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Courses 1. Data Science minor: Five four-point courses (20 points) a. Data Science for Everyone (DS-UA 111) i. Data Science for Everyone is a foundational course that prepares students to participate in the datadriven world that we are all experiencing. It develops programming skills in Python so that students can write programs to summarize and compare real-world datasets. Building on these data analysis skills, students will learn how draw conclusions and make predictions about the data. Students will also explore related ethical, legal, and privacy issues.

b. Introduction to Data Science (DS-UA 112) i. Introduction to Data Science offers the fundamental principles and techniques of data science. Students will develop a toolkit to examine real world examples and cases to place data science techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. In addition, students will gain hands-on experience with the Python programming language and its associated data analysis libraries. Students will also consider ethical implications surrounding privacy, data sharing, and algorithmic decision making for a given data science solution. PREREQUISITE: DS-UA 111 OR CSCI-UA 2 OR CSCI-UA 3 OR permission of program. ii. Spring 2020 webpage / https://wp.nyu.edu/idss20/

c. Causal Inference (DS-UA 201) i. Causal Inference provides students with the tools for understanding causation, i.e., the relationship between cause and effect. We will start with the situation in which you are able to design and implement the data gathering process, called the experiment. We will then define causation, identify preconditions required for A to cause B, show how to design perfect experiments, and discuss how to understand threats to the validity of less-than-perfect experiments. In this course, we will cover experimental design and then turn to those careful approaches, where we will consider such approaches as quasi-experiments, regression discontinuities, differences in differences, and contemporary advanced approaches.

d. Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) i. Prerequisite: Three years of high school mathematics or equivalent. No prior computer experience assumed. Students with any programming experience should consult with the computer science department before registering. Students who have taken or are taking CSCI-UA 101 will not receive credit for this course. Note: This course is not intended for computer science majors, although it is a prerequisite for students with no previous programming experience who want to continue in CSCI-UA 101. Offered every semester. 4 points. An introduction to the fundamentals of computer programming, which is the foundation of Computer Science. Students design, write and debug computer programs. No knowledge of programming is assumed.

e. Either Database Design and Implementation (CSCI-UA 60) i. Introduces principles and applications of database design and working with data. Students use python as they prepare, analyze and work with data; SQL to study the principles an implementations of relational databases; and are introduced to other database paradigms such as NoSQL. Students apply these principles to computer systems in general and in their respective fields of interest. ii. CSCI-UA 2 WITH GRADE OF C OR BETTER. OR CSCI-SHU 101. Prerequisite: CSCIUA.0002. Students who have taken CSCI-UA.480 Data Management and Analysis are NOT permitted to take this course. Introduces principles and applications of database design. Students learn to use a relational database system; learn Web implementations of database designs; and write programs in SQL. Students explore principles of database design and apply those principles to computer systems in general and in their respective fields of interest. Registration is open to CAS, Gallatin, Liberal Studies, Abu Dhabi and Shanghai students on Monday, November 11, 2019. University wide registration will be available Monday, November 18, 2019.

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f. Programming Tools for the Data Scientist (CSCI-UA 381) i.

2. Machine Learning for Language Understanding (DS-UA 203) a. This course covers widely-used machine learning methods for language understanding—with a special focus on machine learning methods based on artificial neural networks—and culminates in a substantial final project in which students write an original research paper in AI or computational linguistics. If you take this class, you'll be exposed only to a fraction of the many approaches that researchers have used to teach language to computers. However, you'll get training and practice with all the research skills that you'll need to explore the field further on your own. This includes not only the skills to design and build computational models, but also to design experiments to test those models, to write and present your results, and to read and evaluate results from the scientific literature.

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Prerequisites: At least one course with a substantial Python programming component (i.e., CSCI-UA 2 or an advanced CSCI-UA or other programming course); basic experience with calculus (i.e., MATH-UA 121, 122, or 123 or credit for testing out of one or more of these courses), and probability theory (e.g. MATH-UA 233), or permission of the instructor. First offered in spring 2020, and every year thereafter.

3. Economics minor: Five four-point courses (20 points)...