| Call Number | 16738 |
|---|---|
| Day & Time Location |
W 2:20pm-5:35pm To be announced |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Hardeep Johar |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | The collection, interpretation, and analysis of data has always been a central pillar of business decision making. Historically, this has followed a two step process, statisticians gather data, organize it, run analytics and prepare reports. At some future point, a decision maker examines these reports, interprets the results and makes decisions. However, with the advent of powerful and inexpensive computing platforms, the collection and analysis of data has moved into the continuous decision making cycle itself, with decisions being constantly updated as new data is instantly analyzed and acted upon. Consequently, decision makers can no longer isolate themselves from the grungy side of data and they need to know where the data originated, how it was transformed, what is the nature, the strengths and the limitations of the analytical techniques used. Today, to be effective, decision makers need an intuitive understanding of the statistics, the math, and the programming that underlie this “live” analytical and decision making process. The objective of this course is to give you an understanding of the analytical side of the decision making cycle, focusing on programming as the element that “glues” the collection, transformation, visualization, and analysis of data. We will see how to get data from common sources (APIs, web scraping), examine the rudiments of data visualization (charts, maps), and get an intuitive understanding of the types of analytical tools in use today (machine learning, deep learning, analysis of networks, analyzing natural language texts). With its extensive collection of libraries, Python is fast becoming the platform of choice for data analytics so Python will be our language for this course. The course is very hands on, and you should expect a lot of programming work, all of it fairly intense. A basic understanding of how to write programs in Python is therefore a must for this class. But, the primary takeaway from the course is not the programming but rather an understanding of the mechanics, the vocabulary, and the techniques in data analytics. Even if you find programming a frustrating and head banging exercise, you can get a lot out of the class (if you’re willing to suffer a bit!). STUDENTS WILL NEED TO EITHER HAVE PASSED B8154 (PYTHON FOR MBAS) OR THE ADVANCED PYTHON QUALIFICATION EXAM (https://www8.gsb.columbia.edu/courses/python#advanced_qual) BEFORE THE FIRST DAY OF CLASS |
| Web Site | Vergil |
| Department | Decision, Risk and Operations |
| Enrollment | 42 students (50 max) as of 12:06PM Tuesday, December 3, 2024 |
| Subject | Decision, Risk & Operations Management |
| Number | B8139 |
| Section | 001 |
| Division | School of Business |
| Open To | Business, Journalism |
| Section key | 20251DROM8139B001 |