MS in Marketing and Retail Science
Dealing with Data & Introduction to Python Programming

Course code: SHBI-GB 7304 B20

Overview

Course Objectives

At a very high level, the course will teach you Python and SQL, plus a few Unix tools that are useful for everyday data handling and processing. At the completion of this course, you should:

• Write simple programs for a variety of data handling tasks (e.g., fetch data from the web, data processing, etc.)
• Retrieve and manage data coming in a variety of formats and from different sources
• Store and query data in relational databases
• Visualize and effectively present data

Software that we will not use or cover

• We do not plan on using R. While it is a very useful open source tool for data analysis and visualization, we can achieve the same results using Python. Furthermore, Python can handle easier bigger datasets, is “cleaner” as a language, and can be used for many more purposes than R. Therefore, to minimize the need to learn multiple tools to achieve the same goal, we standardize on using Python.
• We do not plan on using Tableau, or any other visualization technology (e.g., D3.js). There is a separate Data Visualization course.

Help and Office

• Me: dgode@stern.nyu.edu, 212-998-0021, Office: KMC 10-86.
• TA: Check NYU Brightspace

Topics

• Introduction to programming using Python
• Data modeling and ER model
• Relational databases and SQL
• Basics of data analysis and visualization

Prerequisites

None

Important Information

Since this is a hands-on course, you must bring your laptop to every class with sufficient battery charge. Make sure you can connect to NYU wi-fi.

Attendance and penalty for missing classes

Requiring attendance is necessary for several reasons. First, you incorrectly assume you can catch up on a missed class by watching a recording (if available). Videos do not engage your brain as much as a live class. Second, less than 20% of you watch the recording (if available). You are then lost in class, which provides the wrong signals to me as an instructor. Third, your absence hurts class discussions. Fourth, you miss out on feedback if you do not work through the questions I pose in class. Fifth, I lose the feedback since there are fewer questions.

The policy below will be in effect only after the add/drop period.

Without mandatory attendance, attendance is often below 50%. Therefore, though I dislike doing this, I penalize absences. If you anticipate being absent for good reasons, please email me well in advance. Please enter "Excused" on the attendance sheet described below to avoid the penalty if I approve. If you miss a class due to emergencies and cannot tell me in advance, do not panic. Take care of the emergency first, and then email me. I will permit you to change the "Absent" to "Excused." But if you miss a class without a valid reason, there is a penalty, as stated below.

For sections meeting in 150-190 minute sessions, you will lose one grade (A to A-, A- to B+, B+ to B, B to B-, and so on) for EVERY missed session unless you were explicitly excused via email. Thus, if you miss two class sessions, you will lose two grades, and so on.

For sections meeting in 75-80 minute sessions, you will lose one grade (A to A-, A- to B+, B+ to B, B to B-, and so on) for EVERY TWO missed sessions unless you were explicitly excused via email. Thus, if you miss four class sessions, you will lose two grades, and so on.

Please sit in the same seat in every class and display your name tags. For Zoom classes, you must keep your video on AT ALL TIMES. You must also have a good working headset or mic, as it is extremely rude to be inaudible and force me to ask you to repeat yourself. After entering the class, please mark yourself present in the first 20 minutes on the OneDrive sheet (link posted on Brightspace). You will be marked absent if you are more than 20 minutes late unless it is because of factors beyond your control (traffic, subway, or interviews running late). You will also be marked absent if you leave the class early unless you have my permission or get it afterward. You will get an F in the course if you are caught cheating on the attendance sheet.

Grading

• Five Homeworks: 5% * 5 = 25%
• Final exam: 50%
• Final project: 25%
• Attendance: Please read about the penalty for missing classes above.

Late Assignment Submission Policy

Late submissions (even by 1 minute) will get a zero score because the answers will be posted immediately after the due date and time. No extensions will be granted except for medical or family emergencies. If you have any religious or personal conflicts, please submit the assignments beforehand since the related material will be covered well in advance of the due dates.

Materials

I will distribute Jupyter notebooks. There is required textbook for the course, but the following books are a useful reference for some of the material that I will be covering in class.

• Online version of the class notes on Github. This repository contains material for the class, mainly under the “Introduction to Python” and “SQL” folders.
• Python For Everybody: Exploring Data in Python 3. It is available for free as a PDF on the web (you can also buy a hard copy for $10 on Amazon, or get a Kindle version for $1).
• Learn Python 3 the Hard Way. This book is available for free on the web (you can also get a hard copy if you want for ~$35).
• Learning MySQL, Chapters 4, 5, 6, and 7: This book contains an extensive discussion of MySQL, providing more details on schema design, SQL queries, etc.

Course policies

Unless otherwise noted, we follow the default Stern Policies. Classes are videotaped and a link is posted to NYU Brightspace under the MediaSite tab.

Frequently Asked Questions

• Q: Why Do not we use R/STATA/Matlab/… in this class? My friend says that they are very useful tools for analyzing data, and my internship requires the use of R. A: I agree that all these tools are very useful and should be in the toolkit for any professional who deals with data. However, within the context of a semester-long class, if we attempt to learn all these tools, we will cover everything superficially, and we'll spread ourselves too thin. Python and its libraries is a very mature ecosystem and will give you substantial ability to handle, process, and visualize pretty much anything that you want.
• Q: Should I know programming to take this class? A: No, we will learn programming in Python during the class.
• Q: I know programming and/or SQL. Is this the right class for me? A: It depends on your level. I expect that approximately 40% of the course will focus on teaching you programming and Python, then 40% on databases and SQL, and 20% on a variety of other topics. If you know programming but not Python and are not familiar with SQL, I think that you will get a lot out of this class. If you already know Python but not SQL, it may be worthwhile, but there will be repetition of things that you know. If you are familiar with both programming and SQL, then this is definitely not the class for you.
• Q: Will we learn about big data? A: While we will learn a lot about handling big data sets, most probably we will not cover any “big data” tools, such as Hadoop, Hive, Pig, etc. While it is cool to add these buzzwords in your CV, you will be surprised how far you can go with just a simple relational database and knowledge of SQL, alone. In fact, in most industrial settings that I worked for, SQL is the preferred mode of analysis, not Hadoop, Pig, and other tools like that. Once you add Python in the mix with SQL, your abilities become superpowers. Trust me, “you're going to like the way you look” at the end of the class, even without knowing Hadoop.
• Q: I already know Python, SQL, have used some NLP tools, and I am really interested in learning deeper the following couple of topics…. A: This is not the right class for you. The class is designed to be broad and introductory, not deep and advanced. You should consider taking the data mining class, or some specialized class on the topic of your interest. If you take this class, you are most probably going to be bored, and it will not be a good use of your time. 

Tentative Timeline