Review: AI Product Management Specialization by Duke University

Product management is a super hot career, but sometimes it's hard to keep up with all the breakout specializations. Some of the most common PM specializations are ecommerce and SaaS, but emerging trends include industries in AI and Web3. As a current product manager, or someone looking to upskill into these new trends, we'd recommend checking out Duke University's AI Product Management Program.  Duke is an extremely prestigious university, and we expect AI product management to explode within the next 5 years. The program is hosted by Coursera, and consists of 3 courses with a recommended 4 weeks of work each. This nets a total of 12 weeks, or 3 months for program completion.

The three courses to complete the specialization are:

• Machine Learning Foundations for Product Managers
• Managing Machine Learning Projects
• Human Factors in AI

The material is taught by real industry professionals and covers the basics of product managing with artificial intelligence. We especially loved the course on human factors in AI-- in short, human factors is essentially how to make a real product intuitive, but in the real world (with principles most commonly used in airplane or car design). Putting a course with human factors from Duke on your resume is definitely a way to upskill.

The curriculum is meant to be consumed over a 12-week period, but it can also be completed at your own pace.

Program Level: Intermediate

We absolutely loved the concepts taught in this specialization. That being said, this is not the course to take if you're new to the practice of product management.

We recommend this course for a current product manager or mid-to-senior level product analyst looking to upskill their career into an organization with AI/ML principles.

If you're looking to start a career in product management, never fear! We have options for you too. We recommend either starting with UVA or The University of Maryland's Product Management Specializations to learn the basics of the role, then expanding into this one if you are passionate about the basics.

We’ve recapped the learning objectives from each week to set your expectations for course material. The great part about this program is that you can jump to any course, and any section if it’s interesting to you. For example, if you’re an established PM only looking for prioritization processes, jump to week 4.

However, you only can get the certificate if you complete all 12 weeks of content. To audit an individual week-- find the exact course (we've linked them individually here) and click "audit" to save it to your profile. Then open the desired week on the side panel that aligns with our recaps.

Course 1: Machine Learning Foundations for Product Managers

Learning Objectives from Week 1: What is Machine Learning?

• Describe the basics of machine learning-- including what it is, what it does and why you should care.
• Explain the common types of machine learning tasks.
• Learn machine learning terminology to be able to articulate and understand conversations around AI.

Learning Objectives from Week 2: The Modeling Process

• Learn steps to develop machine learning models and the bias-variance tradeoff.
• Identify possible sources of data leakage and strategies to prevent it.

Learning Objectives from Week 3: Evaluating & Interpreting AI Models

• Differentiate between outcome and output metrics.
• Apply metrics to evaluate performance of regression and classification models.

Learning Objectives from Week 4: Linear Models

• Linear regression-- how it works, best time to use, and downfalls.
• Know the difference between linear and logistical regression.
• Types of regularization and the benefits of each.

Learning Objectives from Week 5: Trees, Ensemble Models and Clustering

• Learn how tree-based models differ from than linear models.
• Ensemble models-- advantages, disadvantages and how they are assembled.
• Know clustering and learn how K-Means clustering works.
  

Learning Objectives from Week 6: Deep Learning & Project Completion

• Learn the intuition and mathematical principles behind deep learning.
• Identify common applications of deep learning for computer vision and natural language processing (NLP).
• Explain the strengths and challenges of deep learning compared to other forms of ML.
  
• Project: get real experience with the process of building and evaluating machine learning models.
  

Course 2: Managing Machine Learning Projects

Learning Objectives from Week 1: Identifying Opportunities for Machine Learning

• Identify opportunities for the application of ML to solve problems and use best practices for product evaluations.
• Apply heuristics and baseline models to jump-start ML projects.
  

Learning Objectives from Week 2: Organizing Machine Learning Projects

• Prioritize projects using the CRISP-DM data science process.
• Structure a ML project team and define roles.
• Organize project team work using best practices and track progress.
  

Learning Objectives from Week 3: Data Considerations

• Identify strategies to collect data to support modeling, and evaluate sources of data.
• Walk through steps to build a data pipeline.

Learning Objectives from Week 4: Machine Learning System Design & Technology Selection

• Describe the key technology decisions involved in designing ML systems.
• Identify the main criteria to consider in making technology selection decisions.
• Get to know some of the commonly used tools among data scientists and ML practitioners.
  

Learning Objectives from Week 5: Model Lifecycle Management

• Identify key elements and risks of an ML system to monitor and determine model retraining strategies.
• Describe the benefits and best practices of model versioning.
  

Course 3: Human Factors in AI

Learning Objectives from Week 1: Design of AI Product Experiences

• Apply design-thinking principles to design human-centered AI product experiences.
• Explain the key user experience design decisions unique to AI products.
• Integrate model transparency, communication of uncertainty and feedback loops within products.
  

Learning Objectives from Week 2: Data Privacy and AI

• Implement privacy policies and practices which comply with the Fair Information Practices (FIPS).
• Identify the key laws related to data privacy in the U.S. & Europe.
• Explain best practices to protect user's privacy in AI systems.

Learning Objectives from Week 3: Ethics in AI

• Explain the goals of Fair, Accountable and Transparent AI.
• Identify sources of bias in AI projects and strategies to mitigate ethical risks.
  

Learning Objectives from Week 4: Human and Societal Considerations

• Differentiate between how AI and humans learn and create predictions.
• Explain approaches to using AI to augment human intelligence.

The program is only $39/month, and comes with a Linkedin Certificate on behalf of Duke University (remember– this is a prestigious place!!). If you complete the curriculum on the proposed timeline, it should take about 5 months, though you could blitz through it on a break in far less. While that seems steep, compared to a degree or bootcamp this micro-certification is a steal!

If you have a learning budget, or are dedicated to upskilling your product management career– we recommend completing the program and getting the shareable certificate (GET RECEIPTS!). This will help make your Linkedin more searchable to recruiters who may be looking for specific keywords and programs.

To audit the program and simply learn the material, this program is completely free! Thanks Coursera! We recommend auditing the first course (AI Foundations) for those who might be unsure of the AI product management concepts. To audit, simply click "Enroll for Free" and click "Audit" on the bottom of the second step. Voila, you're in!

This is a newer course and does not have too many reviews. However, from the reviews it does have, the courses within the specialization are very highly reviewed by real and aspiring product managers alike– averaging between 4.6 and 4.8 per course!

Some of our favorite positive review points:

• It is a good introduction into machine learning concepts that finds the right balance between required depth and and time efficient knowledge transfer. -Wolf Z.
• I rarely leave comments but this is legit one of the best courses I've taken on Coursera. It's clear enough to be accessible to beginners yet offers sufficient information to allow more-intermediate learners take assignments further. Really good, for real. -Jose A.

Aggregations of negative review points:

• Too technical (brush up on your math!)
• Presenters could be better prepared and talk faster (perhaps watch at 1.5x speed)

... and our favorite overall review:

I love the way the course is structured. Jon Reifschneider allows you to view and download the slides before diving into the videos. He explains the content thoroughly and supports his explainations with charts and diagrams which I personally find very helpful. I'm so glad I took the time to complete this course.

-Justine R.

Can't get enough AI? Stanford x DeepAI’s Machine Learning with Andrew Ng 

This is probably the most popular AI / Machine Learning course on the market, with a huge fanbase of raving students. Andrew Ng is one of the most popular voices in AI, and his teaching style is engaging and fun. No pre-reqs are required to excel in this course, and it’s a great overview of the field and where it’s heading. Read our full review here.

For the analytically-inclined: Google's Data Analytics

Google sponsors a data analytics certificate program through Coursera. This is one of the more coveted certificates in the industry for Google Analytics. It's free to audit, but if you want the certificate to show off (recommended), it's $49 a month to complete at your own pace.

For the fans of research: University of Michigan's User Experience  

This class is sponsored by the University of Michigan, and has a great rating of 4.8 with 1.5k reviews. This specialization is a great mix of UX research and design. With a Coursera subscription (included with your product management specialization, may we add!), you can get a certificate OR audit for free! Read our full writeup here.

For the newbie product manager: UVA's Product Management Fundamentals

UVA Sponsors a prestigious specialization program through Coursera. This program focuses on test and learn strategies, identifying and acting on customer insights, and running an effective product program. Read our full writeup here. It's free to audit, but if you want the certificate (recommended), it's $79 a month to complete at your own pace.‍

More In-Depth Content: Udacity's NanoDegree for AI Product Management

This NanoDegree is hosted on Udacity and takes around 2 months with 5-10 hours of learning per week.This program is comprised of 4 courses and 3 projects. The courses are AI in Business, Dataset Manipulation, Model Creation, and Measuring Impact and Updating Models.

Duke's Product Management Program is a great way to sharpen your product management skillset and expand into emerging technology without breaking the bank with an additional college degree or bootcamp.

Here at Bridged we are huge fans of stacking micro-certifications to achieve desired career results. This program could be one notch in your arsenal to really kick your technical expertise into gear!