Review: AI for Business Specialization by Wharton

Artificial Intelligence (AI) has taken the world by storm in recent years and has become one of the most in-demand and rapidly growing career fields. From things you know like Siri and Alexa to topics of the future like drones and self-driving cars, AI has become increasingly integrated into our daily lives and has transformed the way we live and work. With AI being at the forefront of technological advancements, especially recently with the industry storm that is Chat GPT, it's no surprise that companies are eager to embrace and leverage it. The Wharton Business School of the University of Pennsylvania recognized this trend ahead of the curve and launched its AI for Business Specialization in 2021. The program is hosted on Coursera and focused on equipping beginners and professionals alike with skills and knowledge to harness the power of AI in the workplace.

As mentioned before, the course is fairly new and dropped in Q4 of 2021. While there have been massive advances in the AI space since then with the introduction of ChatGPT and Bard, the concepts and practical use cases predominantly remain the same. If anything, they've expanded beyond what this program could image. We loved taking this specialization ourselves to learn about the relevant applications.

The self-proclaimed beginner specialization consists of 4 courses with around 4 weeks of work each at two hours per week. This nets a total of 16 weeks, or 4ish months to complete the specialization.

The four courses to complete the specialization are:

• AI Fundamentals for Non-Data Scientists
• AI Applications in Marketing and Finance
• AI Applications in People Management
• AI Strategy and Governance

The material is taught by legit Wharton professors and covers the basics of how AI and ML can drive decisions in marketing, product, finance and human relations fields. I'm no stranger to AI concepts, especially in product personalization and curated content contexts from my previous life in analytics-- but I found the concepts discussed in this specialization wildly interesting and relevant.

Best for: AI Beginners

This program has great material, but some of the concepts may be a little foreign if you have no previous tech-industry experience. That being said, any unknown information is a quick Google search (or ChatGPT query) away.

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This specialization would be especially relevant to product managers looking to transition into AI/ML, or data scientists looking to have less-technical discussions.

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 HR professional wondering how AI could help improve your workflow, hop over to course 3, and pay special attention to week 2 (direct applications on AI in HR).

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: AI Fundamentals for Business

Learning Objectives from Week 1: Big Data and Artificial Intelligence

• Learn basics of Big Data and the skill sets needed to manage, understand and execute on it in a company.
• Review the 3 types of Machine Learning (Supervised, Unsupervised, and Reinforcement Learning) and practice matching to real-world applications.

Learning Objectives from Week 2: Training and Evaluating Machine Learning Algorithms

• Practice using the 3 types of ML and analyze performance.
• Introduce the concepts of neural networks and deep learning.
• Learn the common loss functions and how they affect your data.

Learning Objectives from Week 3: ML Application and Emerging Models

• Examine the implementation of GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) in Deep Learning.
• Build an example within Teachable Machine.
• Analyze the role of data in building ML systems.

Learning Objectives from Week 4: Industry Interviews

• Observe Ed Lee (VP of Global Menu Strategy at McDonald's) talk about ML / AI challenges.
• Identify use cases for ML in various applicable industries.

Course 2: AI Applications in Marketing and Finance

Learning Objectives from Week 1: AI and the Customer Journey

• Learn about customer journeys and how to cater to individual customer needs.
• Study the ways AI can influence customer behaviors.
• Analyze hidden risks that may come with AI/ML applications.

Learning Objectives from Week 2: Personalization

• Study real-life use cases of personalization and recommendation algorithms (they're EVERYWHERE).
• Learn the benefits and drawbacks of how curated content can affect customer journeys.

Learning Objectives from Week 3: Finance

• Learn the best strategies and limitations for incorporating AI predictions in the financial industry.
• Apply these principles and study AI in assessing credit risk.

Learning Objectives from Week 4: Additional AI Applications in Finance

• Study modern methods for combating fraud in financial institutions.
• Identify techniques to maximize AI in digital finance.
• Examine methodologies to use Big Data to create customized experiences.

Course 3: AI Applications in People Management

Learning Objectives from Week 1: The Promise and Potential of AI in HR

• Learn the ways AI is already impacting Human Resource Management.
• Study decision-making and the key roles it plays in machine learning.

Learning Objectives from Week 2: HR AI Applications

• Parse out the types of data used in AI algorithms.
• Showcase how data is used to make predictions in people management.

Learning Objectives from Week 3: Challenges with Applying AI to HR

• Identify hiring trends and their impacts on hiring decisions.
• Analyze the best strategies for incorporating AI predictions.
• Examine the limitations of machine-based selections.

Learning Objectives from Week 4: Emerging Solutions

• Identify and assess what is considered bias in machine learning.
• Study ways to improve biases in data.
• Learn different use cases of blockchain technology in HR.

Course 4: AI Strategy and Governance

Learning Objectives from Week 1: Economics of AI

• Learn about the different tools currently in use for easier access to AI.
• Study the economic impacts of wider AI adoption.
• Explore AutoML and the competition around more complex AI tools.
  

Learning Objectives from Week 2: Innovation in AI

• Study Big Data use cases and implementation strategies.
• Practice evaluating best-practices and the importance of AI with a sample company.

Learning Objectives from Week 3: Algorithmic Bias and Fairness

• Learn the necessity of equitable algorithms and get familiar with Explainable AI.
• Study the difference between data manipulation and misrepresentation.
• Identify methods of data protection and integrating Explainable AI.

Learning Objectives from Week 4: Linking Non-Financial Metrics to Financial Performance

• Learn how to retain AI's benefits without experiencing as many associated risks.
• Explore ethical AI frameworks and best-practices for responsible AI policies.
• Examine legal ramifications that drive Explainable AI practices.

The program is only $79/month, and comes with a Linkedin Certificate on behalf of the Wharton Business School from the University of Pennsylvania. If you complete the curriculum on the proposed timeline (~2 hours per week of work), it should take about 4 months, though you could blitz through it on a break in far less. We noticed each week only had about 2 combined hours of video and reading materials and one assignment. This means each week is about 3-4 hours of work.While $79/month may seem steep, compared to a degree or bootcamp this college-credited micro-certification is a steal!

If you have a learning budget at your current company, or are dedicated to upskilling your career into something analytics related– we recommend completing the program and getting the shareable certificate (GET RECEIPTS!). This will help make your Linkedin and resume 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.

We recommend auditing the one of the courses-- they're semi-independent--to decide if this program and the professors match your learning style. To audit, simply click "Enroll for Free" and click "Audit" on the bottom of the second step. Voila, you're in!

This course is fairly new and dropped in Q4 of 2021. There have been massive advances in the AI space since then with the introduction of ChatGPT and Bard, but the concepts and applications remain the same. Most of the reviews focused on how applicable the examples were to their current environments, which is something we love to see.

Each of the four course specializations was ranked about 4.6 stars, which also was commendable. Wharton does know how to put on a great program.

Some of our favorite positive review points:

• "A very thorough introduction to application of AI and ML in a business context. I completed the Stanford Machine Learning Specialization, which is quite technical and math-heavy, and this course was a perfect follow-on to put this learning in a real-world context. Easy to follow course content, accessible, well done." - Jon S.
• "The course on AI Strategy and Governance was particularly insightful. It covered AI economics, the need to establish an AI portfolio of quick wins and long-term projects, and aspects such as [relevant] software, skills, production innovation, infrastructure, decentralization vs centralization, and AI risk management. I really enjoyed this program." [shortened] - Julio R.
  

Aggregations of negative review points:

• Not enough real-life examples (we only saw 1 of these points out of all the reviews surveyed)
• Peer reviewed assignments leave lots to be desired (a popular pain-point with Coursera-hosted programs... this is how they keep the specializations affordable!)

... and our favorite overall review:

If you are familiar with high--level AI concepts and want a bit more detail to understand the application and the operations of machine learning, this is a great course.

The concepts build nicely from start to finish, the professors did an excellent job of explaining the material and the slides were a great visual supplement. This should all be basic blocking and tackling when teaching - but it's not.

I've taken other courses offered by higher education institutions and the "teaching" tends to be just a compilation of different reading exercises and random video interviews or discussions with professors - the material may have been interesting and informative, but it wasn't oriented to teaching.

With this course, I didn't have to work hard to grasp the basic concepts and instead had brain space to think more deeply about what I was hearing.
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-Ruthanne S.

This course from Wharton and UPenn is great as a showable certification, but may not teach you everything you need to know to get a role in an AI-adjacent field. Here are some other fabulous programs in the space:

For the Analytical Professional: 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. 

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For Product Managers: Duke University's AI Product Management

Both Duke and UVA are incredibly prestigious organizations working to increase public knowledge of product management. This is a mid-level course-- so have some basic knowledge under your belt first-- focused on product management in the realm of artificial intelligence. Read our full writeup here. It's also free to audit, but if you want the certificate (recommended), it's $79 a month to complete at your own pace.

For the Deep Reader: fast.ai Practical Data Ethics

Fast.ai is a great resource for those interested in the field-- and better yet, it's totally free. The learning program around data ethics was wonderfully informative, and came equipped with recommended reading and watching that felt relevant and thoughtful.

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.

University of Pennsylvania Business Analytics Specialization from Wharton is a great way for beginners to learn the basic areas of analytics. If you're looking for a way to dabble between different analytical worlds to see what's best for you, this could be a great option!
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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!