Course Catalog
At Teb’s Lab we believe in foundational education. Our focus is on core programming and computer science with a bend towards analytics, data science, and machine learning. Classes prioritize hands-on education targeting fundamental skills that set students up for autonomous research and experimentation.
Some courses for which we do have curriculum do not (yet) appear in this listing. If you don’t see a class here that fits your needs consider perusing our open source curricula on Github or requesting a bespoke course consultation.
Introduction to Python
This class introduces the fundamentals of programming in Python, one of the world's most popular programming language. Join the millions of programmers who are using Python to build web servers, machine learning models, data pipelines, or perform data analysis.
Introduction to SQL
This class introduces Standard Query Language (SQL). SQL is the most widely used programming language for accessing databases. Once relegated exclusively to use by developers, the language has proliferated into the realm of data analysis, business intelligence, and beyond. Modern tools such as Salesforce, Tableau, Power BI, and other data visualization tools now have interfaces for writing and executing SQL queries
Introduction to Machine Learning
Learn the fundamentals of today’s hottest category of software: Machine Learning. In this class you’ll learn what distinguishes machine learning from other types of artificial intelligence; how to build and train machine learning models using popular libraries such as Scikit Learn and Tensorflow; how to manage and manipulate datasets using Pandas; and how to manage and mitigate common sources of error and failure in ML systems.
Machine Learning Overview for Non-Engineers
This workshop is designed to give a high level overview of Machine Learning for an audience of people who are not software engineers. This half-day course includes a discussion of critical terminology, the fundamentals of how ML models are trained and produced, and common sources of failure and error in ML modeling.
Intro to Deep Learning
In this class you’ll learn about machine learning’s most flexible family of model: neural networks. You’ll build neural networks using Tensorflow. Manage and manipulate datasets using Pandas; and how to manage and mitigate common sources of error and failure in deep learning systems.