This course builds upon the fundamental concept of Time Value of Money (TVM) using more advanced applications and questions. You will apply the TVM concept in real-life problems of financial planning and saving for college. You will also learn more about loans and apply TVM concepts to borrowing and lending. You will realize that — while the applications are seemingly more complex, but when seen and broken-up into bite-size components — the framework, principles, and tools remain the same. After completing this course you will have an understanding of the detailed mechanics and reasoning behind any decision you make that has consequences for the future. The deeper exposure to financial transactions you will be applicable in any/all decisions. The great news is that these concepts and skills will transfer to your professional/business decisions. This course is part of the four-course Foundational Finance for Strategic Decision Making Specialization.

Discover the future of payment technology, from mobile payments to tokenization. In this course, you will learn new ways of making payments from consumer-to-business (C2B), from consumer-to-consumer (C2C), and from business-to-business (B2B). You will explore current payment system technologies to examine their strengths and weaknesses, and understand the ways technological innovation is changing these traditional systems. You’ll learn about new front-end innovations like digital wallets and mobile payments and also discover back-end innovations like tokenization, mobile money, and new payment infrastructure.

This course teaches you how to calculate the return of a portfolio of securities as well as quantify the market risk of that portfolio, an important skill for financial market analysts in banks, hedge funds, insurance companies, and other financial services and investment firms. Using the R programming language with Microsoft Open R and RStudio, you will use the two main tools for calculating the market risk of stock portfolios: Value-at-Risk (VaR) and Expected Shortfall (ES). You will need a beginner-level understanding of R programming to complete the assignments of this course.

In this 1.5 hours long project, we will be creating an income statement dashboard filled with relevant charts and data. Power BI dashboards are an amazing way to visualize data and make them interactive. We will begin this guided project by importing the data and transforming it in the Power Query editor. We will then visualize the Income Statement using a table, visualize total revenue, operating income and net income using cards and in the final task visualize the year on year growth using clustered column charts. This project is for anyone who is interested in Power BI and data visualization and specially for those who work in accounts and finance departments. By the end of this course, you will be confident in creating financial statement dashboards with many different kinds of visualizations.

In this course, you will learn about AI-powered applications that can enhance the customer journey and extend the customer lifecycle. You will learn how this AI-powered data can enable you to analyze consumer habits and maximize their potential to target your marketing to the right people. You will also learn about fraud, credit risks, and how AI applications can also help you combat the ever-challenging landscape of protecting consumer data. You will also learn methods to utilize supervised and unsupervised machine learning to enhance your fraud detection methods. You will also hear from leading industry experts in the world of data analytics, marketing, and fraud prevention. By the end of this course, you will have a substantial understanding of the role AI and Machine Learning play when it comes to consumer habits, and how we are able to interact and analyze information to increase deep learning potential for your business.

This course is designed to provide a basic understanding of financial statements with an emphasis on the income statement. Building on the foundation formed in the first course, you will learn about the third of our three measurement questions and how the income statement helps to answer this final measurement question. Returning to the real business people introduced in the first course, this second course describes the basic content of income statement in a simple yet relevant context. The course ends by summarizing many of the lessons learned in both courses to leave you with a lasting impression about what financial statements are and how accounting can work for you. We all know that accounting is “the language of business”; let’s make learning this language engaging, and perhaps even fun! Upon successful completion of this course, you will be able to: • Describe the purpose of an income statement. • Define the basic components of an income statement. • Recognize and understand the meaning of several items typically presented on an income statement. • Explain the broader purpose of financial statements and the role of accounting in producing the financial statements. • Read and, to some extent, interpret real-world income statements. If you enjoy this business course and are interested in an MBA, consider applying to the iMBA, a flexible, fully-accredited online MBA at an incredibly competitive price offered by the University of Illinois. For more information, please see the Resource page in this course and onlinemba.illinois.edu.

Personal and Family Financial Planning will address many critical personal financial management topics in order to help you learn prudent habits both while in school and throughout your lifetime.

The FinTech revolution is rapidly transforming the financial industry. The use of digital technologies is the norm, and together with regulatory and market changes it is creating a revolution. After completion of the module, you'll be able to: describe the changes that influence the financial sector, understand the complexity of the payment infrastructure, identify and explain the key payment instruments and how they function, understand the types of money that exits, and recognize changes in the regulatory frameworks and how they inhibit or promote innovation. To begin, I recommend taking a few minutes to explore the course site. A good place to start is the navigation bar on the left. Click Course Content to see what material we’ll cover each week, as well preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. Be sure to introduce yourself to everyone in the Meet and Greet forum! This course should take about four weeks to complete. You can check out the recommended course schedule below to see a quick overview of the lessons and assignments you’ll complete each week. By the time you finish this course, you’ll have mastered mastered the transformational forces of digitalization and the new competitive dynamics it gives rise to, learned from leading financial companies and seen inspirational examples from the digital masters. Good luck as you get started. I look forward to seeing you in class!

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.

The practice of investment management has been transformed in recent years by computational methods. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. In this course, we cover the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduce a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness. As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods. If you follow along and implement all the lab exercises, you will complete the course with a powerful toolkit that you will be able to use to perform your own analysis and build your own implementations and perhaps even use your newly acquired knowledge to improve on current methods.