly so that other people can understand the results and use them to make informed decisions.
Investigate Real Problems, Generate Evidence & Communicate Findings.
You will not spend the entire learning period memorizing definitions, statistical formulas, or programming techniques. You will work through practical scenarios that require you to think critically, investigate information, and explain what your findings mean.
- 01What problem are we trying to understand?
- 02Why does this problem matter?
- 03What questions should we investigate?
- 04What data do we need?
- 05Where can we obtain the data?
- 06Is the available data reliable and suitable?
- 07What needs to be cleaned or transformed?
- 08What patterns can we identify?
- 09Which statistical or analytical methods are appropriate?
- 10What evidence supports our conclusions?
- 11What limitations should we acknowledge?
- 12How can we communicate our findings?
Build analyses that answer real questions.
You will work on practical projects designed to help you understand how Data Science is applied across industries.
The objective is not just to produce charts. It is to build evidence-based explanations that answer meaningful questions.
HealthcareHealth Outcomes Analysis
Analyze public health datasets to surface patterns between clinic access and patient outcomes.
TelecommunicationsCustomer Churn Investigation
Identify factors associated with customer churn using exploratory analysis and statistical methods.
EducationEducation Outcomes Study
Investigate relationships between attendance, resources, and learning outcomes.
From business problems to evidence-based findings.
Understand Data Science fundamentals.
Define problems and formulate analytical questions.
Work with datasets and understand structure.
Clean and explore data.
Apply introductory statistical concepts.
Investigate relationships between variables.
Formulate hypotheses and interpret statistical tests.
Introduce predictive modeling where appropriate.
Evaluate analytical approaches and acknowledge limitations.
Communicate findings through data storytelling.
Complete and document an end-to-end investigation.
Present evidence-based recommendations to industry panelists.
Learn Independently, Collaborate & Build Together.
The Data Science Study Group uses a three-semester learning structure to develop your technical competence, analytical thinking, collaboration skills, and ability to work through practical problems.
Individual Learning
Build your analytical foundation.
- Understanding Data Science fundamentals
- Defining problems and asking analytical questions
- Working with datasets
- Cleaning and exploring data
- Applying introductory statistical concepts
- Completing individual exercises and assessments
TeamBud Collaboration
Investigate data problems as a team.
- Collaborative data investigation
- Exploratory data analysis
- Statistical reasoning
- Data visualization and interpretation
- Reviewing analytical approaches
- Documenting team findings
PairBud Collaboration
Work through the Data Science process as a pair.
- End-to-end data science projects
- Collaborative problem definition
- Data preparation and analysis
- Interpreting findings
- Introductory modeling where appropriate
- Project documentation and presentation
Reflect, Ask Questions, and Improve Your Thinking.
The weekly review provides an opportunity to connect with Coach Timothy, coaches, and other Study Buddies.
Come with your PC, your questions, your practical work, and the concepts you found challenging.
What did we learn during the week?
What confused learners during the practical activities?
What questions came up, and what different approaches can we consider?
Who investigated an interesting problem or developed a useful solution?
What did you struggle with, and what did you discover?
Which concepts need more explanation, practice, or revision?
What are we learning and working on next?
Explore the Data Science Curriculum.
The curriculum is arranged progressively, taking you from understanding the discipline and defining problems to exploring data, applying statistical techniques, introducing modeling, and completing end-to-end projects.
- Definition of Data Science
- Why Data Science matters
- Data Science in everyday life
- Data Science vs Data Analytics
- Data Science vs Business Intelligence
- Data Science vs Machine Learning
- The role of statistics and programming
- Applications across industries
- Introduction to the Data Science process
Explain what Data Science is, understand its applications, and describe how it supports problem-solving and decision-making.
What You Should Be Able to Do
The goal is not simply to complete the curriculum or learn a collection of analytical techniques. It is to develop the ability to approach unfamiliar problems, ask the right questions, investigate available evidence, and communicate what the data reveals.
Ready to Learn How to Think With Data?
Develop the skills to investigate problems, understand information, generate evidence, and communicate meaningful findings.
Whether you are beginning your data journey or strengthening your analytical foundation, the Data Science Study Group provides a structured environment to learn, practise, collaborate, and grow.
Ask better questions. Understand the evidence. Think critically. Make data useful.