Overview

Familiarity with data science and analytics is now an in-demand skill applicable across businesses, industries, and organizations, from retail sales and manufacturing to engineering and regulatory agencies, as well as in the financial sector, insurance industry, cybersecurity, risk assessment, and more. The Bureau of Labor Statistics lists data science among the 20 fastest-growing occupations. The bureau projects that the field will keep growing by more than 30% in the coming decade.

Purdue University has an online course and program offerings in data science and analytics for a broad, diverse collection of users, including professionals who want to expand their skills - and boost their careers - and corporations desiring employee training programs, with noncredit, professional certification, and degree options available.

That includes Purdue's Data Science Series, a collection of courses that ground students in data analysis and modeling algorithms and techniques, as well as data visualization, before teaching them more advanced methods for analyzing large amounts of data to improve decision-making, including some inspired by the human brain. The series also covers technology for deploying and for collecting, analyzing, making sense of, and making use of data from connected devices - often referred to as the Internet of Things (IoT) - such as sensor networks. Finally, the series takes a dive into state-of-the-art deep learning and machine learning algorithms and how to implement, tune and optimize brain-mimicking neural network systems for running those algorithms.

For information concerning registration please email noncredit@purdue.edu.

Courses

This course provides a broad introduction to data analysis and modeling and is typically offered to undergraduate engineering majors. Topics covered include: How do I tell whether data follows a pattern? How do I answer questions about my data? What questions should I be asking in the first place? How can I visualize my data? The professor takes a problem-focused approach to the material, looking at how to use data analysis and modeling algorithms, such as clustering, regression, hypothesis testing, etc., to solve interesting engineering problems. The course uses Python to teach how to write these analyses. There are Python programming assignments during the first part of the course to explore concepts, followed by an end-of-semester mini-project where the students tackle a more extensive analysis and modeling problem.

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Equipped with an introductory understanding of Python and data science topics from Data Science I, the Data Science II course will expose learners to more advanced models, techniques, and other critical big data topics. Standard machine learning models will be covered comprehensively from their mathematical formulation (e.g., least-squares equations for regression) and model training (e.g., stochastic gradient descent) to their practical implementation (e.g., regularized regression in Python) and evaluation on real datasets. More advanced Python topics (e.g., objects and classes) will also be covered for learners to understand how to build reusable modules implementing custom data science methods.

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Broad introduction to the Internet of Things technology and sensors. A variety of aspects will be covered, including sensors for condition monitoring applications, wireless communication protocols, low-cost manufacturing technologies, machine learning approaches for handling data, and a variety of relevant application domains will be included. The course introduces the design of embedded computing systems. The instructors will take a problem-focused approach to the material and applications of interest.

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This course builds an understanding of techniques for meeting reliability and security requirements in connected computing systems, covering not only traditional threats but new threats posed by big data and large-scale systems. Students will learn big data analytic and machine learning techniques for improving reliability and security and develop software to apply to real-world datasets under realistic conditions.

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This course provides focused training on state-of-the-art machine learning algorithms, emphasizing deep learning. The students should acquire a moral understanding of the various techniques that have a proven successful record in solving fundamental engineering problems. Hands-on experimental training is provided through the course projects.

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