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Commerce Data Academy (CDA) is a set of tutorials originally accumulated in order to teach employees at the Department of Commerce standard programming, Data Science, and computational skills. Introductory course that covers basic JavaScript concepts and makes use of free online tools for programming in JavaScript with real-life examples.

Comprehensive video courses covering many coding languages such as Python and Java for beginners.

A set of Excel demos for statistical and geostatistical methods.

Learn how to import data from different sources, create mashups between data sources, and prepare data for analysis. Then, learn how to calculate using the DAX calculation engine. Learn to visualize data using the Power BI cloud service. 6 weeks with 2-4 hours per week to complete.

Gives access to extra tutorials and instructional lessons that help supplement the instruction portion of the course while including other informational videos about Comp. Sci. topics.

An organization that was forged to help those trying to learn programming and web development processes. Python, Javascript, CSS, SQL, and HTML tutorials on their platform that are taken at your own pace.

Gives access to extra tutorials and instructional lessons that help supplement the instruction portion of the course while including other informational videos about Comp. Sci. topics.

Covers content from introduction material on data analytics and geostatistics to spatial data analytics to machine learning. He also covers various programming languages like R and Python while also giving lectures on how to apply them using real-world data.

Comprehensive video courses covering many coding languages such as Python and Java for beginners.

Structured Query Language, the standard language for accessing and manipulating databases. The classic language for data management. 7 hours to complete and self-paced.

Learn how to use SQL to store, query, and manipulate data through video lectures and exercises.

An organization that was forged to help those trying to learn programming and web development processes. Python, Javascript, CSS, SQL, and HTML tutorials on their platform that are taken at your own pace.

Scalable systems are needed to collect, stream, process, and validate data. Introduction to “big” data engineering, students will receive hands-on experience building and deploying realistic data-intensive systems. Topics will cover streaming, data cleaning, relational data modeling and SQL, and Machine Learning model training. Discussion of practice of programming with large external datasets that cannot fit in main memory on a single machine. Course will consist of bi-weekly programming assignments, a midterm examination, and a final.

Introduction to mathematical foundations of machine learning, focusing on matrix methods. Mathematical topics include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Features real-world applications ranging from classification and clustering to denoising and data analysis. Background in calculus and exposure to numerical computing via Matlab, Python, Julia, R is preferred. Prerequisites: 2 quarters of calculus (MATH 153 or higher) or equivalent, and CMSC 11900 or CMSC 12200 or CMSC 15200 or CMSC 16200.

Introduction to mathematical foundations of machine learning, focusing on matrix methods. Mathematical topics include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Features real-world applications ranging from classification and clustering to denoising and data analysis. Background in calculus and exposure to numerical computing via Matlab, Python, Julia, R is preferred. Prerequisites: 2 quarters of calculus (MATH 153 or higher) or equivalent, and CMSC 11900 or CMSC 12200 or CMSC 15200 or CMSC 16200.

The LTER Network Office is hosting this opportunity for graduate students to learn more about career opportunities outside of academia. Each webinar will consist of brief introductions by 4-5 panelists who work in a particular type of career, followed by opportunities for participants to ask questions. Each webinar is recorded and can be accessed through the link. Registration required.

The Environmental Data Initiative is an NSF-funded project, actively promoting and enabling curation and re-use of environmental data. We assist researchers from field stations, individual laboratories, and research projects of all sizes to archive and publish their environmental data. Notes on various EDI workshops ranging from data publication training to hackathons.

A collection of recorded webinars ranging from topics on general and specific data management practices and applications.

The Environmental Data Initiative is an NSF-funded project, actively promoting and enabling curation and re-use of environmental data. We assist researchers from field stations, individual laboratories, and research projects of all sizes to archive and publish their environmental data. A collection of repositories for training in working with data sets, including topics like data set construction, data management, data package design, data portal-related software, and more.

The EDI Youtube channel contains tutorial educational videos on managing and manipulating data tools for constructive use, as well as recorded webinars and discussions.

Gives access to extra tutorials and instructional lessons that help supplement the instruction portion of the course while including other informational videos about Comp. Sci. topics.

Website containing lectures on even more programming languages such as C++ and Linux.

Website containing lectures on even more programming languages such as C++ and Linux.