This course looks into the past, present, and future of technologies that impact labor, with an eye to empowering students with knowledge about the social, economic, and political dimensions of …
Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world …
Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic …
Provides an in-depth exploration of probabilistic and statistical methods used to understand, quantify, and manage uncertainty. Learn foundational concepts in probability and statistics, simulation techniques, and modern approaches to parameter …
Equips students with some of the most used deep learning architectures. Explore feed-forward networks, convolutional neural networks, UNETs, encoders-decoders, generative adversarial networks and transformers. Analyze tools of explainable AI. Focused …
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This course will equip students with some of the most commonly used deep learning architectures. We will explore feed-forward networks, convolutional neural networks, UNETs, encoders-decoders, generative adversarial networks and transformers. …
Provides domain knowledge of humanitarian action, the data and data systems used by humanitarian organizations, the lifesaving implications of predictive analytics in disaster contexts and the complex data ethics considerations …
Genomics Foundations introduces core concepts in modern genomics and human genetics underlying computational biology and public health genomics. The course integrates key biological principles with quantitative reasoning and hands-on use …
An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential …
Trends in hardware and software for Big Data Systems and applications. Cover principles driving data infrastructures, which enabled the training of AI models on datasets (speech, sounds, images, video, languages) …
Code an end-to-end data science project with core software engineering and automation to quickly integrate into a corporate environment. Use version control to focus on solutions, leverage automation at your …
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An intensive overview of cloud infrastructure and their role in data science. Topics will include storage as a service, ephemeral computing resources, auto-scaling, and event-driven workloads. Special attention will be …
This course focuses on making students more effective at identifying and designing AI use cases to create novel AI-powered products and services. Students will work with a variety of AI …
Focuses on the application of data science to critical problems and opportunities in business. You will learn business concepts in strategy, markets and competition, and will apply data science to …
This course provides selected special topics in data science to graduate and undergraduate students.
Covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and …
This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter …
This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.