Financial analysis and modeling are critical skills in today's data-driven business world. This certificate program equips you with advanced Microsoft Excel techniques, forecasting methods, SQL basics, and Python fundamentals for finance. Learn to create impactful financial presentations and leverage big data for insightful analysis. Tune in, pass the final exam, and earn your certificate.

Find out how you can create high-impact, data-rich financial presentations using Excel and PowerPoint. In this course, Microsoft MVP and Financial Modelling Specialist Danielle Stein Fairhurst guides you through creating financial presentations that are compelling, easy to understand, and—best of all—easy to update. Get started with an example spreadsheet featuring typical finance data. Learn how to use conditional formatting to highlight important information, PivotTables to summarize data, and sparklines and charts to create illustrations. Explore several different methods—including copying and pasting, linking, and embedding—to import your work into PowerPoint. Plus, learn how to use PowerPoint animation features to animate Excel charts and tables and focus the viewer's attention.

Big data has transformed the world of business. Yet many people don't understand what big data and business intelligence are, or how to apply the techniques to their day-to-day jobs. This course addresses that knowledge gap, giving businesspeople practical methods to create quick and relevant business forecasts using big data. Join Professor Michael McDonald and discover how to use predictive analytics to forecast key performance indicators of interest, such as quarterly sales, projected cash flow, or even optimized product pricing. Learn how to gather, compute, and use basic financial ratios, and then how to apply those ratios for forecasting purposes looking at key outcomes for the firm. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.

Financial reports are not just summaries of the past—they also include predictions for the future. In fact, most financial institutions are more interested in future performance than historical trends. Banks want to know your future cash flow; investors want to know future profits. In this course, Jim and Kay Stice explains how to create forecasted financial statements for your company. Learn how to use past data such as cost of goods sold, depreciation expenses, and levels of inventory, and understand what caused those numbers to fluctuate over time. Then you can learn how to use the information as the basis for forecasting, applying a simple but powerful equation: assets = liability + equity. You get hands-on practice building three different documents: a forecasted income statement, a forecasted balance sheet, and a forecasted statement of cash flow. Throughout the course, Jim and Kay use famous business cases—like Home Depot’s 1985 cash-flow crisis—to illustrate the importance of accurate financial forecasts and their impact on business decisions.

Financial planning and analysis (FP&A) is a rapidly evolving field within corporate finance and one of the most important functions of any successful finance department. While there are a number of different data visualization tools that support FP&A, most companies rely on the ubiquitous Microsoft Excel. In this course, join instructor, CSP, and CPA Carl Seidman as he provides a comprehensive overview of how to harness the power of Excel for forecasting, planning, analysis, and modeling. Along the way, find out why Excel can be so useful for FP&A, including coverage of how to leverage key analytic tools such as dynamic data tables, dynamic data ranges, dynamic data arrays, and more.

If you work in the financial sector, one tool that may not come to mind when thinking about the day-to-day work in finance is SQL—structured query language. But SQL is the programming language used to query data, so it’s extremely useful for anyone who works with vast stores of information, including financial professionals. As more and more data gets produced from the proliferation of modern tools and technologies, it’s increasingly important to know how to handle all the new data, and in this course, Megan Lieu shows you how to do just that using SQL. She starts with the background of SQL, including use cases, SQL data types, how to query data, how to use functions to perform calculations, and more. She then delves into finance-specific applications of SQL, giving an overview of how SQL is used in finance, the difference when using data in fintech vs. finance, and shows you how to use SQL in combination with a more familiar financial sector tool, Excel.

Python has quickly become one of the most popular and widely used programming languages in the world. And if you work in finance and analyze the stock market or other financial instruments, you need to stay up to date with the most important analytic tools. Join instructor Matt Harrison to get up and running with Python, in this course designed uniquely for financial analysis. Learn how to implement the best practices for loading data and visualizations, performing calculations, ingesting and preparing financial data, coding technical analysis signals, and more. Along the way, test out your new skills in the challenges and coding exercises at the end of each section. Upon completing this course, you’ll be ready to start leveraging the power of Python to optimize your financial analysis workflow.

This course, led by Professor Michael McDonald, introduces the fundamentals of algorithmic trading and how modern financial markets operate. It covers the development of rules-based, back-tested trading strategies and guides you through programming a basic trading algorithm. Topics include market instruments like stocks, bonds, and derivatives, as well as regression-based value prediction and practical examples of automated trading in real-world scenarios.

This course provides a foundational understanding of corporate finance and its influence on business decisions and growth. Taught by professors Jim and Kay Stice, it covers key concepts such as risk management, diversification, capital structure, and the use of debt vs. equity. It also introduces the Capital Asset Pricing Model (CAPM) and explains how financial decisions affect a company's performance and valuation.

In this course, Professor Michael McDonald demonstrates how to use Microsoft Excel for predictive analytics to forecast key business metrics like sales, cash flow, and pricing. You'll learn to apply regression analysis, calculate confidence intervals, and perform stress testing using Excel’s built-in tools—equipping you to generate practical, data-driven forecasts for real-world business needs.

Validates foundational knowledge of core programming concepts, software development practices, and essential object-oriented programming principles. Designed for aspiring software developers and students, it serves as an entry-level credential that introduced key technical skills across multiple development environments.

Application of corporate finance concepts to mergers and acquisitions and corporate governance. The topics will include Mergers and Acquisitions, Corporate Governance, and Restructurings. The tools, techniques, and theories of finance are applied to actual business decisions.

A study of the securities markets, investor objectives, introduction to portfolio theory and the analytical tools of portfolio management, and an examination of investment alternatives. Students will build and manage a security portfolio for analysis as an experiential learning tool.

Examines the information and communication tools, technologies, and standards integral to consumer, merchant, and enterprise services in the payments and financial service sectors. Explores technology's role in reshaping FinTech businesses. Technologies span messaging, communication networks and gateways, core processing, mobile and online software, and application program interfaces (APIs).

A rigorous overview of methods for text mining, image processing, and scientific computing. Core concepts in supervised and unsupervised analytics, dimensionality reduction, and data visualization will be explored in depth.

The basic concepts and analytical tools of finance in both corporate finance and investments. Topics include risk and return, financial institutions, efficient markets, valuation theory, capital budgeting, portfolio theory, cost of capital, and international finance.

Development of a framework that is useful for understanding a broad range of important corporate financial decisions. Substantial emphasis will be placed on discussion of the determinants of corporate financing and payout policies, alternative methods of security issuance, and mergers and acquisitions.

An introduction to current practices in financial modeling. Students will learn how to apply financial models with financial data to perform analysis. This hands-on course provides the skills to apply the theories, concepts, and spreadsheet tools to develop effective financial analysis and decision-making.

Software development techniques in an object-oriented computer language. An intermediate programming course emphasizing systems methods, top-down design, testing, modularity, and structured techniques. Applications from areas of numeric and non-numeric processing and data structures.

A survey of the fundamental mathematical tools used in Computer Science: sets, relations, and functions; propositional and predicate logic; proof writing strategies such as direct, contradiction, and induction; summations and recurrences; elementary asymptotics and timing analysis; counting and discrete probability with applications in computer science.

The design, analysis, and implementation of data structures and their associated algorithms. Lists, stacks, queues and priority queues, trees, graphs, dictionaries, time and space complexity, sorting and searching, advanced problem-solving, and algorithm design strategies.

Analysis of data with appropriate software. Develop data exploration and visualization skills. Apply concepts of random sampling, probability, probability distributions, and expected value. Employ statistical inference in business decision making. Use linear regression for describing and predicting empirical relationships.

An operational and strategic examination of financial institutions, intermediaries, and innovative startups in payments, digital and mobile banking, alternative lending ("alt-finance"), wealth management, and insurance ("insur-tech"). Also examines the practices, business and revenue models, regulatory issues, and competitive opportunities as well as the challenges and threats FinTech firms must address.