Statistical Modelling in Python

Description:

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.

Cost:

£427.20 to £592.80 (inc. VAT)

Website and registration:

https://rss.org.uk/training-events/training/public-courses/software-training/statistical-modelling-in-python/statistical-modelling-in-python-virtual-classroom/

Region:

Greater London

Keywords:

Quantitative Data Handling and Data Analysis, Python

Related publications and presentations:

Quantitative Data Handling and Data Analysis
Python

Back to archive...