There are no required text books. We will rely on:
Your old statistics/regression text (if you have one)+
Free online resources (I will point out where to find them for each topic) +
Extensive notes (Combination of ppt & pdf documents that I will provide)
A number of statistical packages have built in capacities to execute statistical procedures we need (and do so efficiently on large datasets): SAS, SPSS, STATA, R, Minitab, and so forth. In my own research I tend to use SAS (for data analysis), R (for graphics), and MATLAB (for advanced models—not required for most business applications you will encounter). The two most widely used in the business world are SAS and SPSS. For this class we will use SPSS as students find is easier and more intuitive than SAS (and it can do pretty much everything you will ever need). In addition, we will use a number of freebees from Google such as Fusion Tables & Google Trends.
Required: IBM SPSS Statistics version 20.0
SPSS can be accessed via the following:
Purchase a student license (approx $35—www.onthehub.com/spss/)
Use NYU VCL: (
All relevant material related to the course will be posted on Blackboard. Reading material, cases, and class notes will be made available at least a week before they are needed.
Basis for Final Grade:
There are two components to the final grade: (1) Assignments/Case studies (75%), and (2) Take-home final exam (25%). All exercises for the assignments/cases/final are application-oriented and incorporate extensive use of SPSS and EXCEL. They are drawn from three primary sources:
Proprietary data from the business world (mostly used in my research),
Publically available data from various businesses (e.g. Google), government (e.g. Census, BLS), international agencies (e.g. UN/World Bank)
Published research in the top academic journals from various fields. Many (good) journals require authors to publish the data used in their research. We will replicate the findings from some of the most influential papers from various fields for which data are available.
All assignments/cases (including the final exam) are open book, open notes, open internet, closed friends. We will mimic the “real” world where your objective is to solve the problem at hand and make recommendations based on your analysis. I will NOT penalize you if you can find a solution to any exercise on the internet. On the contrary, I will point out (and provide links to) the original sources of data for all assignments/cases.
There will be several Quantitative exercises during the course of the semester (approximately one every week). The objective of the assignments is to provide you with a working knowledge of the tools and techniques commonly used in the industry. We will learn how to summarize and visualize data; execute advanced statistical models; and interpret how the output can be used for decision making. We should think of these assignments as learning a new language-- they form the backbone for longer case studies.
Case studies: These go a step beyond simply executing models. Instead, they are designed to challenge you to (1) Understand the problem at an intuitive level, (2) Use simple data analysis and visualization to verify (or falsify) your intuition, (3) Use appropriate statistical analysis to present your arguments. In order to imitate the real life challenges, the case studies are fairly open-ended and provide little step-by-step instructions.
There will be a take-home Final Exam which will be handed to you well in advance. The exam will cover short exercises pertaining to each topic covered and will be similar in spirit to the case studies/exercises covered in the class.
Due Date, Submission, and Grading:All assignments/cases will be handed out on Wednesday and are to be turned in electronically via Blackboard by Noon the following Tuesday. The objective is to have sufficient time to grade your assignments and point out means of improvement in class on Wednesday. From time to time, I will assign students to lead discussion of the assignments/cases on Wednesday (day after the due date).
Questions about the Assignments/Cases/Exam:All questions pertaining to the assignments/exam need to be posted on the discussion board on BB rather than a personal email to me (more on this in the class). This allows for smooth flow of information to everyone and ensures that no student is at a disadvantage. To encourage you get started early, the last chance to raise questions with me will be in class on Monday (day before the due date). You are free to post queries till the last minute but I will stop answering questions approximately 24 hours before the due date. After that, you will have to rely on the generosity of your class mates for hints. I encourage discussions on any topic (including assignments/cases) as long as it happens in an open forum.
This is a social component of the class and there are no grades allocated to class participation. However, a substantial part of the benefit that you will derive from this course is a function of your willingness to expose your viewpoints and conclusions to the critical judgment of the class, as well as your ability to build upon and critically evaluate the judgments of your classmates. You are strongly encouraged to share articles/videos on any topic that you find interesting and voice your opinions. We will use this as an opportunity to implement the scientific approach to decision making on contemporary issues in the news media.
Laptop Use (0% to -10%):Majority of the topics/methods will require use of laptop computers during the class sessions. It may not appear so but it is obvious to the instructor and your fellow students when your computer screen has Facebook rather than the class notes. Please be courteous to people around you and the educational institute by refraining from text messaging, FB, etc. Repeated infringement will result in a penalty of up to 10% of the final grade.
Some of the material covered in the class is fairly advanced. Regardless of your current comfort level with data/technology/statistics, it is my objective to make sure that every student gets a good grasp of the concepts, methods, and implementation (no child left behind!). If at any time you feel falling behind in the class, please contact me. I am happy to work with you individually or in a group to catch up. However, please note that it is your responsibility to seek help.
It is my goal is to make this an excellent course. I encourage you to provide feedback on any issue that can enhance your learning and progress.