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.

A course that teaches a learner to write server-side code using Express. It walks learners through features required for effective web and mobile application development. By the end of this course students will be able to create a simple web server application with Express.

A course that teaches foundational knowledge of Node.js to leverage your understanding of JavaScript to create new and powerful applications.

A course that teaches foundational knowledge of Python language specifically for backend.

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.).

An introduction to the concepts of Containerization and Virtualizations, and understanding the differences between them.

This practical, hands-on course recaps and ties together all stages of ML cycle in production into an automated CI/CD pipeline.

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.

Using their own licensed version of Excel, students will build on foundational Excel skills to handle more complex analytical situations. Students will learn how to build a variety of models and test scenario analyses to make better data-driven decisions. By the end of this program, students will be able to produce multiple scenarios in Excel, optimize data models, and build predictive linear models to test cause and effect relationships.

This course is designed for executives seeking to foster a data-driven culture within their organization through informed leadership. Employees will learn need-to-know vocabulary for describing and asking informed questions about data initiatives, from the different roles that make up a data team to the data analysis techniques available. Through a series of interactive exercises and breakout discussions, this course will help executives better navigate the data components of their job and empower them to effect strategic data-driven innovation at an organizational level.

This course is designed for managers seeking to bolster their data literacy with a deep dive into data science tools and teams, project life cycles, and methods. This course will demystify the structure of data science projects from start to finish, helping students to make more informed decisions about how to identify data-driven solutions, structure their teams, allocate resources, and interpret results. Employees will also learn how to make the most compelling cases possible by comparing the advantages and disadvantages of a variety of models, methods, and visualization techniques.

This course teaches participants the fundamentals of data visualization, which they can use to support data- driven decision-making when exploring and presenting quantitative information. By the end of this course, participants will be able to recognize misleading or inaccurate charts and graphs, understand the design principles involved in creating compelling and accurate visualizations, and craft a narrative that accurately supports the data, provides context, and reveals actionable insights.

This 12-hour workshop teaches participants the fundamentals of data visualization, which they can use to support data-driven decision-making and a data-driven culture. By the end of this course, participants will be able to recognize misleading or inaccurate charts and graphs, understand the design principles involved in creating compelling and accurate visualizations, and create a narrative that accurately supports the data, provides context, and reveals actionable insights.

Data is often messy, requiring cleaning and restructuring before it can be reliably used in a program or project. In this course, learners will augment their understanding of Python using two of the most popular libraries for data cleaning and wrangling, NumPy and Pandas. First, learners will practice working with NumPy objects, transforming data into efficient arrays for ease of analysis. Then, learners will clean and structure arrays into readable tabular DataFrames using Pandas, allowing them to profile a dataset for key answers and values.

Data is often messy, requiring cleaning and restructuring before it can be reliably used in a program or project. In this course, learners will augment their understanding of base R using an open-source set of packages intended for data cleaning and wrangling, the tidyverse. After installing this package, learners will practice working with functions that allow data to be selected, filtered, summarized, rearranged, and otherwise transformed according to analyst-vetted best practices.

Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, according to input parameters. In this course, learners will identify use cases for decision trees in Python. They will wrangle data and implement a decision tree model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind decision trees to tune the model and improve its classificatory function.

A theoretical course covering topics on how to handle data at scale and the different tools needed for distributed data storage, analysis, and management. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of distributed computing.

Explore how to use Numba the just-in-time, type-specializing Python function compiler to accelerate Python programs to run on massively parallel NVIDIA GPUs.

Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the\npower of GPUs using the most essential CUDA techniques and the Nsight Systems profiler. You’ll learn how to write code, configure code parallelization with\nCUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that\nyou’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable\nmassive performance gains