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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, and introduces the tools and techniques to help them do that analysis.

Course Length

1 day

Knowledge Pre-requisites

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

Course Content

The course is mainly instructor-led guided lab exercises and discussion. For one or two exercises 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.

An introduction to the data analysis capabilities of popular software tools

Attendees practice using the some of the popular tools for data analysis, including Excel, Power BI Desktop, Python, and the the data analysis capabilities of generative AI tools such as ChatGPT.

This may includes a brief look at some of the advanced 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

Understanding tabular data

The course covers why we must clean and shape our data into a tabular (and tidy) format before we can analyse it successfully. It dcovers characteristics of tabular data such as:

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

Techniques for analysing data

The instructor will demo a set of techniques for cleaning, shaping and analysis data these and attendees will practice in lab exercises.

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

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
• a more theoretical discussion of non-tabular data (e.g., text, image, json) and how to analyse these
• 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)