The purpose of this course is to introduce participants to the Python for statistical computing. The course focuses on working with and visualising data in Python, and linear regression modelling in Python using relevant packages.
 
Learning outcomes
 
By the end of the course, delegates will:

• Understand logical and relational data partitioning.
• Have a thorough understanding of popular statistical techniques.
• Have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in Python
• Be able to fit regression models in Python between a response variable and understand how to apply these techniques to their own data using Python
• Be able to cluster data using standard clustering techniques.

 
Topics Covered
 

• Summary statistics: Measures of location and spread.
• Basic hypothesis testing: Examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test, two-sample t-test for paired samples, Wilcoxon signed-rank test.
• ANOVA tables: One-way and two-way tables.
• Simple and multiple linear regression: Including model diagnostics.
• Clustering: Hierarchical clustering, k-means.
• Principal components analysis: Plotting and scaling data.

 
Target Audience
 
Learners who wish to be able to apply some key statistical concepts using the language of Python
 
Knowledge Assumed
The course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing.
Delegates are expected to bring a laptop. Instructions will be provided to help them install the required Python software.

Rob Mastrodomenico

Rob studied a BSc Mathematics and Statistics at the University of Reading. Thereafter, having particularly enjoyed the Statistics side of his degree, he stayed on at Reading to study for a Statistics PhD. Following the completion of his studies worked as a Quantitative Analyst at a statistical consultancy which undertook statistical research and provided sports modeling services in the betting sector.

In 2011 he started up his own company called Global Sports Statistics and has continued working in the sports modeling sector. His interest in Python really began back in 2010 when he was looking at alternatives to R and since then it has become his language of choice.

Rob conducts Python training courses for the Royal Statistical Society and has seen his Introduction to Python Course feature on Sages online training.

Fees		Registration before   15 April 2024	Registration on/after 15 April 2024
Non Member  RSS Fellow  RSS CStat/Gradstat/Data Analyst   also MIS & FIS		£445.00+vat  £377.00+vat  £356.00+vat	£494.00+vat  £420.00+vat  £396.00+vat
Group discounts are also available*: 3-5 people 6-8 people 9+ people *Discount only applies to non-member price		10% discount 15% discount 20% discount