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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

What is data and how can we analyse it?

A discussion of the many aspects of data and approaches to analysis.

Understanding tabular data

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

Data quality and data cleaning

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

Advanced data analysis features of 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 sections

The course may included these if time allows and there is interest.

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)

Use AI tools analyse data

We use AI tools such as ChatGPT to help analyse data.