Today, businesses, governments, and individuals create massive collections of data as a by-product of their activity. Increasingly, decision-makers and systems rely on intelligent technology to analyze data systematically to improve decision-making. In many cases automating analytical and decision-making processes is necessary because of the volume of data and the speed with which new data are generated.
In this course, we will examine how data analysis technologies can be used to improve decision-making. We will study the fundamental principles and techniques of data science, and we will examine real-world examples and cases to place data science techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
Data Science for Business is a course intended for those who need to understand data science and those who want to develop their skill at data-analytic thinking. This course is not a about algorithms. Instead it presents a set of fundamental principles for extracting useful knowledge from data. These fundamental principles are the foundation for many algorithms and techniques for data science, but also underlie the processes and methods for approaching business problems data-analytically, evaluating particular data science solutions, and evaluating general data science plans.
The class meetings will be a combination of lectures on the fundamental material, discussions of business applications of the ideas and techniques, guest lectures from practitioners, case discussions, and student exercises.