+DS provides training modules and learning experiences grounded in generalizable data science content, while partnering with individual units or groups to develop additional specialized content.

Online Learning

Coursera Online Modules

The online +DS modules introduce the basics of data science, across multiple important application domains. These online modules are used as prerequisites for the in-person learning experiences listed below. Together, the online content supports the in-person "flipped" learning experiences. 

Module 1: Introduction to Machine Learning

Logistic regression, a simple machine learning (ML) method, is introduced and then extended to the multilayered perceptron (MLP), a fundamental neural network. Conceptual understanding is provided to motivate the form of the MLP, and its use.

Module 2: Basics of Model Learning

Concepts in learning model parameters are introduced, as well as model validation and testing. Learning based on stochastic gradient descent is addressed, allowing scaling to large datasets ("big data").

Module 3: Image Analysis and the Convolutional Neural Network

The convolutional neural network (CNN) is developed for image analysis, including details of the model and its underlying components. Model training is covered, as well as transfer learning and fine-tuning.

Module 4: Introduction to Natural Language Processing

Application of neural networks to natural language processing (NLP) is covered, from simple neural models to the more complex. The fundamental concept of word embeddings is discussed, as well as how such methods are employed within model learning and usage for several NLP applications.

Please note that Coursera for Duke is accessible to only Duke students, faculty, and staff. If you are not a member of the Duke community, you can access the public version of this Coursera course: https://www.coursera.org/duke.

Recorded Content from Machine Learning Summer School

In addition to the aforementioned Coursera content, +DS offers recordings of Duke’s Machine Learning Summer School (MLSS), which was help in June of 2018. If you are a Duke student, staff or faculty member, you can review these classroom recordings on Panopto, with accompanying slides and links to github code demos.

In-Person Learning Experience

Once you have reviewed the online content, +DS offers in-person opportunities to dive deeper into the information introduced in the online modules. These learning experiences will be developed to target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science.

See past learning experiences

Upcoming In-Person Learning Experiences

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