Course Preview: Financial Analytics: Forecasting, Modeling, and Optimization at Wharton

Program Introduction 

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Hello, and welcome to Financial Analytics, a course offered by the Wharton School at the University of Pennsylvania.  I am Professor Jules Van Binsbergen, and together with Professor Michael Roberts, we will lead you on this journey to build skills in using data analytics for financial decision-making. In this six-week online experience, you'll learn the fundamentals of how to use data analytics to improve financial decision-making. By the end, you'll develop the strategic and analytical skills needed to transform how your business uses data for key decisions. Jules and I will guide you through all of the materials in this course. We've built content into five weeks. And then, created the six-week to apply what you've learned within your organization. So, by the end of this course, you'll be able to Recognize the value data analytics provides to the financial decision-making process, Analyze the opportunities and limits of data and analytics when used for causality and forecasting in finance, and effectively Manage and engage with data analytics teams and tools.  Apply data analytic strategies and tools to real-world financial challenges. Create value for the organization based on financial data analytics. Within this program, you can expect lecture videos, an expert interview, case studies, targeted reading material, interactive Q&A, group discussions, and much more. Let's take a look at the structure of each week.  In Week One, we will Explore the value of data analytics for decision-making. We'll Outline the scientific method and data science workflow and identify potential pitfalls of data analytics. In Week Two, we begin to Analyze data. We'll  Evaluate a company's revenue stream and customer base using the scientific method and data science workflow. The application will illustrate the power of these processes to extract actionable insights from data. In Week Three, you'll explore various forecasting strategies using firms' earnings as your backdrop. 

We will do a battle of man versus machine by comparing the earnings forecast provided by analysts to those produced by advanced forecasting techniques such as machine learning. Next, you will learn to apply forecasting techniques to business decisions by choosing an appropriate modeling technique for the desired outcomes. Finally, for part of this week,  we'll be interpreting machine learning output. In Week Four, we worked together to identify and quantify corporate credit risk. Again, using the scientific method and data science workflow. You'll be able to measure and assess the credit risk of different companies by analyzing their financial information and credit ratings. 

We'll also introduce Machine learning classification models in the context of credit rating prediction. In Week Five, we will look at how to improve investment decisions. Our application area for this week will be asset management. You'll be able to define investment portfolios and identify various asset classes. This will help you explain elements of the investment process to construct optimal portfolios, utilizing a variety of methods, strategies, and tools. including machine learning. In our final Week Six, we'll help you Activate the lessons of the course by guiding you through the process of integrating and applying data analytics to challenges in your organization. 

For the final project, you'll identify a problem or challenge faced within an organization for which data analytics is a  potential solution. For this problem, you'll propose a data analytics solution using the tools and processes we've covered in the course. The ultimate goal is, of course, actionable information that can help you solve the problem. We're looking forward to guiding you along the financial analytics pathway. Let's get started.

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