This course starts of a series of topics on neural networks designed to solve a particular family of tasks. In this course students will be able to get an overview of how to work with image data and build Convolutional Neural Networks (CNNs) - the industry standard for tackling image-based data.

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

A course that builds on the foundations of CSS and dives into the exploration of CSS architecture.

A course that builds on the architecture of CSS and provides learners with a toolset for creating custom stylesheets and enhancing the look of standard webpages.

Cypress is an end-to-end testing framework for your web application. This course explores its features, core concepts, its ecosystem, and how to write tests.

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 introduces the NumPy and Pandas packages for Python, and shows how they can be used to ask and answer a variety of questions involving data analysis. NumPy and Pandas are the foundation of the “PyData stack,” a set of numerical and scientific Python packages that have become extremely popular in recent years. Indeed, some financial institutions have begun to replace certain uses of Excel with Pandas, because of its versatility and power.

This course is designed for executives seeking to foster a data-driven culture within their organization through informed leadership. Leaders 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.

This course is designed for managers seeking to bolster their data literacy with a deep dive into data science team roles, the data project life cycle, and machine learning methods. Learners will discuss strategies for building a data-driven culture within and across teams, allocating resources in support of data projects, and conveying results through data storytelling. Learners will also evaluate the data maturity of their teams in order to prioritize what to do next, as defined by best practices in data governance and data ethics.

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.

A deeper dive into the many capabilities of a relational database, how to optimize usage and make sure that your are getting the most use out of your database so that you have a strong base for your applications.

An introduction to NoSQL databases, how they differ from relational and when and why they are beneficial to use.

An introduction to relational databases and how relational databases can help shape your production-ready application.