Intro to Data Science

CSC 599.70 · Fall 2019 · City College of New York

Instructor Grant Long
Course Email
Lecture Monday 6:30pm-9:00PM, Room 5/110
Credits & Hours 3 credits, 3 hours
Office Hours By appointment at 130 Fifth Ave
Syllabus Available here


The following resources will be crucial to the class. All are available to students at no cost.

About This Course

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%) + Assignments (30%) + Midterm Exam (30%) + Participation & Attendance (10%)

    • Project. A group project that will be due on December 7 in advance of the final class. Students will be expected to work on the project during the second half of the class and will be required to demonstrate their progress during the semester. Grades will be assigned on the basis of overall project quality, demonstration of core principles taught in the class, and individual contributions to the group's effort. Details on the project are here. Project team assignments are here.
    • Assigments. This class includes short, frequent assignments to check comprehension. Most assignments will be assigned through DataCamp, access to which will be made to all students, free of charge. All assignments and quizzes will be graded on a 10-point scale. All quizzes will be announced in advance of class.
    • Exam. A midterm exam will be held in November and will focus on broad concepts the course has surveyed thus far. The format will mimic the style of questions frequently asked in interviews for data-related roles.
    • Participation & Attendance. Students are expected to attend class and be active participants in discussion. This includes, but is not limited to, discussing assigned readings and sharing ideas during classroom exercises.
Important Dates
  • Project Teams Formed, October 16.
  • Midterm Exam, November 11.
  • Project Preliminary Analysis Due, November 23.
  • Project Final Analysis Due, December 7.
Recommended Texts and Materials
  • Required Text: Data Science from Scratch, Joel Grus. 2nd Edition, May 2019 (O'Reilly). Available online.
  • Additional required readings and videos will be made available to students in advance of each week's assignments. All will be availble online at no cost.
  • In addition to the required materials, students may find the following resources helpful in supplementing course materials:
    • Recommended Text: Foundations of Data Science, Avrim Blum, John Hopcroft, and Ravindran Kannan. January 2018. Available free online here.
    • Recommended Text: Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani and Jerome Friedman. 2nd Edition, 2009 (Springer). Available free online here.
    • Recommended Text: Python for Data Analysis, Wes McKinney. 2nd Edition, October 2017 (O'Reilly). Available online.

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.


Subject to revision.