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Dimensionality Reduction and Factor Analysis with Python - 1-day tutor-led online course

Date:

19/11/2021

Organised by:

Mind Project Ltd

Presenter:

Simon Walkowiak MSc, MBPsS

Level:

Intermediate (some prior knowledge)

Contact:

Simon Walkowiak
Phone: 02033223786
Email: info@mindproject.co.uk

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Venue: Online

Description:

1. Course description.

During this 1-day instructor-led online training course, you will learn the theory and practical implementations of typical and less common dimensionality reduction methods as well as procedures used in Factor Analysis. More specifically, you will acquire skills in: 

  • understanding the multi-dimensional structure of different datasets and applying statistical tests to identify meaningful features and components, 
  • conducting the linear and non-linear (i.e. kernel) Principal Component Analysis, 
  • applying Manifold Learning approaches such as Multi-Dimensional Scaling, Isomaps, Modified and Standard Local Linear Embedding to reduce the dimensionality of data, evaluating and comparing solutions from different Manifold Learning methods,
  • visualising and evaluating components identified with linear and non-linear methods,
  • comparing the effects of components extracted with various dimensionality reduction approaches by using them as predictors in a simple linear (i.e. logistic regression) classifier,
  • performing both Exploratory and Confirmatory Factor Analysis, implementing orthogonal and oblique rotations of factors, understanding extracted factors and interpreting their loadings. 

All data analysis methods will be implemented in Python programming language either through custom-made code or with functions and methods available in NumPy, pandas, SciPy, Scikit-Learn, Statsmodels, pca, and FactorAnalyzer libraries for Python. 

 

2. Course programme.

This is a 1-day instructor-led online training course with a week-long follow up period. The course will run from 10:00 in the morning to ~15:30 and will include a 45-minute break for lunch between morning and afternoon sessions. Following the course, you will be able to submit your solutions to the homework exercise and you will receive feedback from the tutor. 

This training course is tutor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners. You can also email the tutor after the course if you have any questions related to the material presented during the course. 

The course will be recorded - you will have access to the video recording of the course and additional resources such as datasets, Python code, academic papers related to the topic of the workshop, and supplementary exercises via Mind Project Learning Platform. 

Course dates: Friday, 19th of November 2021, 10:00-15:30 London (UK) time

Deadline for registrations: Wednesday, 17th of November 2021 @ 17:00 London (UK) time

 

10:00 - Course welcome and introduction

10:15 - Dimensionality reduction - presentation (theory and examples)

10:35 - Dimensionality reduction with linear PCA and non-linear kernel PCA - Python tutorial

12:30 - 13:15 - lunch break

13:15 - Introduction to Manifold Learning for high-dimensional data (theory and examples)

13:30 - Manifold Learning - Python tutorial

14:15 - Exploratory and Confirmatory Factor Analysis - Python tutorial

15:15 - 15:30 - discussion and course wrap-up

 

3. Course pre-requisites and further instructions

  • We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 3.8 installed on their PCs (any operating system). Anaconda’s Python is a free and fully-supported distribution and you can download it directly from https://www.anaconda.com/products/individual#Downloads. Please contact us should you have any questions or issues with the installation process. A list of Python libraries to pre-install before the course will be sent to the enrolled attendees in the Welcome Pack alongside other Joining Instructions.
  • We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial.
  • Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our Mind Project Learning Platform.

 

4. Your course instructor.

Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with the ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse etc.), research and academia (GSMA, CERN, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).

 

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/dimensionality-reduction-and-factor-analysis-python-nov21/.

Cost:

£120 per person for the whole course (regular fee) or £75 per person - applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee). Additional discounts available for multiple bookings and groups.

Website and registration:

Region:

Greater London

Keywords:

Quantitative Data Handling and Data Analysis, Variance estimation, Latent Variable Models, Principal components analysis, Factor analysis, Confirmatory factor analysis, Data Mining, Python

Related publications and presentations:

Quantitative Data Handling and Data Analysis
Variance estimation
Latent Variable Models
Principal components analysis
Factor analysis
Confirmatory factor analysis
Data Mining
Python

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