Weill Medical College of Cornell University · Courses
eCornell CEEM
41 courses with the subject eCornell CEEM, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
eCornell CEEM500Probability Analysis0
The world is full of possibilities, but how likely are any of them to occur? Immerse yourself in probability theory and discover how to evaluate the likelihood of different outcomes in a variety of real-life circumstances. You will get started by examining the different methods for describing and representing data, including the basic axioms that form the core of probability theory. You will then explore real-world applications and discover how engineers and scientists use random variables and associated analytical functions to quantify the likelihood of various occurrences in experiments and processes. You will gain experience using discrete random variables, or when the number of possible outcomes can be counted, as well as continuous random variables, or when the number of possible outcomes is infinite. You will also explore data transformation and several kinds of probability distributions, including ones that assess the joint probability of two or more related variables. Toward the end of the program, you will apply what you have studied and investigate ways of modeling and predicting variability and patterns in data through the use of ordered collections and graphical representations, including normal, lognormal, Gumbel, and Weibull distributions. Note: You will need a working knowledge of algebra and univariate calculus prior to enrolling and will need to get access to the course's textbook, the ninth edition of Probability and Statistics for Engineering and the Sciences by Jay Devore. We suggest you obtain the book prior to the start of the course and consider the interactive eTextbook version, available for purchase online or from other retailers.
An enterprise with an innovation culture doesn't just happen. You must plan for both financial success and cultural change. There are several types of and approaches to innovation. How do you create an innovation strategy for your enterprise? In this course, you will begin to create a roadmap called the innovation placemat. You will identify your organization's goals and align your innovation strategy to it. You will cultivate an executive champion and set SMART goals for your new product, service, or technology. You will identify risks and barriers to deployment and create mitigation plans to overcome them. Along the way, you will hear case studies of organizations large and small, private and government, established and startup, and in many domains who have successfully established an innovation strategy with sustainable positive effects on their bottom lines.
To create and sustain a culture of innovation and entrepreneurship in your organization, it is helpful to establish an environment that supports certain mindsets. And these mindsets can create first a culture change in your organization, often followed by a higher financial return on investment. These mindsets are the competencies that convert ideas to impact. In this course, you will learn about and apply three key innovation competencies: lean startup, maker culture, and design thinking. Each of these competencies are used by large and small organizations, resulting in new products and services and satisfied employees and customers. Lean thinking is a form of customer discovery where you will develop a series of hypotheses and then test them. Maker culture is based on the do-it-yourself ethos and can help you prototype and test products quickly, reducing time to market. Design thinking is a process of empathetically listening to and then co-designing with your customers. While the three competencies have some overlapping methodology, one or two of them will best support your innovation strategy and tie in more effectively with your organization's overall strategy. The following course is required to be completed before taking this course: Developing Innovation Strategy
There are many exciting tools you can use to implement innovation at your organization. These tools are the hammer and nails of innovation. In this course, you will learn about 14 innovation tools. You will also see how other organizations have used them to successfully increase cultural and financial ROI, please customers, and improve operational efficiencies. These tools range from simpler activities such as conducting employee training, hosting hackathons, and implementing design sprints to more complex methods such as establishing an external incubator, founding a center of excellence, and acquiring another company. You will then further iterate your innovation placemat. The following courses are required to be completed before taking this course: Developing Innovation Strategy, Building Innovation Competencies
So far you have created an innovation strategy and established a vision, SMART goals, and outcome measures. You've identified competencies such as lean startup, makerspace, and design thinking, and selected tools to build an innovation culture. Now you will learn how to implement your strategy. After you map key internal stakeholders, you will devise a campaign plan for your strategy and build a dedicated team. You will understand the different motivations of your innovation shop and the performance engine and learn to work effectively with performance engine team members. You will further build out your innovation placemat with your implementation plan, identifying policies that can enhance innovation at your organization. The following courses are required to be completed before taking this course: Developing Innovation Strategy, Building Innovation Competencies, Innovation Tools
eCornell CEEM535Evaluating and Scaling Innovation0
In this course, you will devise a strategy to manage a portfolio of innovation projects at your organization. You will examine best practices for portfolio management and establish a plan to spread your innovation and innovation culture. Then you will examine typical risks to your scaling strategy and establish a sustainment plan. Finally, you will revise your innovation placemat and present a practice pitch. This activity will prepare you to pitch your innovation placemat at your organization. The following courses are required to be completed before taking this course: Developing Innovation Strategy, Building Innovation Competencies, Innovation Tools, Implementing Innovation
As a person working in a technical field, you most likely have to give talks or presentations to different audiences, some of whom will be non-experts or decision makers outside your sphere of expertise. In this course, you will look at your own current practices for giving talks as a technical expert and establish a set of action items for improvement. You will look closely at your old habits and establish new ways to design, build, and deliver effective in-person and virtual presentations for an array of internal and external audiences. You will also examine, reevaluate, and apply best practices for engineering, technical, and scientific presentations, and explore current and past presentation practices that do not achieve the intended goals and results. This examination will help you refine your ability to deliver an effective message. Specifically, you will perform an assessment of presentation techniques, both personal and at your workplace. From that starting point, you can reengineer your presentations to meet specific technical communication needs. Most importantly, you will have an opportunity to discover and articulate your organization's presentation culture, identify areas for improvement in your own slide design and presentation skills, set goals for leveraging effective new presentation habits, and formulate a detailed action plan for improvement. Throughout this course, you will read various selections from Dr. Traci Nathans-Kelly's book "Slide Rules," which contains helpful insights and examples that you can apply in your own presentations. Furthermore, you will prepare for a specific work-related talk and define the needs of co-presenters, audience members, subject matter experts, and other stakeholders.
Since the advent of presentation software like PowerPoint, presenters have been led astray by the design of slide templates. The software applications were originally created to help presenters outline their talks, but the slideware's design did not account for the needs of audiences nor factor in cognitive research. As a consequence, the templates have ingrained poor presentation habits that often confuse and disengage the people who are meant to benefit from these talks. In this course you will have an opportunity to begin challenging the norm and break this cycle of "slide deck drudgery." By replacing old presentation habits with new best practices that you gain from this course, you can shift your focus to the needs and interests of your audience, and you can begin to use your slides to communicate your ideas more clearly and effectively. You will explore new techniques that will help you to improve the flow of your talk and keep your audience focused on your main ideas. You will then study effective presentation design and development practices as you read various selections from Dr. Traci Nathans-Kelly's book "Slide Rules," which contains valuable insights and examples that you can apply in your own presentations. It is recommended to only take this course if you have completed "Redesigning Slides for Impact" or have equivalent experience.
eCornell CEEM573Designing Slides for Live and Legacy Use0
When giving a presentation, you want to ensure you communicate all of your critical ideas while you have your audience's attention. There are more effective ways of doing so beyond the standard large amounts of text and bullet points. In this course, you will have the opportunity to rethink the way you design your presentations and slides. You will discover that there are straightforward ways to use your slide decks to serve two purposes: support your technical and business presentations while making your slide decks reusable and valuable resources inside your organization. You will then examine the life cycle of your presentations and begin to document who uses your slides, when they are used, and what clearances are needed to share and use them. You will also consider legal issues or proprietary concerns that may exist. Finally, you will start to build a process to help you protect proprietary information before you share it with external parties. As part of your study, you will review various selections from Dr. Traci Nathans-Kelly's book "Slide Rules," which provides helpful insights and enlightening examples that you can apply in your own presentations. It is recommended to only take this course if you have completed "Redesigning Slides for Impact" and "Engaging Presentation Techniques," or have equivalent experience.
eCornell CEEM574Strategizing for Audiences With Different Expertise0
Your work in a technical field likely means that you periodically interact with colleagues, customers, suppliers, and other stakeholders who live in a different part of the world, speak a primary language different from your own, or have expertise in a different or non-technical field. As a technical expert, your ability to anticipate the needs of audiences from diverse backgrounds and communicate effectively with them is essential. In this course, you will have an opportunity to explore how you can prepare to meet the needs of audiences with differing backgrounds, primary languages, and levels of expertise, and even varying degrees of receptivity to your message. You will examine principles of persuasion and consider how and when to apply them both effectively and ethically. As part of your studies, you will also review pertinent selections from Dr. Traci Nathans-Kelly's book "Slide Rules," and you will look at how you can prepare for the unexpected in your talks and maintain your composure when disruptions occur. By the end of this course, you will have gained techniques and insights that you can apply as you prepare and develop presentations for a wide range of audiences with varying needs and interests. It is recommended to only take this course if you have completed "Redesigning Slides for Impact," "Engaging Presentation Techniques," and "Designing Slides for Live and Legacy Use," or have equivalent experience.
eCornell CEEM575Projecting Expertise and Confidence During Presentations0
Effective communication skills are the hallmark of a professional no matter what the profession. In this course, you will begin to parse the ways in which you present yourself to others as a technical expert. In any presentation or talk that you give, whether face to face or online, you want to project expertise, confidence, and professionalism. The idea of professionalism, however, can vary. Everyone expects professionalism, but the definition and perception of professionalism differs greatly among individuals, organizations, nations, and cultures. In this course, you will delve into the concept of professionalism in presentations and explore how it varies depending upon the context, the participants, and their expectations. You will study effective practices for designing, developing, and delivering professional-grade online meetings and team presentations. Additionally, you will have an opportunity to examine the importance of your body language, eye contact, and voice in projecting confidence in your talks, regardless of the setting or delivery medium. You will then record a talk and take an inventory of your expert presence in the video. You will also investigate the nuances and complexities of developing and delivering team presentations. In the final part of the course, you will complete the process of preparing to deliver a professional-level team talk. It is recommended to only take this course if you have completed "Redesigning Slides for Impact," "Engaging Presentation Techniques," "Designing Slides for Live and Legacy Use," and "Strategizing for Audiences With Different Expertise," or have equivalent experience.
eCornell CEEM576Constructing High-Impact Content for Global Delivery0
Thanks to ongoing advances in communication technologies, you can collaborate in real time with colleagues, customers, suppliers, investors, and other stakeholders around the world. That reality presents both exciting opportunities and potential challenges. You can share ideas and receive information with great ease, but your message could encounter cultural and language barriers as you communicate with diverse audiences. In this course, you will explore effective ways to deliver high-impact content that meets the needs of multinational audiences in live, online, and face-to-face presentations. Using practical insights studied in this course, you will strategize and prepare for high-impact presentations in formal settings. You will scrutinize how to design and adapt your slides and delivery techniques to meet the needs of international audiences speaking multiple languages. You will also see how you can leverage your slide presentations to create custom videos that address the needs of your target audience. It is recommended to only take this course if you have completed "Redesigning Slides for Impact," "Engaging Presentation Techniques," "Designing Slides for Live and Legacy Use," "Strategizing for Audiences With Different Expertise," and "Projecting Expertise and Confidence During Presentations," or have equivalent experience.
By some estimates, 90% of the data that has ever existed has been created in the last two years. This is a staggering figure and has given rise to new challenges and opportunities in almost every industry: What kind of data do you need to collect to compete, and how can you make sense of it once you have collected it? As technology evolves and the volume of data increases, how can you make the best use of all this information? How can you use the data to help drive your decision-making? How can you make data work for you? How can you ensure your data accurately reflects the population in which you're interested? In this course, you will determine the types of engineering and business questions you can answer, the kinds of problems you can solve, and the decisions you can make, all through using data analytics. You will explore best practices for collecting information so that you can make informed predictions, develop insights, and better inform organizational decision-making. You will see real-world examples that demonstrate how those tools work. Additionally, you will have a chance to apply some of the concepts to your own work. You will explore best practices for sampling and examine how different types of sampling are suited for different situations. Finally, you will see real-world examples that demonstrate how those tools work and have a chance to practice sampling techniques in some case-study scenarios.
eCornell CEEM582Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis0
Visualization is one of the most simple and effective ways to find patterns in data. These patterns include: What is the general range and shape of the data set? Are there any clusters of observations? Which variables correlate with each other? Are there any obvious outliers? As your data set grows in terms of the number of data points and variables, however, it becomes increasingly difficult to visualize all this information at once. At most, you can plot data points on a three-dimensional axis and add further distinctions of size, color, shape, and so on. Yet this can easily become too busy and difficult to read. How, then, do we find patterns in really big data sets? In this course, you will explore several powerful and commonly utilized techniques for distilling patterns from data. You will implement each of these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible for you in your own work. Understanding Data Analytics
eCornell CEEM583Finding Patterns in Data Using Cluster and Hotspot Analysis0
When you have large groups of objects, it is often helpful to split them into meaningful groups or clusters. One example of this would be to identify different types of customers so that a company can more efficiently route their calls to a helpline. As a second example, suppose an automobile manufacturer wanted to segment their market to target the ads more carefully. One approach might be to take a database of recent car sales, including the social demographics associated with each customer, and segment the population purchasing each type of automobile into meaningful groups. Specialized approaches exist if your data contains information that relates to time and geography. You can use this additional information to identify geographical and temporal hotspots. Hotspots are regions of high activity or a high value of a particular variable. These results can help you focus your attention on a particular region where a problem is occurring more than usual, such as the incidence of asthma in a large city. In both cluster and hotspot analysis, the results can help you discover new and interesting features, problems, and red flags regarding the data being analyzed. In this course, you will explore several powerful and commonly utilized techniques for performing both cluster and hotspot analysis. You will implement these techniques using the free and open-source statistical programming language R with real-world datasets. The focus will be on making these methods accessible and applicable to your work. Understanding Data Analytics Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
eCornell CEEM584Regression Analysis and Discrete Choice Models0
A story can play an important role in understanding data. It can help distill complex information into something manageable- something we can think about easily, relate to, and use to make decisions. For many problems that we encounter globally, however, a story that describes what already happened is not enough precision for the job we want to perform. Often, we would like to use available data to make numerically accurate predictions about what might happen in the future. This task requires the construction of mathematical models that are well suited to our real-world problems. In this course, you will explore several types of statistical models used with data to make predictions. These models bring with them a whole batch of important concerns, such as estimation and validation, that make the entire process into both an art and a science. You will implement each of these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible for you in your own work. Understanding Data Analytics Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis Finding Patterns in Data Using Cluster and Hotspot Analysis
Supervised learning is a general term for any machine learning technique that attempts to discover the relationship between a data set and some associated labels for prediction. In regression, the labels are continuous numbers. This course will focus on classification, where the labels are taken from a finite set of numbers or characters. The prototypical and perhaps most well-known example of classification is image recognition. The goal is to take an image (represented by its pixel values) and determine what objects are in the image. Is it a dog? A grapefruit? A stop sign? There are many practical classification tasks, such as determining whether an individual's financial history makes them high risk for a loan, whether there is a defect in a material based on some sensor readings, or whether a new email is spam or not. These problems share the same basic form and can be solved with many different types of mathematical, statistical, and probabilistic models developed by the machine learning community. In this course, you will explore several powerful and commonly utilized techniques for supervised learning. You will implement each of these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible for you in your own work. Understanding Data Analytics Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis Finding Patterns in Data Using Cluster and Hotspot Analysis Regression Analysis and Discrete Choice Models
eCornell CEEM586Neural Networks and Machine Learning0
Neural networks, a nonlinear supervised learning modeling tool, have become hugely popular within the last two decades because they have been successfully applied to a wide range of problems, including automatic language processing, image classification, object detection, speech recognition, and pattern recognition. They are mathematical models that are loosely built up based on an analogy to the interconnected neuron in the brain. They take in a vector or matrix of input data and output either a classification value or an approximation to a functional value. The beauty is that the relationships between the inputs and outputs can be highly non-linear and complex. In this course, you will explore the mechanics of neural networks and the intricacies involved in fitting them to data for prediction. Using packages in the free and open-source statistical programming language R with real-world data sets, you will implement these techniques. The focus will be on making these methods accessible for you in your own work. Understanding Data Analytics Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis Finding Patterns in Data Using Cluster and Hotspot Analysis Regression Analysis and Discrete Choice Models Supervised Learning Techniques
Many real-world decision situations are complicated by uncertainty, complexity, and competing objectives. When you begin to frame and analyze a complex decision, you quickly realize: Defining the problem is the problem. In this course, you will explore the concepts and tools you'll need for framing and analyzing complex decisions. You will define and frame the key components of a problem and identify the values that will inform your decision. You will then begin to map your values to your objectives with applicable examples. Finally, you will apply decision analysis to a wicked problem. Overall, you will examine how your unique perspective can alter how you frame and make complex decisions.
As you begin to frame your decisions, it's important to organize the problem in a manner that allows for the proper examination of solutions and their varying impacts. At this point, you can begin to incorporate the use of decision trees to meet that goal. What are the various kinds of decisions you can make? How is the impact of each decision path weighted and differently valued? Constructing decision trees will provide you with some answers to these questions. In this course, you will explore the aspects of decision trees and when to best incorporate them into your decision analysis. You will map how performance measures may influence different sequences and outcomes. You will calculate risk profiles, develop modeling measures, and decide on the proper usage of decision trees in discrete choice situations. While decision trees are commonly and widely used, they won't always be the right approach; you will explore in detail why that is and when it can make the most sense in given situations.
eCornell CEEM593Accounting for Subjectivity and Valuing Information0
When you conduct your quantitative analysis, the fact that it is quantitative may seem to imply that the data is objectively set in a definitive manner and not suited for subjective analysis. In reality, quantifiable metrics include subjectivity at several points, including how the problem is approached and how you elicit the observations of knowledgeable individuals. It may surprise you, but you will often encounter subjectivity while you're trying to frame a decision process that is objective. Many times you'll discover that the questions that you're asking, the types of probabilities you need, and other information won't involve perfectly clear observations. Making a decision, therefore, isn't always as simple as picking the "best" quantitative option. When you consider potential solutions, how does the elicitation of subjective data come into play? In this course, you will analyze the processes and theories you must consider as you begin to explore the subjectivity of objectivity in decision making and how they are related. You can potentially elicit subjective expertise to get a sense of the demand for a certain approach. You can also think about the overall riskiness of your potential choice as you deliberate over options. And there are biases and heuristics that may affect your decision. This all points to the importance of understanding the value of information and how it might influence your decisions. Evaluating how the subjectivity of objectivity can affect how you view options will provide added context and tools for decision making. These elements will help guide you as you further consider how to frame your choices.
eCornell CEEM594Risk Attitudes and Key Assumptions in Utility Theory0
Making a decision often isn't as simple as choosing between Choice 1 and Choice 2 because one option is clearly better than the other. In the real world, not only are there complexities regarding financial costs, but there are deeper considerations around risk. Adding risk into the equation then requires considering whether the more profitable option is not worth the risk, and adding utility theory allows for the proper modeling of these complex choices. Risk and other considerations can subsequently be placed into a decision tree for further evaluation. You have explored how subjective probabilities and the value of information can be integrated into your decision analysis. In this course, you will examine how risk attitudes and utility theory also impact your decision analysis. You will identify how risk attitudes are related to several axioms and paradoxes. You will also use utility theory to devise a model for quantifying subjective inputs to a decision then apply these additional considerations to a decision tree framework. Risk and utility theories have the potential to totally change how you may have originally framed your decision, but this fuller picture provides invaluable insight into how you should make your decisions. It will also offer clearer context for when you incorporate more complicated analysis tools into your analysis process.
In complex engineered systems and design processes, you do not have the luxury of having fully defined decision alternatives with a clear mapping of their performance trade-offs or consequences. As you transition from classical discrete-choice problems, problem structuring and evaluation tools must become more advanced in their ability to explore large design problems while providing innovative decision analytical tools for helping to clearly map possibilities, their trade-offs, and key consequences. In this course, you will discover how to formulate multi-objective design problems and more formally consider their trade-offs using the concepts of non-dominance, Pareto optimality, and a posteriori decision support.
eCornell CEEM596Modeling Design Decisions and Performance0
As computational simulation becomes more commonplace in design and decision processes, it is important for you to consider how uncertainty could be impacting your perceptions of performance, trade-offs, and consequences. A single simulation can be seen as mapping from your design alternative to its performance objectives based on a single set of assumptions and choices used in the model's representation of the system of interest. Monte Carlo simulation can be thought of as accounting for uncertainties in your modeling assumptions and choices where you can simulate performance if your design resides in many different but plausible alternative worlds (i.e., many states of the world). In this course, you will broaden the types of performance measures that can be used in your decision framings to include risks and vulnerabilities. You will assess the value of Monte Carlo simulation in better understanding the sensitivities, risks, and consequences of your candidate design alternatives. You will also explore the emerging insights and analytics associated with decision making under deep uncertainty. Given the many ways that our decisions shape concerns surrounding people, profit, and planet, finding solutions that maintain acceptable performance across many plausible futures then explicitly mapping their vulnerabilities is extremely valuable.
eCornell CEEM597Designing for Resilience and Robustness0
Decision making for complex systems often necessitates the modeling of system dynamics, optimization across multiple conflicting objectives, the analysis of uncertainty, and the visual analysis of performance. The depth of analysis is often limited by the tools available to decision makers. Recently, however, a number of software packages have been developed and deployed specifically to aid in decision making for complex systems. In this course, you will explore the latest options in real-world decision making in the face of uncertainty. You will also use the open source Python library Rhodium to examine the shallow lake problem by testing multiple problem formulations, examining trade-offs between conflicting objectives, and discovering consequential combinations of uncertainty. In your final project, you will examine your decisions in simulated situations of uncertainty.
A foundation in risk analysis is a crucial skill set in today's business marketplace. When you can understand risk and apply strategies to manage it, you set yourself up for success in an ever-changing world. In this course, you will explore key risk evaluation strategies and tools. You will begin by delving into the basics of risk analysis, studying hazards, risk management strategies, and vulnerability identification. You will then broaden your focus to visualizing and estimating risk, discussing models such as risk matrices that categorize risks by likelihood and consequence. Finally, you will analyze the causes and effects of adverse events, constructing event trees and performing fault tree analysis using practical tools like Python and R. By the end of this course, you will have a firm grasp of risk analysis and evaluation, including how to assess the likelihood and effects of adverse events.
Evaluating risk can feel difficult without the proper statistical techniques in your toolbelt. To give yourself that foundation, you will need to understand both the concepts and the tools involved in the process. In this course, you will walk through the process of risk assessment using several statistical techniques. You will begin by learning to monitor and analyze variations to ensure product or service quality. You will then practice evaluating risk using discrete distributions and summarizing findings using common statistical techniques for risk analysis, including skewness and distributions such as log-normal distributions. You will also gain familiarity with critical functions and concepts in risk analysis, including power law functions and distributions, semi-variance, volatility and beta, and value at risk (VaR), a financial metric used to assess potential asset or portfolio losses. Finally, you will examine design methodologies geared toward improving product reliability, including design for reliability, design for Six Sigma, and quality control. By the end of this course, you will be well-equipped to analyze and evaluate risk using numerous statistical methods. Risk Analysis Foundations
Once you have a sense of risk for a situation, how can you model that risk using the most common tools of the industry? In this course, you will assess a given situation, select the appropriate modeling tools, and perform a risk analysis. You will begin by identifying the attributes that make a model strong, providing you with a blueprint for creating user-friendly and reliable models for risk analysis. You will explore the most commonly used risk analysis tools, including simulation, fault trees, event trees, Bayesian networks, and statistical tools. Your examination of simulation tools will have you quantifying risk by creating a Monte Carlo simulation and constructing a Bayesian network. Finally, you will discuss mitigation, diversification, and transfer of risk as well as how to pick the correct combination for a given situation. By the end of this course, you will have the necessary foundation to model risk and apply strategies to set you up for success. Risk Analysis Foundations Risk Evaluation
Reducing risk in business and life is a goal for many of us, but how can we effectively assess risk in a world where everyone's perceptions are so different? In this course, you will explore risk perception, which describes how each person views and measures the nature and severity of danger. Unlike the numerical depiction of risks, risk perception is influenced by personal experiences, mental activities, context, and inherent personality traits. You will discuss the effects of risk perception on risk evaluation and decision making. You will then explore models such as expected utility, bounded rationality, and prospect theory, giving you a foundation in the elements of risk perception. Finally, you will investigate cognitive shortcuts and biases, including the availability heuristic, anchoring and adjustment, confirmation bias, and overconfidence, and determine their repercussions on behavior. These skills will give you the tools to examine the risk perceptions of various stakeholders, address cognitive biases, and identify suitable models for decision making under uncertainty. By the end of this course, you will be equipped with the methods and insights necessary to effectively evaluate and manage risks, even in complicated situations. Risk Analysis Foundations Risk Evaluation Risk Modeling
In this course, you will explore the intricate relationship between the product manager, the product, and the customer. You'll begin by examining the foundational aspects of product management, including the nature of products, the skills required for effective management, and the common causes of product failures. You'll also gain insights into the product management process, learning to differentiate your role as a product manager from the tasks involved in managing products. Through the course materials and activities, you will engage with the CORE approach and the Analytical Pyramid, essential tools for classifying ideation skills and navigating the product management journey. You'll then delve into the product development process, exploring its cyclical nature and the distinction between managing and executing tasks. You'll practice identifying management-specific responsibilities and discover how to optimize product design using techniques like Kansei engineering. Finally, you will gain hands-on experience using Python for data manipulation and A/B testing. You'll discover how to measure customer preferences, leverage data to guide marketing strategies, and develop a deeper understanding of customer perspectives. This practical experience will prepare you to manage the product life cycle effectively and drive success in the market.
eCornell CEEM622Developing a Data-Driven Product Strategy0
In this course, you will discover how to leverage data to develop and refine product strategies a skill that is increasingly crucial in today's data-driven market. You'll start by exploring essential marketing metrics to design impactful customer journeys then use these metrics to optimize advertising strategies. You'll practice distinguishing between KPIs and attribution metrics to make informed marketing decisions, gather and interpret environmental data to contextualize your product strategies, and integrate these strategies into a company's broader marketing efforts. Throughout the course, you will delve into various topics, including global ad spend, the marketing funnel, and advertising effectiveness metrics. You'll engage in practical exercises such as journey mapping, competitor briefs, and calculating key marketing metrics like cost per click (CPC) and return on ad spend (ROAS). You'll also be introduced to advanced concepts like marketing mix modeling (MMM) and multi-touch attribution (MTA), equipping you with the knowledge and skills to interpret complex data and guide product and marketing strategies. By the end of the course, you will have a comprehensive understanding of how to synthesize data-driven insights to develop and implement effective product strategies within an organization, giving you the confidence and expertise to excel in your role.
eCornell CEEM623Optimizing Product Strategy Using Analytics0
This course equips you with the analytical tools and techniques needed to create data-driven strategies that resonate with your audience. Through a combination of interactive learning, an engaging case study, and hands-on coding exercises, you will explore fundamental ideas such as survival analysis, customer lifetime value, and sentiment analysis, which are essential for understanding customer behavior, predicting outcomes, and tailoring product strategies to meet consumer needs. These approaches will enable you to make informed decisions, refine product offerings, and create meaningful connections with your customers, ultimately driving engagement and loyalty. You will then apply these analytical techniques to your own product environment. You'll use real data to discern customer preferences, analyze product features, and determine optimal strategies for improving product performance. By the end of the course, you'll have developed a comprehensive strategy that leverages consumer insights to enhance product value and drive customer loyalty.
eCornell CEEM624Analyzing and Communicating Consumer Insights0
In this course, you will explore the fascinating world of consumer behavior, discovering what drives customers and how to communicate those insights effectively. You'll start by analyzing motivations and internal influences on consumer choices, gaining a deep understanding of how and why customers make decisions. Through interactive activities and discussions, you'll explore real-world examples and apply these concepts to identify key behavioral implications. This knowledge will lay the foundation for formulating product specifications that are both insightful and actionable. Throughout the course, you will develop critical communication skills to present your insights in a way that influences decision making. You'll get hands-on experience analyzing consumer motivations and presenting data-driven insights in meaningful ways. You'll work on formulating product specifications and ultimately craft a strategy for relaunching a recalled product. By the end of the course, you'll have the skills to understand consumer behavior, communicate insights to stakeholders, and make informed product decisions that resonate with your target audience.
eCornell CEEM625Applying Project Methodologies to Technical Products0
In this course, you will discover how to apply different project management methodologies to tackle complex product challenges. You'll explore frameworks like Agile, Scrum, Kanban, and the RICE prioritization method to determine the most effective way to streamline project execution. Throughout this journey, you'll get hands-on experience selecting and implementing these methodologies in real-world product environments, allowing you to see how each approach can lead to more efficient outcomes. You will also develop persuasive communication skills, leveraging consumer behavior insights to craft effective product messaging that resonates with your audience. You'll work on streamlining a technical product's development process and applying consumer behavior models to predict decisions and create effective pricing signals. By the end of this course, you'll have the ability to manage product development with confidence, driving both consumer engagement and business success.
eCornell CEEM626Creating a Compelling Value Offering0
In this course, you will discover how to create a value offering that resonates with your customers by leveraging key economic principles and pricing strategies. You'll start by exploring financial fundamentals like discounted cash flow and the time value of money then use these tools to construct solid business cases and determine optimal pricing. Through practical activities, you'll develop pricing strategies that evolve over a product's life cycle, applying these techniques to real-world scenarios to create a sustainable and compelling value offering. As you progress, you will explore behavioral pricing techniques to ensure your product's value is effectively communicated and resonates with your target audience. You'll determine how to drive alignment and action across teams, preparing for a successful product launch. By the end of the course, you'll have the skills to set strategic pricing, understand market dynamics, and deliver a value offering that stands out in the competitive landscape.
Data is the foundation for informed decision making and process improvement with Six Sigma. In this course, you will be introduced to the essential statistical methodologies for Six Sigma Black Belt certification. To build a strong analytical foundation, you will explore core statistical techniques, such as sampling methods, data collection, probability, and distribution analysis. You will then assess measurement systems using tools like gauge repeatability and reproducibility along with other measurement system analysis techniques. Finally, you will apply these skills toward hypothesis testing, correlation, regression, and multivariate analysis, empowering you with skills to analyze and interpret complex datasets. By the end of this course, you will be positioned to select appropriate data collection methods, evaluate their effectiveness, and use statistical tools to address real-world challenges. This course requires you to complete various statistical calculations using Microsoft Excel or another tool, language, or software of your choice.
eCornell CEEM632Six Sigma Tools and Methodologies0
Whether you are leading a Six Sigma project or supporting one, mastering Six Sigma tools is crucial for successful process improvement. This course equips you to engage with the essential tools from the ASQ Six Sigma Black Belt Body of Knowledge. You will have the chance to implement tools such as bow-tie diagrams, risk matrices, event trees, fault trees, and quality function deployment. You will also apply tools such as failure mode and effect analysis, quality control charts, and design of experiments, providing a comprehensive toolkit for process improvement. Through practical exercises and case studies, you will practice and refine these methodologies in various organizational contexts, gaining confidence in driving quality and efficiency in your organization and beyond. This course requires you to complete various statistical calculations using Microsoft Excel or another tool, language, or software of your choice.
Successfully managing risk is a key factor in delivering Six Sigma projects that meet business goals. In this course, you will focus on identifying and measuring risk across various domains, including manufacturing, finance, natural disasters, and cybersecurity. You will apply tools such as failure mode and effect analysis, quality function deployment, and voice of the customer to manage and mitigate risks effectively in diverse contexts. You will focus on adapting risk management tools to specific scenarios and synthesizing common strategies across different industries. Finally, you will incorporate risk analysis into product design and production processes, enhancing quality and reducing failures. By the end of the course, you will be prepared to evaluate risk in real-world situations and formulate strategic recommendations for risk mitigation. This course requires you to complete various statistical calculations using Microsoft Excel or another tool, language, or software of your choice.
In Six Sigma projects, the ability to analyze risks and make informed decisions can determine success or failure. In this course, you will examine the impact of risk analysis on decision making within Six Sigma projects. First, you will identify domain-specific risks and use modeling techniques to demonstrate the potential outcomes of different decisions. You will then develop and implement risk mitigation plans, supporting your ability to navigate complex decision-making scenarios confidently. By the end of this course, you will understand how risk analysis influences decisions, equipping you to guide your organization in minimizing risks while maximizing opportunities. This course requires you to complete various statistical calculations using Microsoft Excel or another tool, language, or software of your choice.
Effective leadership and project management are critical for delivering successful Six Sigma projects on time and within scope. In this course, you will explore essential best practices for Six Sigma project management and leadership. You will begin by examining effective leadership strategies, analyzing different leadership styles, and integrating elements to optimize team performance and project outcomes. You will gain experience with practical skills, such as developing a work breakdown structure, creating Gantt charts, managing resources, and fostering an environment for early problem reporting. Finally, you will discover how to strategically utilize meetings, milestones, and SMART goals to drive project success. By the end of this course, you will be equipped with a comprehensive toolkit for successfully leading and managing Six Sigma projects.
Throughout this course, you will synthesize all the knowledge gained throughout the certificate program. You will have the opportunity to integrate statistical methodologies, core Six Sigma tools, leadership strategies, and project management best practices to understand their collective impact on Six Sigma project success. You will engage with practical exercises, collaborative discussions, and case studies, preparing you for the Certified Six Sigma Black Belt (CSSBB) exam and equipping you to apply Six Sigma principles in diverse professional settings. By emphasizing real-world application, you will develop a comprehensive project management plan aligned with Six Sigma methodologies, setting you up to bring these skills with you in your role and beyond.
