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Data and Statistics

An overview of topics related to data and statistics.

Before You Analyse Your Data

If you are working with raw data (your own or someone else's) the data set typically requires cleaning and preparation before any analysis. This includes activities such as

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

This 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:
Online Books  |  Streaming Videos

View a list of videos on data cleaning

The same methods may also be used for data analysis, reporting and visualizations.  However, there are other easier-to-use software that can help you with analysis and reporting.  See the next section "Data Reporting / Visualization" for more information and check the ITS Software page for any statistical analysis software that may be available to you in your program.

Where to Learn About Data Analysis


Search the Seneca Libraries for books, videos and articles on specific data analysis topics:

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