Data and Statistics

Before You Analyse Your Data

Raw data (your own or someone else's) requires cleaning and preparation before analysis. These are tasks such as

• fixing errors
• reformatting or adding calculated fields
• standardizing / normalizing data values
• enriching the existing data with data from other (related) sets

This part is one of the most important steps in your data analysis: Ensuring that your data is prepared will improve the quality of the data and allow you to draw more reliable and valid conclusions.

While you are able to manually review and clean data for small amounts of data, there are tools and methods that can help you automate some of the data cleaning steps. Programming languages such as JavaScript, Python, SAS, and R can help set up data cleaning routines using your own programs, or use existing functions and code libraries shared through Github and other resources.

Materials available from the library on data cleaning:

Books or Articles  |  Streaming Videos

While you may use the same methods for analysing, reporting and visualising data, there are other easier-to-use software available to report and summarise your data.  See the next section "Data Reporting / Visualization" and check the ITS MyApps page for any statistical analysis software that may be available to you.