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 4-Day Livestream Seminar Taught by Jason Anastasopoulos, Ph.D.

Python is a premier language for modern data science and data analysis. It is a free, open-source language that has a simple, easy-to-understand syntax and an incredible range of data analysis and visualization libraries. In four days, this seminar provides a comprehensive introduction to Python. The goal is to get participants to fully understand many of the basic elements of Python and immediately apply them to practical data analysis and data collection problems.

Python is rapidly becoming the preferred language of data scientists in both industry and academia. It’s used by Google, Facebook and other tech giants to perform data analysis and run machine learning algorithms that can handle hundreds of thousands of terabytes of data per day.

Python can be used for:

• Storing and analyzing large and small datasets.
• Web scraping and data collection using APIs.
• Beautiful data visualization.
• Natural language processing and text analysis.
• General machine learning.
• Deep learning.
• Image analysis and much, much more…

How you will benefit from this seminar:

This seminar is a foundational course in Python. The goal is to get participants to fully understand many of the basic elements of Python and immediately apply them to practical data analysis problems.

By the end of this seminar you will be able to:

• Program using Python (Jupyter) notebooks and IDEs.
• Understand and use basic data analysis and visualization libraries such as NumPy, Pandas, Matplotlib, Seaborn and statsmodels, among others.
• Use basic data structures needed to do data analysis: variables, lists, loops, dictionaries, Boolean operators, functions.
• Perform data analysis and basic statistical inference: GLMs, ANOVA, hypothesis testing.
• Produce beautiful data visualizations.