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Analysing Data Course

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

This is a course that teaches how to analyse data. Firstly attendees consider, describe and discuss the datasets that they use regularly.

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.

Much data is organised in a tabular format (rows and columns). One form of tabular data is called tidy data and is the most useful format for analysis and visualisation. We discuss the characteristics of tabular and tidy data and how clean data so that it is firstly tabular then also tidy.

We do a quick review of the tools for data analysis, for example: Excel, Power BI, Python, SQL, and the data analysis capabilities of generative AI tools such as ChatGPT.

The course has several optional modules that we may cover depending on attendee interest

• how does survey data differ from typical data and how to analyse survey data
• approaches to improve data quality
• an overview of descriptive statistics

Here is the course outline.

Please follow the joining instructions well before the course.

In the lab exercises, we explore many datasets from the course datasets landing page. Keep this link handy.

A note on Excel and other data analysis tools used in the course

The course uses Excel in the hands-on lab exercises. This is not an Excel course per-se. There are many other good tools to analyses data: for example, Python, SQL, Power BI, and R. This is for practical purposes. Nearly all attendees have at least basic familiarity with Excel but possible not with some of the other tools mentioned.

Excel also offers three separate and usefully distinct tools for data analysis. These are:

• the Excel formula language
• Power Query, that offers and user interface and cleaning and shaping data in a repeatable fashion
• Python, the de-facto language for data analysis, data science and AI. (This is available in Excel’s current channel although not all organisations have yet deployed this version so may not be available to all attendees)
• an embedded generative AI model, Copilot, which can help us with our data analysis. It can also help us to use Python - for example to build charts better and faster than using the standard charting options.

Course Structure

Initial attendee data analysis needs exercise

What is data? How do we analyse it?

Tabular Data discussion

Tidy Data discussion

Tabular and Tidy Data exercise

Tools for data analysis

Optional Topics

Improving Data Quality: discussion and exercise

Survey Data

Descriptive Statistics and descriptive statistics exercise

Predictive Analysis and predictive analysis exercise

Performance Analysis & Reporting

Appendix

Data Quality Approaches

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