Our increasingly connected world is producing more data than humans are able to process and is transforming our economy and many businesses. In making decision, executives rely on predictions and forecasts that are based on the versatile data that nowadays companies are increasingly gathering. Problems of this nature occur in fields as diverse as finance, operations management, marketing, risk management among others. Making accurate forecasts is thus a necessity for managers to cope with issues such as seasonality, demand changes, price-cutting maneuvers, and swings in the economy and is fundamental for creating a competitive advantage. For instance, in the retail industry, accurately forecasting demand is key to optimizing order quantities, stock levels, and in avoiding stock-outs and costly obsolete product disposals. Accordingly, predicting consumer behaviors allows retailers to adjust sourcing and distribution strategies and in maintaining a cash-flow that otherwise would be tied to extra inventory.
Students will be able to gain basic R skills and learn about different predictive analytics models. Particularly the students will learn to:
- Identify different features of realistic data and analyze these features using the appropriate predictive analytics methods;
- Identify the limitations and the applicability of different predictive analytics methods.;
- Develop different predictive models to predict data and evaluate these models quantitatively; and,
- Implement predictive models using R and/or python.