Design thinking is taught as a systematic approach to innovation and creative problem-solving that can be used in many industries. In this course, students are introduced to resourceful problem discovery through methods of noticing, observing, and analyzing data. Using a combination of the lean startup methodology and design thinking, students conduct experiments as well as interviews, and test prototypes to verify the viability of proposed solutions. Further, ideation models are applied to conceptualize new-to-the-world technologies, business models, products, and services as potential business ventures. Students will become comfortable with ambiguity, experimentation, and online collaboration.

This course provides an introduction to machine learning and artificial intelligence. Since unstructured data comprises a large and important source of information, students cover deep learning techniques to tackle complex problems involving images, text, and recommendation systems. After taking this course, students will: (1) understand the basics of machine learning and its application to business problems, (2) be able to apply insights to complex AI projects, and (3) understand the pros and cons of different techniques and their preferred business application scenarios.

This experiential course is treated as a mini-accelerator. The class teaches “the entrepreneurial mindset” as well as how to apply that to launch and scale a business, develop solutions to business challenges, or create new products and services that result in growth opportunities for an organization. Students explore a rigorous and disciplined approach to validating product market fit, building teams, developing viable business models, and crafting messaging and pitches that resonate with the audience. Students apply these learnings on a remote team project. The challenge? Launching and scaling a business or solution, ideally generating revenue before the end of the semester. Every few weeks, students convene in the classroom to receive feedback and coaching on their approaches, results, and future experiments.

Students will develop skills for applying modern machine learning methods and other analytics techniques (such as Python programming) to address practical problems encountered in asset management, corporation finance, and financial institution settings. The techniques include tree regression, neutral network, textual analysis, web scraping, data visualization, and model assessment and selection using cross-validation.

Design thinking is taught as a systematic approach to innovation and creative problem-solving that can be used in many industries. In this course, students are introduced to resourceful problem discovery through methods of noticing, observing, and analyzing data. Using a combination of the lean startup methodology and design thinking, students conduct experiments as well as interviews, and test prototypes to verify the viability of proposed solutions. Further, ideation models are applied to conceptualize new-to-the-world technologies, business models, products, and services as potential business ventures. Students will become comfortable with ambiguity, experimentation, and online collaboration.

Innovation and creativity are at the heart of business development and profitable growth. Students explore topics including trend analysis, data visualization, market prediction, design thinking, and prototype development. At the conclusion of this course, students will be able to: (1) analyze data to gain insights, recognize patterns, and discover trends for use as innovation catalysts, (2) integrate the appropriate types of innovation into new and existing ventures, (3) develop, lead, and manage innovation teams, (4) apply creativity techniques and design thinking to solve problems, ideate, prototype, and test product/service ideas, and (5) apply innovation techniques across organizational settings and in decision making processes.

This course focuses on three themes: (1) self-awareness and self-efficacy, (2) critical analysis of the economic and social effects of women in the workforce, and (3) an exploration of how self-awareness, interpersonal influence, commitment, goal setting, and power are necessary for effective entrepreneurship. After completing this course, students should be able to: (1) analyze data, trends, causes, and opportunities for women in various roles and ranks in business, (2) develop knowledge of current literature on women in leadership and apply that knowledge to communicate and act more effectively, (3) develop practical skills, tools, and solutions for effective leadership, (4) gain self-understanding through assessment tests (i.e., Herrmann Brain Dominance Instrument), and (5) develop a personal leadership strategy for the future.

Students will develop skills for applying modern machine learning methods and other analytics techniques (such as Python programming) to address practical problems encountered in asset management, corporation finance, and financial institution settings. The techniques include tree regression, neutral network, textual analysis, web scraping, data visualization, and model assessment and selection using cross-validation.

Design thinking is taught as a systematic approach to innovation and creative problem-solving that can be used in many industries. In this course, students are introduced to resourceful problem discovery through methods of noticing, observing, and analyzing data. Using a combination of the lean startup methodology and design thinking, students conduct experiments as well as interviews, and test prototypes to verify the viability of proposed solutions. Further, ideation models are applied to conceptualize new-to-the-world technologies, business models, products, and services as potential business ventures. Students will become comfortable with ambiguity, experimentation, and online collaboration.

This course provides an introduction to blockchain technology and its disruptive role in sectors such as banking, payments, securities exchanges, real estate, insurance, supply chains, healthcare, and media. Topics include fundamentals of blockchain technology, applications and use cases of blockchain in different industries, implications of blockchain on business practices and regulation, cryptocurrencies, initial coin offerings, blockchain platforms (Ethereum, Quorum, Corda), smart contracts, and web-based decentralized applications.

This experiential course is treated as a mini-accelerator. The class teaches “the entrepreneurial mindset” as well as how to apply that to launch and scale a business, develop solutions to business challenges, or create new products and services that result in growth opportunities for an organization. Students explore a rigorous and disciplined approach to validating product market fit, building teams, developing viable business models, and crafting messaging and pitches that resonate with the audience. Students apply these learnings on a remote team project. The challenge? Launching and scaling a business or solution, ideally generating revenue before the end of the semester. Every few weeks, students convene in the classroom to receive feedback and coaching on their approaches, results, and future experiments.

Through a blend of lectures, case discussions, guest speakers, and student presentations, students explore topics such as the Internet of Things (IoT) in insurance markets, automated underwriting, blockchain for claims processing and automation, big data/machine learning in actuarial science and related ethical issues, and telematics. By the end of the course, students will learn to: (1) describe a variety of applications of technology to the insurance industry, (2) think critically about the viability of each of these innovations, and identify threats and opportunities, and (3) discuss insurtech concepts confidently and expertly in front of a variety of audiences.