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Graduate Certificate in AI Foundations for Product Innovation

Starting August 2020: An online graduate-level program in applied AI and Machine Learning designed for working professionals

Artificial intelligence and machine learning are creating immense opportunities for innovation in products and services across every industry, from health care to energy to manufacturing—and beyond.

Duke’s online Graduate Certificate in AI Foundations for Product Innovation is a standalone, credit-bearing non-degree offering aimed at professionals who want to keep working and keep learning. Learn how to apply these technologies within your industry to build innovative products and services.

This certificate can be completed in 15 months. You pay tuition per course without the commitment required for a traditional degree program.

Plus, for applicants who can start in August 2020 there is:

  • No application fee
  • No GRE required
  • Scholarship consideration

HOW TO APPLY


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Scholarships

A limited number of merit-based scholarships covering 20 percent of tuition are available. No separate application required—all applicants are automatically considered.

HOW TO APPLY

Apply by July 17, 2020—Start Python Bootcamp on August 17

  • Open to qualified applicants worldwide
  • GRE is not required for this certificate
  • No application fee for applicants to the 2020-2021 academic year

Application Requirements

Earn a Duke Credential

What You'll Receive

A great education! You'll develop a strong grasp of AI and Machine Learning fundamentals—as well as the business, policy, and ethical considerations of implementing them in products and services.

Also, as a Duke online student you will:

  • Experience online courses taught by expert Duke faculty and industry practitioners
  • Build a portfolio of real-world, hands-on projects
  • Receive individualized course advising
  • Be engaged with outstanding peers around the world and with our faculty
  • Be invited to attend an optional on-campus workshop and reception
  • Earn credits toward a Master of Engineering in AI for Product Innovation, if you decide to continue at Duke

Is This Program Right for You?

In general, applicants should:

  • Hold at least a bachelor’s degree in engineering or science
  • Have education and/or professional experience in a technical or scientific industry, and a desire to learn to apply AI within the industry
  • Have at least one semester of experience in programming in any language, Python preferred. An extensive programming background is not required for admission
  • Expect to provide a statement of purpose, resume and letters of recommendation


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and sign up for live Q&A events with the director »

“There is a growing need for engineers with domain knowledge plus machine learning skills, and Duke’s AI for Product Innovation program helps address that need with its focus on developing leaders who can apply advanced analytics to create new products & services.”

Adnan Haider ’07
Associate Partner, Advanced Analytics & AI, IBM

Curriculum

Certificate students will complete a preparatory bootcamp and a series of four core courses designed with input from industry experts and structured around the product development process.

The novel curriculum is designed to build foundational skills in AI and machine learning, combined with a heavy emphasis on applying these technologies to real-world problems.

Courses cover key aspects of the product development process for building AI-enabled products and, in addition to core technical knowledge, students will gain an understanding of the business, legal and ethical aspects of utilizing AI.

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Online Summer Python Bootcamp

The Python Bootcamp is a six-week online summer training course for working professionals in the fundamentals of Python programming, the commonly used libraries for data analysis, and the basics of probability and statistics. Python Bootcamp ensures each student is well-prepared to enter the core courses of the certificate program.

510: Sourcing Data for Analytics

Course Description: In industry, one of the main activities, and challenges, of implementing machine learning applications is collecting data to use in modeling. This course introduces students to both the technical and non-technical (business, regulatory, ethical) aspects of collecting, cleaning, and preparing data for use in machine learning applications. The first segment of the course will be an introduction to numerical programming focused on building skills in working with data via the Numpy and Pandas libraries, two of the most common tools used by teams working with data and modeling. Technical aspects covered will include the types of data, methods of sourcing data via the web, APIs, and from domain-specific sensors and hardware (IoT devices), an increasingly common source of analytics data in technical industries. The course also introduces methods and tools for evaluating the quality of data, performing basic exploratory data analysis, and pre-processing data for use in analytics. Non-technical aspects covered include an introduction to data privacy, GDPR, regulatory issues, bias, and industry-specific concerns regarding data usage.

520: Modeling Process & Algorithms

Course Description: This course is an introduction to the modeling process and best practices in model creation, interpretation, validation, and selection of models for different uses. The primary machine learning algorithms, both supervised and unsupervised, are introduced and explained with the necessary level of mathematical theory to establish students’ intuition for how each algorithm works. The primary focus will be on “traditional” machine learning approaches but it will also introduce deep learning and its applications. At the end of this course, students should have a solid understanding of the end-to-end modeling process and the different types of model algorithms along with the strengths, weaknesses, assumptions, and use cases for each type.

530: Applying AI in Practice

Course Description: This course focuses on the implementation via programming of the data pre-processing and machine learning modeling process, taught in a case-based format where students gain experience in applying the concepts learned in the other core courses (Sourcing Data for Analytics, and Modeling Process & Algorithms) on real-world scenarios in industrial domains. The course will be taught in Python, as the language used today by the vast majority of teams in industry working with data modeling. The content will consist of case studies drawing on real-world, often messy datasets that will reinforce students’ programming skills in cleaning, exploring and visualizing data, applying machine learning algorithms, and interpreting and validating results in the context of solving business problems. Programming assignments will expose students to several of the commonly used existing tools and libraries for implementing ML.

540: Building Products Using Deep Learning

Course Description: This course builds on the previous semester core courses by bringing together the various aspects of a machine learning-based product implementation, including opportunity identification, data sourcing, business planning, and model prototyping and evaluation. The course is intended to be hands-on and includes a significant team project prototyping the use of a deep learning model within a new or existing industry product/service to create an additional value-add for the product. In addition to project-based learning, a number of industry-focused case examples will be used to strengthen each student's understanding of the applications for deep learning, including natural language processing, computer vision, and analysis of structured and time-series data. The course will reinforce students’ skills across all aspects of the product development process using AI and will build a solid understanding of the applications of deep learning in particular.

Expert Duke Faculty

The online courses are taught by Duke faculty members with industry experience—and who teach these same courses in classrooms on the Duke campus.

Jon Riefschneider

Jon Reifschneider

Program Director

A former technology executive who was responsible for launching predictive analytics products on which over half of major US electric utilities and global airlines depend.

Luis Morales

Luis Morales

Programming Faculty

Twenty-eight years of experience in technical leadership roles at Cisco Systems and AT&T Labs. Winner of Cisco’s highest technical recognition.

Daniel Egger

Daniel Egger

Data & Analytics Faculty

Founder and CEO of a series of venture-backed technology companies before joining the Duke faculty.

“The strength of Duke’s reputation in AI and Machine Learning, combined with the focus on applying these technologies to solve the world’s big challenges in healthcare, retail, energy and beyond, make Duke the logical choice for someone interested in pursuing education in this space.” 

TARUN THAPAR, ’15
SENIOR PRODUCT MANAGER-PERSONALIZATION, WAYFAIR

Tuition and Financial Aid

Tuition is paid per course. A limited number of merit-based scholarships are available.

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Tuition

Duke Online tuition for the 2020-2021 academic year is $7,338 per course.

There are two semesters per academic year. Students typically take one course per semester. At current rates, completion of the four required program courses would result in a total tuition cost of $29,352.

Scholarships

For students joining the certificate program in the 2020-21 academic year, a limited number of merit-based scholarships are available which cover 20 percent of tuition expenses. All applicants to the program will be automatically considered for the available scholarships.

Transcript Fee

All entering students will be charged in the first semester a one-time mandatory fee of $120 for transcripts. This fee entitles the student to an unlimited number of Duke transcripts.

Payment of Accounts

The Office of the Bursar will issue invoices to registered students for tuition, fees, and other charges approximately four to six weeks prior to the beginning of classes each semester.  The total amount due on the invoice is payable by the invoice late payment date which is normally one week prior to the beginning of classes. A student is required to pay all invoices as presented and will be in default if the total amount is not paid in full by the due date. A student in default will not be allowed to receive a transcript of academic records.

Inquire at the Bursar’s Office for information on Monthly Payment Option, Late Payment Charge, and Refunds for Withdrawal from School during fall and spring semesters. Go to Bursar's Office website »


Admissions Team

Have a question? Contact our admissions team. We look forward to hearing from you!

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