Kathryn Caggiano received a B.S. in Mathematics from the College of William and Mary in 1990 and a Ph.D. in Operations Research from Cornell University in 1998. Prior to returning to Cornell in 2007, Professor Caggiano was an Assistant Professor of Operations and Information Management in the School of Business at the University of Wisconsin-Madison. Outside of academia, she worked for several years in technology and supply chain consulting with Price Waterhouse and PeopleSoft Supply Chain Solutions. In her current role as Director of Master of Engineering Studies, Professor Caggiano is actively involved in the professional preparation and development of ORIE students at both the undergraduate and graduate levels. Under her leadership, the ORIE MEng program was selected as a finalist for the 2012 UPS George D. Smith Prize, INFORMS’s flagship award for the outstanding practical preparation of OR students.
Have you ever received a dataset that contains useful information but you can't quite get your hands on it? The data may not be in the right format, may have errors, or perhaps require additional elements. Though these usability issues can make it difficult or even impossible to get answers to important questions, we can often transform a dataset like this into something ready to use when it has an underlying structure.
In this course, you will discover how to make data usable by following a disciplined process of transforming, cleaning, and synthesizing data. You will gain hands-on practice getting your data ready by using filters and logical functions to structure data, identify errors, and create a “clean” dataset. Upon completion of this course, you will have developed the skills necessary to create a transformed dataset in Excel that you can then use to develop informative dashboards or perform critical analyses.
Key Course Takeaways
- Transform the structure of your data by completing missing data and converting data to the right format
- Find and fix common errors found in both text and numeric data
- Augment your dataset with new data fields using existing and supplemental information sources
How It Works
Who Should Enroll
- Finance professionals
- Data analysts
- Data scientists
- Business analysts
- Managers and executives
- Students who need in-depth Excel knowledge