Find out how to write and configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and apply the techniques to accelerate a CPU-only Laplace Heat Equation to achieve performance gains.

This 12-hour workshop educates participants on the fundamentals of data science and how to apply them in such a way that it is relevant even to those who will neither manage nor consume data regularly. Attendees will learn: data science concepts and associated terminology; why data science is important; what it means to work in a data-driven culture including the skills necessary to thinking critically about data; common issues in data collection and analysis such as bias, data gaps, and imprecision; strategies for interpreting data visualizations produced by others; and foundational steps those who are not data scientists can take to incorporate data analysis into their work.

Learn how deep learning (DL) works through hands-on exercises in computer vision and natural language processing (NLP). You will train deep learning models from scratch, and pick up tricks and tools for achieving highly accurate results along the way. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly

Find out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining of deep neural networks using TensorFlow.

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.

This course is designed for students looking to take their data analysis skills to a more advanced level and learn how to communicate effectively with data. By the end of this program, students will be able to build interactive data visualizations, define best practices in data visualization, and manipulate data in Power BI.

This course builds on foundational data skills to teach students how to effectively manipulate data in SQL. SQL is one of the most popular database management languages available and is critical in storing and retrieving data. By the end of this program, students will be able to describe database structures, import and combine multiple data types, and manipulate data to discover new insights.

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.

Date: May 2, 2024Time: 8:00 p.m. – 9:00 p.m. WAT (UTC +1) Event Duration: 1 Hour (includes Q&A) Cost: Free Join us for an insightful…

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.

The "Practical Python Programming" course is an intermediate-level Python course aimed at individuals with some programming experience. It focuses on teaching Python through the practical problem of manipulating data, particularly stock market data, and introduces other programming topics such as data analysis, plotting, web programming, and software design. The course provides hands-on exercises, emphasizes the use of real-world examples, and covers essential concepts like functions, classes, generators, testing, and package organization.

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.