There are many sources for training on the basics of analytics and machine learning: universities; bootcamps; online education.

But where do you go for advanced skills training on the latest technologies?

You go to Quansight.

Quansight's Workshops equip your team with cutting-edge Python data analytics and machine learning skills. 

Our staff and training network include maintainers and leading contributors from the teams developing new Python data analytics tools. The result is that we can offer workshops from experts as soon as a new technology is released.


Quansight’s Workshops Empower Your Teams with Advanced Scientific Computing and Machine Learning Skills



Live sessions with expert trainers provide all the components that enhance successful learning by optimizing delivery speed, addressing students’ needs quickly, having 1 on 1 discussions with learners, and leaving no questions unanswered to ensure a deep understanding of the training material

Onsite and remote workshops consist of interactive teaching sessions and hands-on exercises to solidify your team’s learning. 



Have you already participated in one of our onsite or remote group workshops?  Are you interested in additional support?  Do you prefer an individualized mentoring experience?  If so, 1 on 1 coaching with our deep bench of data science experts is the answer. Our 1 on 1 coaching provides you or your team with access to experts to help reinforce key concepts and help navigate the important journey from Python or data science novice to expert.

Quansight's 1 on 1 coaching program structure is flexible and can be designed to fit with each learner’s needs.

Connect with us to talk about our workshops



Our training covers a broad range of topics in Data Science, Data Engineering and Data Analysis. A few of our latest workshops are featured below. Don't see what you are looking for? Connect with us and tell us what you need. Chances are we have it or can build it for you.


Each of the workshops below can accommodate up to 20 participants per training session. They will be delivered using video-conferencing technology in conjunction with Q-Hub (a customized JupyterHub with required software pre-configured).


This one-day course introduces Dask for scaling data analysis in Python. The workshop comprises initial explorations of the technical limits of NumPy & Pandas, the fundamentals of parallel computing in Python, using Dask dataframes in practice, and machine learning with Dask.


This half-day course introduces the open-source Python RAPIDS libraries for accelerating computation with GPUs (graphics processing units). Participants practice using the RAPIDS libraries for common ETL and machine learning workloads without having to program with low-level languages like C++.


This 5-day intensive workshop prepares practitioners to apply deep learning techniques to
computer vision problems, i.e., the automated analysis & interpretation of images. This includes the rudiments of computer vision theory & methods (e.g., feature extraction, object recognition, registration, segmentation, etc.) and principles of machine learning & deep learning (e.g., supervised/unsupervised learning, neural networks, etc.). The principal goal is to develop the core understanding to support building practical computer vision systems for tasks like object recognition in various realistic lighting conditions or natural settings.



This one-day workshop introduces participants to the foundations of Deep Learning. Participants practice constructing neural networks of various levels of complexity  (e.g., with text, images, etc.) to connect the core ideas to their realization in practical applications.


This half-day workshop introduces participants to Dask-ML for scaling standard Python machine learning tools (e.g., Scikit-Learn, XGBoost). Participants apply various pre-built models on moderate-to-large datasets to learn best practices for parallel & out-of-core machine learning.


This two-hour workshop introduces participants to Intake, a lightweight package for finding, investigating, loading and disseminating data.  Participants learn the fundamentals of using Intake to deploy data as would be required in industrial settings under various constraints (e.g., hardware, security, etc.).


This two-hour workshop introduces participants to the Xarray project for manipulating multi-channel data (e.g., as occur commonly in geosciences, etc.). Participants practice using Xarray for data analysis extending techniques from Pandas & NumPy to high-dimensional labeled arrays.


This one-day workshop builds techniques for web-based data exploration and interactive-app development using open-source Python libraries (including HoloViews, GeoViews, Bokeh, Datashader, and Param). These tools enable constructing rich high-performance, scalable, flexible, and deployable visualizations easily.


This one-day workshop provides a deep-dive into internal features of the Python programming language as related to asynchronous computation, concurrency, efficiency, functional programming, and object-oriented design.


This half-day workshop introduces participants to Numba, a tool for Just-in-Time compilation of Python code. Participants practice profiling sample application codes and accelerating them with Numba.

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