This repository contains the class materials for the Business Analytics taught at New York University by JeanCarlo (J.C) Bonilla and Nil Simsek.
By JeanCarlo Bonilla. Comments or questions please email at jb3379@nyu.edu
###Course Description Business analytics is a set of data analysis and modeling techniques for understanding business situations and improving business decisions. This course provides an introduction to business analytics concepts, methods and tools with concrete examples from industry applications. In the first part of the course, we will focus on data analysis concepts and understanding data with a refresher on basic probability and statistics. In the second part, we will cover the principles and techniques for data visualization to improve comprehension, communication, and decision-making. The final part of the course will introduce the basic principles and techniques of applied mathematical modeling for managerial decision making with an emphasis on optimization models that are widely used in diverse industries and functional areas, including finance, marketing, and operations. Finally, throughout the course, we explore the challenges that can arise in implementing analytical approaches within an organization.
The course emphasizes that business analytics is not a theoretical discipline: these techniques are only interesting and important to the extent that they can be used to provide real insights and improve the speed, reliability, and quality of decisions.
###Course Web Page You must have access to the NYU Classes site (http://classes.nyu.edu/). All announcements and class-related documents (supplemental and suggested readings, discussion questions, etc.) will be posted there.
Some class announcements will be distributed via NYU e-mail. Thus, it is important that you actively use your NYU e-mail account, or have appropriate forwarding set up on NYU Home (https://home.nyu.edu/).
###Statement of Academic Integrity Students are expected to follow standards of excellence set forth by New York University. Such standards include respect, honesty, and responsibility. This class does not tolerate violations to academic integrity including:
- Plagiarism.
- Cheating on an exam.
- Submitting your own work toward requirements in more than one course without prior approval from the instructor.
- Collaborating with other students for work expected to be completed individually.
- Giving your work to another student to submit as his/her own.
- Purchasing or using papers or work online or from a commercial firm and presenting it as your own work.