Data Science Education

Book Enplus founder and data science expert Dan Gerlanc for your next training, conference or Big Data analysis or engineering seminar.

What Our Clients Say

"Daniel Gerlanc's Python course was fun and easy to digest, yet comprehensive and covered several topics. He understands how to teach a large audience and is able to adapt his delivery to the members of the class."

Kate Strachnyi - Story By Data
LinkedIn's Top Voice in Data Science, 2018 - 2019

"...If I were to organize a future Python/Pandas training session, Dan would be the first person I'd ask. Dan's ODSC session was by far the best in-person Python training I have ever attended."

Matt Van Vlack - Vice President
Director, Quantitative Research - Fidelity

"I thought the training was excellent in all respects. I especially appreciated the informal Q&A that ran through the whole thing, where your expertise shone through and your relaxed, informal style made everyone comfortable asking the simplest questions."

Colm O'Cinneide
Professor of Mathematics, Columbia University

"As a data scientist with nearly four years of experience in the field, I was able to walk away from the session with a few new tips and tricks under my belt. The package is an all-around excellent bundle taught by a talented and experienced data engineer, analyst, and scientist."

Jane Thompson - Manager of Analytics

Courses We Offer

Contact us to schedule time with Dan. He’s available for small group and company trainings, as well as online and in-person seminars and conference presentations.

Foundations of Python and Pandas

Pandas is both powerful and one of the most popular libraries for working with tabular data in Python. In this course, you'll learn how to use the two most important data structures in Pandas, the Series and the Dataframe, and understand how to avoid common Pandas missteps.

Advanced Python and Pandas

Building on the material in "Foundations of Python and Pandas", this course explores the more advanced features of Pandas. These include working with time series data, performing joins, and reshaping data between wide and long formats.

Dask and Python

In this course, you'll learn how to use Dask, a Python library for parallel and distributed computing, to scale compute and memory across multiple cores. Dask provides integrations with Python libraries like pandas, numpy, and scikit-learn so you can scale your computations without having to learn completely refactor your code.

API Development with FastAPI

In this course, you'll learn how to use FastAPI, a modern Python library that greatly simplifies API development by using recent Python language features like type hints and asyncio. We'll learn how to connect arbitrary machine learning models to your API and review cases where more specialized tools for model serving should be used.