Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 2-Day Livestream Seminar Taught by Edwin Dalmaijer, Ph.D.

Python is a general-purpose programming language. It is open-source, powerful, and easy to use. Because of this, Python is one of the most popular languages in the world, and it has become indispensable in data science.

In this course, we will cover two main classes of analytical approaches that aim to uncover what makes up a dataset by identifying separable dimensions or subgroups. These are unsupervised machine-learning techniques: you simply give them your data, and they will carve it up without requiring further input from you.

In the first half of this course, we will cover dimensional analyses. These include principal component analysis, exploratory factor analysis, and independent component analysis. Their objectives are to find the set of (latent) components that gave rise to patterns across variables in your dataset.

In the second half, we will cover subgroup analyses. These are used to find distinct clusters of individuals, and include k-means, c-means, and latent class analysis.

The above will be implemented in scikit-learn, a Python package for machine learning. It is a powerful tool for data science, and its common interface will allow you to extend what you learn in this course to other models.