Clustering Algorithms in Practice with Python - 1-day tutor-led online course

Organised by

Mind Project Ltd


Simon Walkowiak MSc, MBPsS




86-90 Paul Street


View in Google Maps  (EC2A 4NE)


Simon Walkowiak
Phone: 02033223786


1. Course description.

This hands-on 1-day tutor-led online training course comprehensively covers standard and more advanced clustering algorithms and their implementations in Python programming language. Through a series of practical tutorials, the course demonstrates how to apply and evaluate clustering methods in academic, industrial and business settings e.g. in social science, genetics, customer/product segmentation and recommendation systems. During this course you will:

  • Learn how to implement k-means, hierarchical clustering, affinity propagation, mean shift, DBSCAN and spectral clustering algorithms to identify meaningful clusters in various datasets, 
  • Understand data processing requirements, computational efficiency and specific use cases for each method,
  • Implement more complex variants of typical methods by optimising their hyperparameters e.g. using different distance metrics, cluster linkage approaches and underlying mathematical algorithms,
  • Evaluate and compare the clustering solutions returned by various methods and their variants through a number of metrics such Davies-Bouldin and Calinski-Harabasz scores, Rand Index or Mutual Information estimates,
  • Visualise the obtained clusters in 2D and 3D plots, calculate profiling attributes for each cluster and interpret them. 

All clustering algorithms presented during this course will be implemented in Python programming language either through custom-made code or with functions and methods available in NumPy, pandas, SciPy, Scikit-Learn, and Statsmodels 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, 26th of November 2021, 10:00-15:30 London (UK) time

Deadline for registrations: Wednesday, 24th of November 2021 @ 17:00 London (UK) time


10:00 - Course welcome and logistics

10:15 - Introduction to clustering methods (theory and examples)

10:45 - K-means clustering in practice - Python tutorial

11:30 - Hierarchical clustering in practice - Python tutorial

12:30 - 13:15 - lunch break

13:15 - Evaluation metrics for clustering solutions - Python tutorial

13:45 - Other clustering approaches: Affinity Propagation, Mean Shift, DBSCAN, Spectral Clustering - 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 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 or by phone on 0203 322 3786. Please visit the course website at


Intermediate (some prior knowledge)


£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


Greater London


Latent Variable Models, Principal components analysis, Cluster analysis, Data Mining, Machine learning, Python

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