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Data science certificate prepares leaders for high-growth field

two hands hover over a table, which is covered in interconnected circuits. Some of the circuits are lit up orange.

With data science employment projected to grow 36% by 2033, professionals across industries are seeking ways to leverage the power of data analysis to drive decision-making. The Data Science Modeling certificate program — developed by Sumanta Basu, an associate professor at Cornell Bowers Computing and Information Science — bridges the gap between basic statistics and advanced data science applications.

The certificate program consists of four comprehensive courses: Nonlinear Regression Models, Modeling Interactions Between Predictors, Foundations of Predictive Modeling and Ensemble Methods. Participants learn to capture complex relationships in data through advanced regression techniques, transform categorical variables into meaningful predictors and build models that adapt to real-world complexities. Through hands-on practice in R programming, professionals develop practical skills in decision trees and random forests to solve challenging prediction problems.

In a recent conversation with eCornell, Basu explained how the program blends statistics and data science.

How do you help students bridge the gap between theoretical concepts and real-world applications?

“We are teaching students these concepts, but in parallel, we are also giving them very real and relevant topical examples that they can apply right away. For example, we use the data set from Tompkins County’s COVID-19 counts. We plot the number of days since the pandemic, the number of new infections and the number of hospitalizations. All of these variables change over time, so the pattern cannot be captured by a single line.

“This example points out how you can use these [non-linear regression] tools to model. And you use the data to predict and understand the evolution of the pandemic. We use a tool called spline or a piecewise polynomial, which is more advanced for capturing nonlinear relationships. This shows the differentiation and how you can use new models or new methods to improve your fit and improve your predictions.”

What’s your approach to teaching students about different types of data and their corresponding models?

“Machine learning models vary depending on the type of data you’re analyzing. The classical machine learning model is for structured data that can be organized in the form of a table. If your data is unstructured, if it’s just text, or if it’s a bunch of images or audio files or video files, then you’ll need more modern tools like deep neural networks. But as long as the data is structured and can be stored in a table format with a bunch of numbers or categories, what we do here, compared to some other courses in the machine learning world, is still state of the art in industry and built based on statistical foundations.

“We have the flexibility to pause and really get students to appreciate what each piece of this complex machinery is doing, in what way they can go wrong, how to understand the limitations and how to explain it to others in simpler terms.”

Turn statistical expertise into data science proficiency — enroll in the Data Science Modeling certificate program today!

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Digital marketing certificate helps hospitality professionals drive customer engagement

Professionals working on documents at a conference room table

As consumers increasingly rely on online resources to make travel and hospitality decisions, businesses must adapt their marketing strategies to meet customers where they are. But what constitutes a successful strategy in an increasingly saturated market?

In the Hospitality Digital Marketing certificate program, associate professor Rob Kwortnik and former clinical professor Bill Carroll — both from the Cornell Nolan School of Hotel Administration — present an approach based on foundational hospitality marketing concepts and principles designed to integrate a company’s brand communications across media channels.

“Today’s hospitality consumers make decisions across multiple digital touchpoints before they ever walk through your door. This program teaches professionals how to create and execute marketing strategies that reach guests with messaging that resonates,” Kwortnik said.

Through a detailed case study of a fictional Baltimore hotel, participants learn to develop comprehensive integrated marketing communications (IMC) strategies that create consistent cross-channel brand experiences. Courses include Marketing Hospitality Brands Through Digital Media, Implementing Brand Strategy Through Digital Media, Communicating the Brand Across Marketing Media and Success Metrics for Hospitality Digital Marketing.

“We guide participants through building a complete IMC strategy for a hotel,” Kwortnik said. “They learn to identify their target market, develop creative strategies and optimize their online presence. These are skills they can immediately apply to their own properties.”

The coursework covers shaping consumers’ brand beliefs, developing website and search engine marketing strategies and content creation for social media and online communities. Participants also explore the full suite of traditional media, including print, radio, television and public relations. Working with real-world scenarios, professionals learn to adapt to shifting market conditions and evolving media landscapes — and they understand how to measure the success of their IMC activities.

“The hospitality industry’s future belongs to professionals who understand how to connect with guests in meaningful ways through digital marketing,” Kwortnik said. “This program prepares leaders to shape that future while staying true to the fundamentals of hospitality excellence.”

Year-long, free access to the Hospitality and Marketing Symposia are available as part of the program. The sessions enable learners to engage in real-time conversations on essential hospitality industry topics and trends with professional peers and experts from the Cornell community and beyond.

Learn to lead digital evolution in hospitality marketing. Enroll in Cornell’s Hospitality Digital Marketing certificate program.

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4 AI insights for executives, corporate boards

Ai in the Boardroom Cornell Keynote. Clockwise from left: Partners at Cleary Gottlieb Steen & Hamilton LLP James Hu, Angela Dunning, Chase Kaniecki, Lillian Tsu, Benet O’Reilly and D. Bruce Hoffman.

Understanding AI’s impact and ensuring its responsible implementation is essential for board members and senior executives looking to stay ahead in today’s fast-evolving corporate landscape. In a Cornell Keynote, legal professionals from Cleary Gottlieb Steen & Hamilton LLP shared what top management needs to know about navigating the opportunities and challenges AI brings to the boardroom.

  1. Navigating intellectual property (IP) challenges.

Many generative AI tools feature a public-facing platform in which users enter text prompts to generate new works, such as text, images or songs. However, because these AI models are trained on extensive datasets, often sourced from the internet, AI may create outputs based on copyrighted material.

There are currently around two dozen IP litigation cases in the United States concerning the training and output of generative AI models, including OpenAI’s ChatGPT and Google’s Imagen. “The key question is whether courts will find that the training of these models was fair use or infringing,” explained Angela Dunning, a partner who focuses on commercial litigation.

If the courts rule that AI training methods are not fair use, the development of these tools could change significantly, potentially affecting their availability and competitiveness, especially in the international market.

“The United States Copyright Office has taken the position that AI-generated outputs are not protected under U.S. copyright law because they are not authored by a human,” said Dunning. “This is now in conflict with some jurisdictions around the world, such as the EU’s AI ACT, which may impose restrictions on the use of copyrighted content for AI training and apply them to AI models globally if their output comes into Europe.”

As companies increasingly incorporate AI technology into their workplaces, board members should ensure that their companies have robust policies in place to mitigate IP risks, such as obtaining proper licenses for training data and regularly reviewing compliance with evolving laws. Proactively addressing these challenges can help protect the company’s innovations and preserve its competitive advantage in the market.

  1. Addressing SEC disclosure requirements.

Erik Gerding, director of the SEC Division of Corporate Finance, recently released a statement signaling AI as a disclosure priority for the SEC and compelling companies to be as detailed as possible in their annual reports, including identifying how the company defines AI and how the technology could benefit the company’s operational outcomes, financial condition and growth potential.

Specificity is crucial for general counsels and legal teams submitting AI disclosures to the SEC. “The SEC is focused on making sure that disclosures public companies make on AI and the opportunities it presents are tailored to that company rather than being a generic boilerplate disclosure,” noted Lillian Tsu, a partner specializing in securities and capital markets transactions.

Tsu also highlights the importance of attending to the material risks of AI technology, as the data that AI generates are predictions, not conclusions. “In other words, the disclosure should not be divorced from what is happening in reality,” added James Hu, a partner who focuses on merger and acquisition (M&A) transactions.

By adhering to these guidelines, companies not only comply with SEC requirements but also foster greater transparency and trust with stakeholders. Comprehensive and tailored AI disclosures reflect a company’s commitment to responsible innovation, offering a clear view of how AI integration aligns with the company’s strategic goals and risk management practices. 

  1. Understanding antitrust implications.

According to D. Bruce Hoffman, a partner whose practice focuses on antitrust enforcement, AI introduces four significant issues on the antitrust front: the use of AI for collusion, unilateral conduct, mergers and compliance with the Robinson-Patman Act.

The use of AI for collusion has garnered the most attention from antitrust enforcers such as the Department of Justice and the Federal Trade Commission due to concerns that AI systems could enable competitors to coordinate pricing or other market behaviors. 

“The Department of Justice has drawn an analogy stating that AI, in this context, is no different from a person. If competing companies were to send their cost data to this person who then instructed them to charge the same price, that would constitute collusion. This can potentially lead to antitrust trouble as well as exposure to civil problems,” said Hoffman.

Another issue is the role of AI in unilateral conduct, particularly in monopolization cases. AI systems could make decisions that, while rational, could be seen as anticompetitive if they harm competitors in ways that violate Section 2 of the Sherman Act. The challenge for antitrust enforcers will be determining when an AI’s autonomous actions cross the line from aggressive competition to unlawful conduct.

  1. Leveraging AI in M&A.

AI is quickly becoming a driver of M&A activities as companies look to strengthen their competitive edge. Benet J. O’Reilly, a partner specializing in mergers and acquisitions, notes that AI is a hot investment area, with firms either developing AI in-house or acquiring third-party tools to integrate into their operations.

This surge in AI-driven M&A is fueled by the need to stay ahead in a rapidly evolving technological landscape. Companies are not only seeking innovative technologies but also the talent and expertise behind them. “Talent acquisitions are crucial because they not only bring valuable technology in-house but also prevent that talent from being available to competitors,” explained O’Reilly.

However, integrating these AI-focused teams into larger, more traditional organizations can be challenging. Companies must strike a balance between maintaining the creativity and agility of these teams while aligning them with broader corporate objectives — a process requires careful planning and a willingness to adapt. 

As AI reshapes industries, M&A deals may become more prevalent, with companies increasingly turning to strategic acquisitions to build out their AI capabilities. For executives, understanding the complexities of these transactions will be crucial.

 

For more insights on approaching AI-related developments in the boardroom, experience the full Keynote “Artificial Intelligence in the Boardroom: What Board and Senior Executives Need To Know” on the eCornell website. 

This Keynote is part of  a series of discussions leading up to eCornell’s 2025 Board of Directors Forum. Register now for the opportunity to network and share best practices on cybersecurity, supply chains, data and AI, and earn a Cornell Tech Board of Directors Forum certificate from Cornell Tech.

 

Photo: Clockwise from left: Partners at Cleary Gottlieb Steen & Hamilton LLP James Hu, Angela Dunning, Chase Kaniecki, Lillian Tsu, Benet O’Reilly and D. Bruce Hoffman.

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Certificate program primes professionals for risk analysis in business

Computer mainframe lit up by blue light. Small dots of red and yellow punctuate the rest of the mainframe with several computer chips.

Every company and venture comes with risk. In eCornell’s Risk Analysis certificate program, developed by Linda Nozick, director of the Department of Civil and Environmental Engineering at Cornell, professionals learn how individuals and businesses can avoid, mitigate, share and diversify risks. The certificate includes four key modules: risk analysis foundations, risk evaluations, risk modeling and risk perception.

In a recent conversation with eCornell, Nozick discussed how the program charts out risk in a quantitative and statistically focused manner.

How do we quantify risk?

“Measuring risk is actually really difficult. It is one thing to say: Something’s risky. But once you have to ask ‘how risky,’ you have this question of how to put that risk in context with other risks. And we can talk about this really in an interesting way, when we think about valuing human life or how we handle risk in the public sphere. We make very different decisions about investment for risk mitigation on the highway system than we do in a nuclear power plant. You see the massive difference in funding. We try to illustrate that by looking at specific application domains and statistics with understanding that probability distribution. How likely is an outcome and how bad is it?”

What is risk perception and how does risk attitude affect decision making?

“Risk has a lot to do with how people interpret things, and we don’t interpret them all the same way. And so I think it’s important in the risk space to kind of understand how your perceptions, your attitudes toward risk make you more vulnerable to risks or help you mitigate risks. Somebody who’s really risk prone doesn’t worry so much about risk, and they take more risks than their company would like them to. You are trying to understand how risk attitudes affect decision making. Attitudes actually do drive how you make choices . . . that’s really what this whole thing is about: How does your mental headspace impact your decision making when it comes to risk?”

Is this course content constrained to risk professionals?

“Not at all. This course really is agnostic with respect to the application domain. We talk about financial risk. We talk about health risks. We talk about all sorts of risks. There really is that opportunity to see the applications across different types of business professionals and roles and industries, which could really give folks interesting perspectives and a lot of fundamentals as they’re changing careers or moving up in their career.”

Equip yourself with the tools to identify risks and apply strategies that protect you and your hard work — no matter your industry — in eCornell’s Risk Analysis certificate program. Learn more and enroll now.

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Cornell Keynotes podcast: AI today – laws, ethics and protecting your work

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Understanding the ethical and legal use of AI is important for any business. In a new episode of the Cornell Keynotes podcast from eCornell and the fourth installment of the “Generative AI” series, Cornell Tech professor Karan Girotra pairs up with professor Frank Pasquale from Cornell Law School to discuss the laws and ethics of generative AI while looking at performance guarantees as well as unintended consequences and outcomes.

The conversation highlights how organizations in finance, health, education, media and manufacturing are using these technologies in clever ways and charts a path for the next generation of use cases — ones that go beyond using assistants to enhance individual productivity.

Read more on the Chronicle.

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Professionals apply techniques for digital transformation in AI certificate program

AI has broadened how companies integrate technology and digital transformation into their operations. For Karan Girotra, the Charles H. Dyson Family Professor of Management in the Cornell SC Johnson College of Business, AI prompts questions about a new wave of automation..

The AI for Digital Transformation certificate program, authored by Girotra, combines academic theory and practical executive experience in business and technology into one seamless journey through the new opportunities and potential pitfalls that AI brings. Girotra explains how businesses can utilize these new tools for success through data collection and experimentation. The program concludes with tips for encouraging a culture of learning and leadership through AI.

Girotra discussed the power of data-driven AI enhancements and digital transformation in a recent conversation with eCornell.

How essential is learning AI for professional development right now?

“That really comes down to the question: How is the world going to change [from this breakthrough]? We’ve seen from previous generations of automation that when we automate physical work, the new efficiency forms a pattern. This time, the automation of cognitive language work allows us to benefit in certain ways, including increasing productivity of individuals and organizations. People who don’t work on implementing these ideas risk being left behind and won’t reap the benefits of automation.”

How have you worked to make digital transformation, specifically with AI, accessible for a general audience?

“The program is not basic in ambition, but it is basic in style. The technical language is minimized, and [the courses] do not use jargon. In fact, there is a whole module that I call ‘Cut through the techno babble.’ So it’s designed to be extremely accessible.”

With AI constantly evolving, does this course have longevity in its application?

“Right now, there are so many hyper-specific courses in the AI boom. You have marketing with AI or trading with AI or one of a million other subspecialties. The problem with specific versions is that they change and lose their value with [any procedural innovation]. But if you learn AI more generally – what it can do for any role – then you can invent new ways to use it without copying the current ways people are using it. In a way, there’s a trade-off. When you get more narrow, AI becomes more relevant [for current issues], but it becomes less useful as the world changes. With this course, we teach the conceptual knowledge behind AI in digital transformation to let individuals chart their own procedures in a changing environment.”

Keep pace with the rapid advancements in AI and digital technologies in the AI for Digital Transformation certificate program. Learn more and enroll.

Quotes have been edited for clarity.

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Cornell Keynotes podcast: Mismanaging hybrid teams

Worker distracted by dog during virtual meeting

The shift toward hybrid work exploded during the COVID-19 pandemic and has since become a staple in all types of organizations.

Although hybrid teams can offer a number of benefits, leaders often find that the practices they have come to depend on for managing in-person teams do not translate well to the hybrid context. And with hybrid team management being the responsibility of both leaders and team members alike, where can you look for opportunities for improvement?

In a new episode of the Cornell Keynotes podcast from eCornell, professor Brad Bell, director of the Center for Advanced Human Resource Studies at Cornell’s ILR School, shares ways that hybrid teams are mismanaged and presents strategies for effectively managing hybrid teams.

Read more on the Chronicle.

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Leaders strategize for new corporate environment in Hybrid Work Strategy certificate

Woman sits in an office set up. In front of her, there is a meeting on her monitor with six people displayed.

Since 2020, organizations all over the world have shifted to hybrid work models. This change has forced leaders to reevaluate how remote collaborative processes can still drive organizational culture. Brad Bell , Donna Haeger and Theomary Karamanis , faculty authors of Cornell’s Hybrid Work Strategy Program, spoke to the eCornell team about the challenges and opportunities posed by hybrid work.

Is it possible to replicate an in-person environment in a virtual workspace?

Theomary Karamanis, Senior Lecturer, SC Johnson College of Business: “That’s the biggest mistake that organizations make: They feel that they need to simulate everything that happens organically in an in-person environment into a virtual environment, and you cannot do that. Instead, try to have less live meetings, less synchronous communication and a bit more asynchronous [work]. Live meetings should usually be limited to problem solving, creativity and conflict resolution.”

How does the hybrid work environment affect the leadership structure of an organization?

Brad Bell, Professor, ILR School: “In these semi-remote environments, leaders can’t be as hands-on, they can’t see everything that’s happening, so a lot of the leadership has to be assumed by the team members themselves in these hybrid and remote settings. This opens a gap [inside the organization’s leadership structure]. Someone that is not a leader might look at courses like [Hybrid Work Strategy] and assume they need to be in a management role to benefit from this. But without usual in-office interaction, those concepts and tools have become importantly applicable to both the leaders and the members.”

What can leaders do to ensure that their hybrid organizations are consistently productive?

Donna Haeger, Professor of Practice, SC Johnson College of Business: “Oftentimes at work, whether it’s hybrid or not, everyone’s focused on ‘task, task, task’, ‘get the work done.’ That’s become even more of a pressure because things do become disjointed when people are not colocated . . . An organization’s culture and productivity are tied together. Tasks and relationships, and the synergy between those, are what really create productivity. [Fostering that synergy] is really essential in a hybrid work environment: The research has shown that the stronger the culture, the more productive the workplace will be.”

Begin developing your confidence as a hybrid team leader with eCornell’s Hybrid Work Strategy certificate program. Learn more and enroll now.

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Justin Heitzman, an eCornell writing intern, contributed to this post.

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Cornell Keynotes podcast: Mid-year trends in generative AI tech

3D chrome brain statue, generated with AI

What are the latest breakthroughs in generative AI? What’s just noise?

In a new episode of the Cornell Keynotes podcast from eCornell, Karan Girotra, the Charles H. Dyson Family Professor of Management and professor of operations, technology and innovation at the Cornell SC Johnson College of Business and Cornell Tech, explores what’s new in the world of AI, including updates on Apple Intelligence, Anthropic and advancements in China.

Read more on the Chronicle.