Clustering & Customer Segmentation with Python - 2-Day Live Training Course

Date:

10/10/2022 - 11/10/2022

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

Venue: Online

Description:

1. Course description.

This hands-on 2-day, instructor-led, live, online training course comprehensively covers industry-standard and less common, more specialised clustering algorithms and customer segmentation methods along with their computational implementations in Python programming language. It also serves as an in-depth introduction to most recent and cutting-edge Big Data cluster analysis tools and methods which provide greater computational efficiency and scalability compared to typical clustering methods. Through a series of coding tutorials, brief lectures and short practical exercises, the course demonstrates how to apply, optimise, visualise and evaluate clustering methods in academic, industrial and business settings e.g. in social science, biological and medical sciences, genetics, customer/product segmentation and recommendation systems. 

All clustering algorithms presented during this course will be implemented in Python programming language either through the custom-made code or with functions and methods available in Python libraries e.g. NumPy, pandas, SciPy, Scikit-Learn, and Statsmodels.

More details on this training course and registration available at: https://www.mindproject.io/product/clustering-and-customer-segmentation-with-python-october-2022/.

2. Who is this course for?  

This instructor-led, live, online, short course is suitable for post-doctoral researchers, Master's or PhD students, industry data analysts or enterprise data/ML scientists and ML engineers, who are currently using the Python programming language (preferably at intermediate level) and would like to expand their skills to include theoretical understanding and practical implementations of industry-standard and modern, cutting-edge, scalable clustering and customer segmentation algorithms in Python.

3. Programme outline. 

This is a 2-day instructor-led online training course with a week-long follow up period. The course will run from 10:00 in the morning to ~16:30 in the afternoon (London, UK time) each day 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 practical exercises for which you will receive personal feedback from the tutor. 

This training course is instructor-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 recordings 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: Monday & Tuesday, 10th & 11th of October  2022, 10:00-16:30 London (UK) time

Deadline for registrations: Friday, 7th of October 2022 @ 17:00 London (UK) time

During this instructor-led live course you will:

• Implement industry standard partitional, linkage-based, density-based or spectral clustering and customer segmentation methods such as k-means and hierarchical clustering approaches, as well as less common, but more specialised algorithms such as clara, affinity propagation, mean shift, DBSCAN, optics, minimum spanning trees and spectral clustering to identify meaningful clusters in datasets from social science, biology/medical science/genetics, and business/finance fields, 
• Learn about recent, cutting-edge cluster analysis techniques applicable to Big Data e.g. parallel implementations of density-based lightning connection clustering (LAPO-DBSCAN) and improved k-means (Wang, 2022) which rectify typical issues of common clustering approaches,
• Understand data processing requirements, computational efficiency and specific use cases for each presented method,
• Implement more complex and better optimised variants of typical methods by selecting 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, Dunn's validation index, Silhouette Index, Jaccard Index, (Adjusted) Rand Index, Fowlkes-Mallows Index or (Adjusted) Mutual Information estimates,
• Visualise the obtained clusters in 2D and 3D plots, calculate profiling attributes for each cluster and interpret them,
• Discuss advanced methods of learning deep representations by reviewing Deep Learning clustering approaches of different types e.g. sequential multistep (e.g. Fast Spectral and Deep Sparse Subspace Clustering), joint (e.g. Task-Specific and Graph-Regularised Networks and Deep Clustering Networks) or closed-loop multistep (e.g. Deep Embedding Clustering - DEC) methods.

4. Further instructions.

• We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 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. You may also use any other Python IDE of your choice and/or your own Python virtual environment. A list of Python libraries to pre-install before the course will be sent to the enrolled attendees in the Welcome Pack alongside other course Joining Instructions.

• We recommend that the attendees have practical experience in data processing/engineering or quantitative research with Python programming language – gathered from either professional work or university education/research. A good knowledge of statistics or experience with ML techniques would be beneficial. We suggest that the course is preceded with our “Python for Data Analysis” instructor-led six-week online training course.

• 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.

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/clustering-and-customer-segmentation-with-python-october-2022/.

Cost:

By 16th of September 2022 (Early Bird offer): £450 (normally £600) per person for the whole course (regular fee). £330 (normally £450) per person for the whole course 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:

https://www.mindproject.io/product/clustering-and-customer-segmentation-with-python-october-2022/

Region:

Greater London

Keywords:

Cluster analysis, Data Mining, Machine learning, ICT and Software, Quantitative Software, Python, clustering algorithms, customer segmentation, hierarchical clustering, deep clustering

Related publications and presentations:

Cluster analysis
Data Mining
Machine learning
ICT and Software
Quantitative Software
Python

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