This course will provide you with concepts and tools to utilize data for making informed business decisions. We will start with the raw data and work our way to conclusions and examine all the intermediate steps in detail. Topics as data collection, model selection, built-in assumptions, and uncertainty will be at the core of the course. You will familiarize yourself with tools to apply these concepts in practice.
Credits: 3, prerequisites: none.
Sophisticated algorithms that learn from data are taking over the world. Ever shopped at Amazon.com or watched a movie on Netflix? In both cases, recommendations are being made for more items to buy or more movies to watch which will increase consumption and profits. It is expected that businesses that do not engage in business analytics and related techniques will eventually fail as there is a competitive advantage in using these approaches. In this course, we will look at clustering (grouping ‘things’ such as customers), classification (predicting a categorical variable such as whether someone will buy or not), optimization (finding the best solution given some constraints), and regression (predicting a number such as someone’s salary). These approaches can be used to understand your business through data in different ways and allow data driven decision making.
While this course can be challenging at times, all work is done in Excel which should be familiar to most. We will use advanced functionality such as vlookups, pivot tables, the solver, and much more.
Module 1: Spreadsheet Basics and Beyond I
Module 2: Spreadsheet Basics and Beyond II
Module 3: Cluster Analysis I
Module 4: Cluster Analysis II
Module 5: Classification I
Module 6: Classification II
Module 7: Review and Midterm
Module 8: Optimization I
Module 9: Optimization II
Module 10: More Cluster Analysis I
Module 11: More Cluster Analysis II
Module 12: Regression I
Module 13: Regression II
Module 14: Review
Module 15: Final Exam
- Formulate a detailed plan to go from gathering and analyzing data to making a decision.
- Select the appropriate concepts for the decision at hand.
- Appraise and employ the applicable tools for the decision at hand.
- Verify what assumptions are being made about the data.
- Assess whether the assumptions being made are appropriate.
- Judge the amount of uncertainty in the data and selected model and how it affects the decision.
- Business Analytics terminology
- Decision making
- Data mining
- Data warehousing
- Reasoning under uncertainty
- Problem solving
- Practical application of tools