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You can't understand the world today if you don't understand corruption. Every day, we see high-level government officials resigning, governments changing, hundreds of thousands of people standing in squares, holding up signs, and often risking their lives to protest the corruption in the world. It's critically important to the health of our societies to understand what people are protesting against. We want to understand corruption. This course is designed to teach you about what corruption is, what causes corruption, the effects of corruption, and how to control corruption. We'll be talking about this from a global perspective using examples from all over the world. We'll also be talking about corruption from the perspective of countries, governments, businesses, citizens, and the people within these entities. At the end of this course, you'll have a better understanding of why the world is changing according to the path of corruption. If you're in a position to make changes within your respective entity, to make decisions about corruption, you'll be able to make better decisions.

There is mounting concern that organizational groups and teams often fail to learn from their past experiences. It’s pertinent to address this issue as groups and teams are often the main ways that work gets done in organizations. In this course, we examine the main reasons that groups and teams are often ineffective, which include: • The lack of organizational structures and support for teams and groups • The lack of understanding and emphasis on learning • Misaligned reward structures So, what can be done to create an enabling learning culture in teams? This course emphasizes practical and impactful ways to begin to address this state of affairs. Through the use of stories, scenarios with actors simulating different team issues, examples and dialogue, you learn how to: • Understand teams in their larger organizational context • Diagnose the learning strengths and barriers to learning on teams • Identify ways to develop a team and group that continually learns and impacts the larger organization positively • Create an organizational environment that encourages learning and innovation

The course starts by exploring entrepreneurial ecosystems and systems thinking. The Technology Adoption Life Cycle is introduced to help explain market dynamics and support developing business strategy. Market research concepts and tools are presented that learners will apply to their course projects. Founder, key manager, and board member considerations are examined, and the qualities of effective leadership are reviewed. The operational structures and processes of a tech startup are identified, and the Balanced Scorecard is presented as a tool for tracking critical performance metrics. Learners will create a first draft of Product, Market, and Management & Operations Plans for their new product or service ideas. Creating a Startup Company can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

Creativity requires us to collaborate with others, and this course helps you be a better creative collaborator. We need to be able to pitch our creative ideas so that others are excited rather than baffled or dismissive by them. We need to be able to evaluate the ideas of others so we identify rather than miss creative solutions. We need to be able to work with our teams such that creativity thrives rather than is suppressed. This course addresses each of these needs, identifying challenges and providing guidance for effective performance. The end result is guidance on how to foster effective creative collaboration. You will be able to: -Design pitches for innovative ideas to build excitement and clarity -Evaluate the pitches of others to identify great new ideas -Lead groups to foster effective collaboration for innovation This course is part of Gies College of Business’ suite of online programs, including the iMBA and iMSM. Learn more about admission into these programs and explore how your Coursera work can be leveraged if accepted into a degree program at https://degrees.giesbusiness.illinois.edu/idegrees/.

This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.

Once we have generated data, we need to answer the research question by performing an appropriate statistical analysis. Engineers and business professionals need to know which test or tests to use. Through this class, you will be able to perform one sample tests for comparison to historical data. You will also be able to determine statistically significant relationships between two variables. You will be able to perform two sample tests for both independent and dependent data. Finally, you will analyze data with more than two groups using the Analysis of Variance. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE

Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4

This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

Organizations are complex and can be difficult to manage well, so managers need tools to help them better understand and deal with the many complexities and challenges associated with organizational life. In this course, you will learn theories, principles, and frameworks that will help you design more effective organizations. You will be able to: - Analyze organizations from multiple perspectives to better understand business challenges - Strategically plan for effective organizational governance - Develop systems to plan for growth and change - Understand implications of the external environment on business and strategically respond to these factors This course is part of Gies College of Business’ suite of online programs, including the iMBA and iMSM. Learn more about admission into these programs and explore how your Coursera work can be leveraged if accepted into a degree program at https://degrees.giesbusiness.illinois.edu/idegrees/.

Elevate your operational management skills with our Driving Operational Performance course, building upon the foundations laid in the Introduction to Operations Management. Led by the renowned Dr. Terwiesch, this course provides an in-depth exploration of advanced operational concepts, equipping you with the tools to analyze and optimize complex processes. You will gain practical insights into enhancing operational efficiency through hands-on problem-solving and engaging video lectures. In this course, you'll delve into the intricacies of process analysis, learning to identify bottlenecks within intricate systems and understanding how variability impacts operational performance. You'll explore the relationship between customization and setup times, enabling you to fine-tune processes for maximum efficiency. Capacity planning will be demystified as you discover how product mix affects capacity calculations and the importance of implied utilization. Additionally, you'll develop the ability to bridge finance and operations, using the Return on Invested Capital (ROIC) framework to connect financial and operational variables. Our course structure is designed for your success, combining engaging video lectures, hands-on practice problems, and interactive discussions. You'll have the flexibility to pause videos, work on problems, and witness Dr. Terwiesch's problem-solving approach, ensuring a thorough grasp of the material. Join us in pursuing operational excellence and acquiring the advanced skills necessary to drive success within your organization. Enhance your ability to analyze and optimize complex processes, link operations with finance, and improve customer experiences. With a focus on practicality and real-world applications, this course empowers you to become a more effective operational manager. Enroll today to take your career to the next level and make a lasting impact on your organization's performance. Real-world examples from industry leaders will illustrate key concepts throughout the course. Please note that these case studies are for educational purposes and do not imply endorsements of the mentioned businesses. To fully benefit from this course, it's recommended to complete the Introduction to Operations Management course beforehand.

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

This course will give you hands-on FPGA design experience that uses all the concepts and skills you have developed up to now. You will need to purchase a DE10-Lite development kit. You will setup and test the MAX10 DE10-Lite board using the FPGA design tool Quartus Prime and the System Builder. You will: Design and test a Binary Coded Decimal Adder. Design and test a PWM Circuit, with verification by simulation. Design and test an ADC circuit, using Quartus Prime built-in tools to verify your circuit design. Create hardware for the NIOS II soft processor, including many interfaces, using Qsys (Platform Designer). Instantiate this design into a top-level DE10-Lite HDL file. Compile your completed hardware using Quartus Prime. Enhance and test a working design, using most aspects of the Quartus Prime Design Flow and the NIOS II Software Build Tools (SBT) for Eclipse. Create software for the NIOS II soft processor, including many interfaces, using Qsys (Platform Designer) and the SBT. Compile your completed software using the SBT. Use Quartus Prime to program both the FPGA hardware configuration and software code in you DE10-Lite development kit. Record all your observations in a lab notebook pdf. Submit your project files and lab notebook for grading. This course consists of 4 modules, approximately 1 per week for 4 weeks. Each module will include an hour or less of video lectures, plus reading assignments, discussion prompts, and project assignment that involves creating hardware and/or software in the FPGA. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.

This course can also be taken for academic credit as ECEA 5600, part of CU Boulder’s Master of Science in Electrical Engineering degree. Optical instruments are how we see the world, from corrective eyewear to medical endoscopes to cell phone cameras to orbiting telescopes. When you finish this course, you will be able to design, to first order, such optical systems with simple mathematical and graphical techniques. This first order design will allow you to develop the foundation needed to begin all optical design as well as the intuition needed to quickly address the feasibility of complicated designs during brainstorming meetings. You will learn how to enter these designs into an industry-standard design tool, OpticStudio by Zemax, to analyze and improve performance with powerful automatic optimization methods.

This course starts with a close look at entrepreneurial finance, revenue models, and sources of startup funding. Learners will build a revenue model, create a Financial Plan for a new startup idea, and prepare an investor presentation for raising capital. Business legal structures are reviewed and some of the key legal considerations and pitfalls for startups are examined. Intellectual Property (IP) is presented as a business strategy for creating value and building competitive advantage. The role of ethics in a startup environment is explored in the context of an entrepreneurial mindset. The value of building a learning organization is presented. Startup growth strategies for building technology companies and exit options for technology entrepreneurs are reviewed. Learners will complete a business plan for a new product or service idea. Forming, Funding, and Launching a Startup Company can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

Many find themselves frustrated in their careers by lack of preparation for dealing with predictable dilemmas that regularly arise in the world of work. Two in five professionals fired in their jobs after college are terminated for lying, or misuse of technology. Easy to avoid! Or is it? What if you find you have to compromise your values to keep your job? What if they ask you to lie or cheat, even though you know if you are found out you will be fired? What if you find out the company is breaking the law? Successful people know about and have the skills taught in this specialization. You should, too. Those who wish to make a difference, build positive reputations, become leaders, and advance in their careers must cultivate professional success skills, along with their specific job skills. The central themes of this course – knowing yourself and your values, recognizing and meeting ethical challenges, using an analytical decision-making framework, identifying potential pitfalls, and developing tools to use in the moment – are essential soft skills for succeeding at work. In short, an ethical framework is necessary, and not just nice; smart ethics, as we define them, are integral to success.

Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.

In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.

In this course, you’ll learn the foundational economic theories behind health care innovation and how to optimize your own health care practice or organization. Designed to help you gain a practical understanding of innovation techniques, operations management, and value and quality in the health care setting, this course will help you apply these frameworks to assess health care practices and apply innovation while managing risk. You’ll also explore the best practices for evaluating one’s innovative practices, using real-life examples of success to see the concepts in action. By the end of this course, you’ll have honed your skills in optimizing health care operations, and be able to develop the right set of evaluations and questions to achieve best innovative practices within your organization.

This course begins with an overview of general intellectual property law in the United States, then examines how the policy choices behind those laws shapes the behavior of major players in the healthcare industry. We will examine how patents and trademarks shape the behavior of pharmaceutical companies, and how complex issues involving medical research in university settings, and ownership of genetic information and material, are resolved by intellectual property laws. The course concludes with in-depth case studies of an international pharmaceutical company, a major medical device company, and a start-up in the emerging DNA-testing industry.

In this first course of the specialization, we will discuss the limitations of the Internet for business and economic activity, and explain how blockchain technology represents the way forward. After completing this course, you will be able to explain what blockchain is, how it works, and why it is revolutionary. You will learn key concepts such as mining, hashing, proof-of-work, public key cryptography, and the double-spend problem. You’ll be able to describe seven design principles for blockchain technology, and the challenges facing the people developing it. You’ll also meet the players in the blockchain ecosystem, and consider your own role in stewarding the blockchain revolution.

This course provides a brief introduction to the fundamentals of finance, emphasizing their application to a wide variety of real-world situations spanning personal finance, corporate decision-making, and financial intermediation. Key concepts and applications include: time value of money, risk-return tradeoff, cost of capital, interest rates, retirement savings, mortgage financing, auto leasing, capital budgeting, asset valuation, discounted cash flow (DCF) analysis, net present value, internal rate of return, hurdle rate, payback period.

Solving the problems and challenges within the U.S. healthcare system requires a deep understanding of how the system works. Successful solutions and strategies must take into account the realities of the current system. This course explores the fundamentals of the U.S. healthcare system. It will introduce the principal institutions and participants in healthcare systems, explain what they do, and discuss the interactions between them. The course will cover physician practices, hospitals, pharmaceuticals, and insurance and financing arrangements. We will also discuss the challenges of healthcare cost management, quality of care, and access to care. While the course focuses on the U.S. healthcare system, we will also refer to healthcare systems in other developed countries. In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.

Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).

This course focuses on examining various practical applications of the fundamental financial analysis and valuation techniques employed in the investment banking industry. Specifically, we will examine how to analyze how a private equity firm or other financial sponsor completes a leveraged buyout of a company, how a public company analyzes the impact of acquiring a company on its earnings per share, and how a company completes an initial public offering. The course will benefit anyone who desires to increase their ability to understand and execute M&A and capital markets transactions, including (but not limited to), entrepreneurs, consultants, bankers, investors, analysts, corporate managers, marketers, strategists, and dealmakers of all types.

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

In this course, you will learn crucial skills needed to understand the intricate dynamics that go into the process of negotiation, and how you can go into your negotiation confident and fully prepared. You'll learn about the framework that goes into shaping a successful negotiation, in addition to gaining the knowledge that will allow you to adapt to rapidly changing circumstances. You'll also learn about emotional control, crafting questions to help you get the information that you need, and skills that will allow you to negotiate in any setting. You will also successfully learn how to navigate a negotiation through real-world exercises, and how to best work to build trust, diffuse anger, and make rational decisions based on the information at hand. Lastly, you will learn how to prepare to negotiate in any setting and use your skills to facilitate with teams and influence outcomes. By the end of this course, you’ll be able to utilize your newly acquired skills to successfully negotiate for employment, contracts, and in any part of your life. Within this course, you will end with the knowledge of how to craft a successful negotiation strategy and manage the conflict that can arise, as well as build trust.

In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.

This course is designed to impact the way you think about transforming data into better decisions. Recent extraordinary improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. The course on operations analytics, taught by three of Wharton’s leading experts, focuses on how the data can be used to profitably match supply with demand in various business settings. In this course, you will learn how to model future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk. The course will introduce frameworks and ideas that provide insights into a spectrum of real-world business challenges, will teach you methods and software available for tackling these challenges quantitatively as well as the issues involved in gathering the relevant data. This course is appropriate for beginners and business professionals with no prior analytics experience.

This course can also be taken for academic credit as ECEA 5601, part of CU Boulder’s Master of Science in Electrical Engineering degree. Optical instruments are how we see the world, from corrective eyewear to medical endoscopes to cell phone cameras to orbiting telescopes. This course will teach you how to design such optical systems with simple mathematical and graphical techniques. The first order optical system design covered in the previous course is useful for the initial design of an optical imaging system but does not predict the energy and resolution of the system. This course discusses the propagation of intensity for Gaussian beams and incoherent sources. It also introduces the mathematical background required to design an optical system with the required field of view and resolution. You will also learn how to analyze these characteristics of your optical system using an industry-standard design tool, OpticStudio by Zemax.

By drawing on social science perspectives, this course enables you to learn what diversity is, and how to use it to maximize team performance, innovation and creativity. You also learn how to draw out the collective wisdom of diverse teams, handle conflict and establish common ground rules through real-world cases and peer-to-peer discussions. In addition, you discover how to overcome common biases faced in diverse teams. Systems of power, reward and rhetoric are discussed to help you create prosperous teams where differences flourish.

Unlock your potential in the workplace by understanding the principles of organizational behavior. Inspired by Peter Drucker's insightful observations, this course offers a practical guide to navigating human dynamics in professional settings. You'll gain actionable skills in motivation, leadership, teamwork, and cultural awareness. Specifically, you will learn how to foster effective team collaboration, resolve conflicts constructively, develop your leadership style, and communicate effectively across cultures. This understanding will empower you to drive positive change within your team and organization. No prior experience is required. Suitable for individuals interested in business, project management, or leadership roles.

Learn how to incorporate resilience interventions into your personal and professional life with Dr. Karen Reivich. In this course, you are exposed to the foundational research in resilience, including protective factors such as mental agility and optimism. Several types of resilience interventions are explored including cognitive strategies; strategies to manage anxiety and increase positive emotions such as gratitude; and a critical relationship enhancement skill. Throughout the course, you will hear examples of individuals using resilience skills in their personal and professional lives. Suggested prerequisites: Positive Psychology: Martin E. P. Seligman’s Visionary Science, Positive Psychology: Applications and Interventions and Positive Psychology: Character, Grit & Research Methods.

This course is designed to provide a comprehensive foundation in Power BI, equipping learners with the skills to visualize, analyze effectively, and present data insights. Participants will explore a variety of chart types, understand their applications, and optimize visual reporting for business intelligence. The course begins with an introduction to various bar and column charts, including stacked bar, stacked column, clustered bar, and clustered column charts, essential for comparative analysis. Learners will then progress to line charts, pie charts, funnel charts, scatter charts, and map charts, providing insights into trends, distributions, and geographical data. Participants will gain hands-on experience working with table and matrix visuals, understanding the role of filters and KPI visuals for tracking performance metrics. Special emphasis is placed on card visuals, report types in Power BI, and designing analytical report layouts for clarity and impact. Advanced visualization techniques, including ribbon charts, waterfall charts, and gauge charts, will be explored alongside methods for selecting appropriate report visuals. Additionally, learners will delve into integrated visuals, such as line and stacked column charts, line and clustered column charts, ensuring effective multi-dimensional analysis. This course is structured into multiple modules, each featuring lessons and video lectures that provide both theoretical understanding and hands-on practice. Participants will engage with 3:00–4:00 hours of instructional content, reinforcing learning through graded and ungraded assignments to ensure real-world applicability. Module 1 - Power BI Visualizations: Essential Charts for Data Storytelling Module 2 - Power BI Visualizations: KPI Insights and Reporting Whether you're preparing for Power BI certification or seeking to optimize business intelligence workflows, this course equips you with the essential skills to master visualization techniques and enhance data-driven decision-making. The course is for Business Managers, Data Analysis Managers, Digital Marketing Managers, Power BI Associates and Experts.

In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.

In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment

This course discusses costs and business practices to establish the cost of a product. The concept of time value of money (TVM) is developed to determine the present and future values of a series of cash flows. TVM principles are then applied to personal finances and retirement planning. This is a practical course that uses spreadsheets to better prepare learners in engineering and science for a career in industry. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

Project Management: Foundations and Initiation provides students the foundational knowledge of how engineering projects are managed and initiated. Project managers are responsible for project scope, stakeholder management, effective communication, and team leadership. In this course, you will develop introductory skills needed to manage traditional engineering projects, along with tools needed to engage stakeholders and build diverse teams. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Logo image courtesy of Jehyun Sung, available on Unsplash at https://unsplash.com/photos/6U5AEmQIajg?utm_source=email&utm_medium=referral&utm_content=photos-page-share

The goal of this second course of the Project Management specialization is to provide students with skills necessary to plan and execute traditional engineering projects. Project managers must plan and manage complex projects constrained by time and budget. As part of this course, you will determine project schedules, budgets, and risk assessments. At the end of this course, you will also be able to identify and explain various quality tools and methods used in project management. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Logo image courtesy of Shane Rounce, available on Unsplash at https://unsplash.com/photos/DNkoNXQti3c?utm_source=email&utm_medium=referral&utm_content=photos-page-share

This course can also be taken for academic credit as ECEA 5316, part of CU Boulder’s Master of Science in Electrical Engineering degree. This course provides an in-depth and full mathematical derivation and review of models for scheduling policies and feasibility determination by hand and with rate monotonic tools along with comparison to actual performance for real-time scheduled threads running on a native Linux system. By the end of this course the learner will be able to full derive the fixed priority rate monotonic least upper bound for feasibility as well as justifying the rate monotonic policy and will be able to compare to dynamic priority scheduling including earliest deadline first and least laxity policies. At the end of this course learners will be able to fully derive and explain the math model for the rate monotonic least upper bound as well as performing timing diagram analysis for fixed and dynamic priority software services. Tools to provide analysis will be learned (Cheddar) to automate timing analysis and to compare to actual performance. Specific objectives include: ● Rate monotonic theory (complete math models) ● Differences between fixed priority rate monotonic policy and dynamic priority earliest deadline first and least laxity policies ● Scheduling theory and practice writing code for multi-frequency executives, priority preemptive RTOS services, and real-time threaded services on traditional operating systems (Linux) ● Building a simple Linux multi-service system using POSIX real-time extensions on Raspberry Pi 3b using sequencing and methods to log and verify agreement between theory and practice ● Timing diagram generation and analysis using Cheddar This course includes specific hardware and software requirements. Please review the FAQ below for complete details.

The ultimate destination for a security manager is the Chief Information Security Officer (or Chief Security Officer) a senior executive role responsible for all cybersecurity operations in the organization. But how do you get from entry-level IT or security employee to the CISO’s office and what do you need to know when you get there? This course examines the career path and requirements to be an effective CISO, as well as the roles and responsibilities of the position. In this course, a learner will be able to: ● Identify the career development and path of a Cybersecurity professional from entry-level to CISO ● Define and describe the role and function of a CISO in planning for cybersecurity ● Identify the development of a cybersecurity governance program and the role the CISO would play in it ● Discuss the strategic responsibilities of the CISO in overseeing an organization’s cybersecurity program

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

In this course, we will apply the central concept and applications of Time Value of Money (TVM) to explore the structure and pricing of stocks and bonds at an introductory level. In this course, you will learn about bonds, different types of bonds (Zero Coupon bonds, Government bonds). You will learn about bond pricing calculations and see their direct connection to market data on bonds. You will also learn about stocks, and their pricing and valuation. You will learn about growth and dividend stocks and how to use market data. After completing this course, you will have an understanding of the two fundamental and pervasive ways in which savers transfer money to governments and corporations. You will be able to apply all this knowledge to personal investing decisions and, importantly, these same tools and frameworks are applicable to corporate decisions. This course is part of the four-course Foundational Finance for Strategic Decision Making Specialization.

Do you want to be more successful? This course was designed to help you define what success means to you, and to develop a plan for achieving it. Wharton Professor G. Richard Shell, an award-winning author and the creator of the popular Wharton School course on the meaning of success, created this course to help you answer the questions that arise when you consider how best to use your life. Drawing on his decades of research and mentoring, Shell offers personalized assessments to help you probe your past, imagine your future, and measure your strengths. He then combines these with the latest scientific insights on everything from self-confidence and happiness to relationships and careers. Throughout, he shares inspiring examples of people who found what they were meant to do by embracing their own true measure of success. Get ready for the journey of a lifetime—one that will help you reevaluate your future and envision success on your own terms. Students and executives say that Richard Shell’s courses and executive training programs have changed their lives. Let this course change yours.

Learn to excel in product management roles while learning directly from industry-leading product managers and real-world case studies from Amazon, Intuit, Procter and Gamble, Barnes and Noble, Whole Foods, Google, Twitter, Tesla, Dropbox, Eventbrite, Blue, Samson, Coffee Meets Bagel, Mixpanel, and more! Featuring expert product manager instructors including: * NANCY WANG, Head of Data Protection Services, Amazon Web Services; Founder and CEO, Advancing Women in Product * WAYNE DUSO, Vice President for Edge Computing, File Services, and Data Services, Amazon Web Services * ARUM KANG, Co-Founder & CEO, Coffee Meets Bagel * AMIR MOVAFAGHI, CEO, Mixpanel; former Global Head of Business of Operations, Twitter. Sponsored by AMAZON WEB SERVICES (AWS). This is the first of four courses in the ACE Recommended Real-World Product Management Specialization. When you complete all four courses, you can earn up to 6 college credits.

Behind the success and failure of teams lies team culture. But what exactly is culture? How and why does it contribute to success and failure? In this course, you learn how to recognize aspects of team culture of which most people are typically unaware. It is often these seemingly unimportant aspects that have the greatest effect on the outcomes of group tasks. Your recognition of them enables you to form, join and lead teams more effectively. You also come to appreciate the role of ritual and symbols in the daily operation of teams, as well as in situations of change and crisis. Knowledge of the ideas and information in this class enables you to be a better team player and a more effective team leader.

Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will develop a more profound knowledge and understanding of diverse groups by hearing actual accounts from various marginalized populations and adopting an approach of first looking inward, then confronting ignorances, biases, and stereotypes of marginalized populations, and ending each lesson with a personal reflection. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Ave Calvar. Available on Unsplash at https://unsplash.com/photos/5u4L-E4eQWw

This course can also be taken for academic credit as ECEA 5632, part of CU Boulder’s Master of Science in Electrical Engineering degree. This course presents in-depth discussion and analysis of metal-oxide-semiconductor field effect transistors (MOSFETs) and bipolar junction transistors (BJTs) including the equilibrium characteristics, modes of operation, switching and current amplifying behaviors. At the end of this course learners will be able to: 1. Understand and analyze metal-oxide-semiconductor (MOS) device 2. Understand and analyze MOS field effect transistor (MOSFET) 3. Understand and analyze bipolar junction transistor (BJT)

This course can also be taken for academic credit as ECEA 5346, part of CU Boulder’s Master of Science in Electrical Engineering degree. UX and Interface Design for Embedded Systems is the first of three classes in the Embedded Interface Design (EID) specialization, an online version of the on-campus EID class taught in graduate embedded systems design. This first course is focused on user experience (UX) and the related methods, practices, and principles that will help ensure your embedded interface designs for devices and systems are what your users both need and want. The class includes an introduction to UX, and then a four phase breakdown of a typical UX design process, including planning, user research, design methods, and testing for verification and validation. Much of the content provides generally applicable UX techniques, but particular focus is included for considerations in developing embedded devices. The class includes practical projects that let you try some of the key methods in a thorough interface design process.

For most of the last century, critical advances in technology were happening inside large, well-funded industrial R&D machines that dotted the Western world. Today’s landscape, however, is very different. Even the largest and most sophisticated corporate leaders can no longer dominate their fields or dictate the pace of development. With companies across sectors seeking to harness the transformative potential of blockchain technologies, the blockchain ecosystem is an exemplar of new models of industry collaboration. In this course you will learn about various stakeholders in the blockchain ecosystem and the leadership roles they need to play in order to steward the blockchain revolution in global commerce. You will discuss various organizational models and best practices for industry consortia and will explore several examples of consortium projects that are at the forefront of the blockchain ecosystem. You will also explore the themes of standardization and interoperability—two major challenge areas concerning the scalability and throughput limitations inhibiting mainstream enterprise adoption of blockchain in global commerce. Development of this course was made possible thanks to the generous support of FedEx. Dale Chrystie, Business Fellow & Blockchain Strategist at FedEx, delivers three of the course videos, bringing a real-world industry perspective to the course. The scripts for these videos have been written and/or reviewed by the Blockchain Research Institute and approved by INSEAD to ensure that this content is pedagogically sound, unbiased, and academically rigorous.

In this Capstone you will recommend a business strategy based on a data model you’ve constructed. Using a data set designed by Wharton Research Data Services (WRDS), you will implement quantitative models in spreadsheets to identify the best opportunities for success and minimizing risk. Using your newly acquired decision-making skills, you will structure a decision and present this course of action in a professional quality PowerPoint presentation which includes both data and data analysis from your quantitative models. Wharton Research Data Services (WRDS) is the leading data research platform and business intelligence tool for over 30,000 corporate, academic, government and nonprofit clients in 33 countries. WRDS provides the user with one location to access over 200 terabytes of data across multiple disciplines including Accounting, Banking, Economics, ESG, Finance, Insurance, Marketing, and Statistics.

Have you ever paid someone for doing you a favor? You may be accustomed to tipping wait staff or other service providers, but what if you paid a police officer for NOT issuing a parking ticket? Or if you gave a “gift” to a government official for signing a contract with your company? Tipping might be okay, but paying a bribe is a violation of the law. Bribery is widespread and exists almost everywhere and in many forms. Compliance programs address this and other forms of corruption. In this course we’ll discuss what corruption actually is by examining, for example, the difference between a tip and an illegal bribe. We will examine where corruption exists, how it exists, its ramifications, and the genesis of today’s anti-corruption laws. We’ll also focus on practical strategies for creating compliance policies and procedures that address and prevent corruption. We will learn about the U.S. Foreign Corrupt Practices Act, the UK Bribery Act, and many other anti-corruption laws. Much of the course will focus on building out compliance policies and procedures that address corruption. We will also look at practical methods for preventing corruption in an organization as well as red flags and other mechanisms for identifying corruption early so that it can be adequately addressed.

通过探究客户决策根源，学习市场营销基础知识。主要课题包括品牌塑造战略（例如，品牌定位、品牌传播）、客户中心化市场营销战略，以及新市场开拓（例如，渠道战略、市场营销规划）。成功完成本课程后，您可以运用所学技能处理现实商务挑战，这也是沃顿商学院商务基础专项课程的组成部分。

了解如何提升工作效率和提高质量标准，学会分析和改善服务业或制造业商务流程。主要概念包括流程分析、瓶颈、流程速率和库存量等。成功完成本课程后，您可以运用所学技能处理现实商务挑战，这也是沃顿商学院商务基础专项课程的组成部分。