Data analysis: visualisations in Excel

By Ardit Demaj Categories: Data analysis
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About Course

Learning outcomes

After studying this course, you should be able to:

  • explore the functionalities of Excel that are used for problem solving in a business context
  • demonstrate the numeracy skills required for gathering and organising data for decision making related to a specific problem
  • use graphical techniques (histograms and scatter diagrams) to provide a visual summary of available data
  • recognise data presentation and communication techniques used in a range of traditional and electronic media
  • describe the relationship between two variables (independent and dependent variables).

What Will You Learn?

  • By the end of this course, you will be able to confidently analyze and visualize data using Microsoft Excel. Specifically, you will learn how to:
  • Import, organize, and structure datasets in Excel for analysis
  • Use essential Excel functions to clean and prepare data
  • Identify relationships between variables using basic analytical techniques
  • Create a variety of data visualizations such as charts, graphs, and tables
  • Choose the right type of visualization for different datasets and insights
  • Interpret visual data to make informed conclusions and decisions
  • Present data clearly and professionally for reports or presentations
  • Build a foundation for more advanced data analysis skills

Course Content

1 Excel spreadsheets: a tool for organising and starting to process data
Before making any business decision, managers need to see a clear picture of their data, which provides them with all the relevant information. Therefore, it is good practice to organise and present data in a way that makes it useful for decision making and problem solving. Microsoft Excel is the most attractive and useful tool for data analysis. Researchers and analysts alike use this tool for various applications in the real world, such as in business, medicine, academia, logistics, operations, transportation, tax and auditing, marketing, accounting and finance. Moreover, it is flexible enough to be used with all types of data, irrespective of whether the data is qualitative or quantitative. In this section, you will make extensive use of Excel spreadsheets. Therefore, your task is to learn and familiarise yourself with the basics of using Excel. This will enhance your analytical skills as well as your employability skills. This section briefly explains the various features and functions of Excel that are used by researchers and data analysts to explore, organise and analyse data.

  • 1.1 Accessing Microsoft Excel
  • 1.2 Opening an Excel file
  • 1.3 Adding the Data Analysis ToolPak in Excel
  • 1.4 Decimal points and dates
  • 1.5 Using shortcut keys in Excel
  • 1.6 Use of Excel spreadsheets

2 Univariate data visualisation
In practice, there are two ways to visualise data in Excel. These are: tabular form graphical form. While presenting data in Excel, it is important to know the features of data. If your data is univariate (that is, the data consists of many observations for only one variable) then you can either use a frequency table or a histogram to present the data and get an idea of its features. In the JC Electrics example, if the analysis is only carried out for ‘Generators’ (therefore only for one variable), this will be seen as a univariate analysis. However, if your data is bivariate (that is, the data consists of two variables (an independent variable and a dependent variable)) and you need to know the relationship between these two variables, then, for example, you can use either a contingency table or scatter diagram to present the data and get an idea of its structure. You will learn about bivariate data visualisation later in this session. The next section will briefly explain frequency tables.

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