Analytic and Statistical Techniques

In-person teaching Classroom
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The Analytic and Statistical Techniques course covers how data is prepared and some simple statistical analysis in R and Python.

Scatterplots, correlation and regression are also included within the course.


This course is for employees who collect, organise and study data to provide business insights.

Learning Outcomes

At the end of the course participants will know:

  • Processing and analysing data
  • Routine data analysis steps
  • Data preparation and simple statistical analysis in R
  • Data preparation and simple statistical analysis in Python
  • Scatterplots, correlation and regression
  • Linear regression and multiple linear regression in R
  • Linear regression and multiple linear regression in Python
  • Time series modelling

Course Content

  • The routine steps of data analysis
  • Creating a problem hypothesis
  • Identifying what to measure
  • Collecting data
  • Understanding the size, nature and content of the data
  • Data loading
  • Data cleansing
  • Modelling data
  • Data preparation
  • Model definition
  • Troubleshooting
  • Validation testing
  • Improving model performance
  • Visualising data
  • Analysing data
  • Interpreting results
  • Documenting and communication results
  • Capabilities and functions of statistical programming languages
  • R, Python and SQL
  • Non-relational databases
  • Software tools
  • Preparation techniques
  • Missing data
  • Inconsistencies
  • Redundancy


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