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Analysing Data Course Outline

Who should attend

This is for anybody who would like to be able to better interpret and analyse datasets.

Learning Objectives

The course teaches students how to explore, analyse data and to ask the right questions that lead to better insights.

Course Length

1 day

Knowledge Pre-requisites

No prior experience in data analysis is required, but basic familiarity with Excel is assumed. This is not an Excel course per-se, but we will use Excel in all the hands-on lab exercises.

Course Content

Attendees work in small groups of 4 to 5 people in guided discussions and exercises for much of the course. A spokesperson for the team then reports back to the entire class on the team’s results or conclusions.

Describing the dataset used by attendees

Attendees consider, describe and discuss the datasets that they use regularly.

What is data and how can we analyse it?

A discussion of the many aspects of data and approaches to analysis. We use a practical definition of data: something we can review or analyse to provide insight, inform actions, improve decisions and outcomes and look at how some candidate datasets measure up to this definition.

Understanding tabular data

• tables rows and columns
• categorical, numerical and ordinal types
• column data types: dates, strings, numbers

A tour of data analysis tools

A quick look and comparison of the some of the popular tools for data analysis, including Excel, Power BI, SQL, Python the data analysis capabilities of generative AI tools such as ChatGPT

Advanced data analysis features of Excel

A very brief look at some of the data analysis capabilities within Excel. These include:

• data cleaning and shaping in Excel’s Query Editor
• using Python in Excel
• using Copilot, Microsoft’s generative AI tools embedded into Excel
• Excel’s data tables and use in sensitivity analysis

Recap and Resources

We will summarise the lessons learned during the day and suggest a few resources for attendees who would like to learn more about data analysis.

Optional modules

We will cover these if there is time and interest from attendees.

Data quality and data cleaning

• Aspects of data quality: incomplete data, missing values, incorrect data, irrelevant data, outliers
• Approaches to fixing bad data.

An introduction to descriptive statistics

Descriptive statistics help us explain and summarise our data.

• measures of central tendency (mean, median, mode)
• measures of dispersion (minimum, maximum, range, standard deviation)