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.

Prepares practitioners to tackle the automated analysis & interpretation of images with practical computer vision systems. This includes the rudiments of computer vision theory & methods (e.g., feature extraction, object recognition, registration, segmentation, etc.).

Introduces Dask for scaling data analysis in Python. The workshop begins with an overview of the fundamentals of parallel computing in Python with explorations of technical limitations of NumPy & Pandas. After exploring core Dask data structures, participants will apply Dask arrays & dataframes in practice, using dashboard tools to monitor Dask workflows and measure performance.

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.

Builds techniques for web-based data exploration and interactive-app development in Python using open-source Holoviz libraries (that is, HoloViews, HvPlot, Datashader, and Panel). These tools enable constructing rich high-performance, scalable, flexible, and deployable visualizations easily.

Overviews Intake, a lightweight package for building and effectively using data catalogs with Python. Intake helps with finding, investigating, loading and disseminating data. Participants will learn the fundamentals of using Intake to connect with data sources in practical situations.

Introduces participants to Nebari, an open-source data science platform developed for collaboration and scalability. Participants will familiarize themselves with using Nebari to provide robust infrastructure that can quickly be set up — deployed — by data practitioners or administrators.

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.

International Attendees: For those residing outside of the US, please email us at [email protected] to reserve your spot at Polars Essentials. We are working on a glitch that doesn’t allow international attendees to register.

Date: May 16th, 2024
Time: 10:00 a.m. – 2:30 p.m. EDT

Price: $150.00

Intro

Master data structures, manipulation techniques, and high-performance analysis in the upcoming training, Polars Essentials, led by Marco Gorelli, a core developer of pandas and Polars. From dataframe operations to time series analysis, this is your chance to learn from the best in the field using a “blazingly fast dataframe library for manipulating structured data.” 

This four-hour course will run in two, two-hour segments with a break in between. Save your seat now to start leveraging the full potential of Polars.

Introduces participants to the Polars library for high-performance dataframe manipulation. Participants will have the opportunity to explore the Polars API for data creation/manipulation with Polars and tune data pipelines for speed.

Introduces participants to the foundations of Deep Learning through PyTorch. Participants practice constructing neural networks of various levels of complexity to connect the core ideas to their realization in practical applications (e.g., image processing, natural language processing, etc.).

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 (e.g., C/C++).

Introduces participants the core concepts and tools to use Large Language Models (LLMs) in practice. Participants will learn the concepts underlying LLMs and what they can — and cannot — do.

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.