| Instructor | Grant Long |
|---|---|
| Course Email | itds.ccny@gmail.com |
| Lecture | Monday 6:30pm-9:00PM, NAC 5-123 |
| Credits & Hours | 3 credits, 3 hours |
| Office Hours | Varies, by appointment |
| Syllabus | Available here |
This course consists of a survey of analytical tools and concepts in data science, with goal of equipping students with an understanding of the best practices used by professional data scientists and analysts in top companies in technology, finance, and media. The course begins with an overview of fundamentals in data handling and exploratory data analysis, followed by an introduction to core concepts in statistical modeling and machine learning, and concludes with a brief introduction advanced concepts in data science.
Students will work with a wide variety of real world data sets throughout the course in order to gain hands on experience. Emphasis will be placed on frequent practice through writing and reviewing code each week. In addition, students will be assigned and expected to discuss short reading assignments ranging from academic reviews of popular topics in analytics as well as data science and engineering blog posts from companies such as Airbnb, Spotify, and Facebook. Tasks and readings will aim to demystify the work of data teams in the real world, and familiarize students with the concepts and resources needed to secure and succeed in analytical roles.
Project (30%) + Homework & Quizzes (30%) + Midterm Exam (30%) + Participation (10%)
The CUNY Policy on Academic Integrity governs behavior in this class. Academic dishonesty is prohibited in the City University of New York and is punishable by penalties, including failing grades, suspension, and expulsion.