# Data science (2020 - 2022)

**Data Science for Everyone**

DS-UA 111 *Prerequisite: high school algebra or permission of the program. Lecture and laboratory. Offered every semester. 4 points.*

Prepares students to participate in today's data-driven world. Students engage with core principles of data analysis and programming and gain practical experience with real-world datasets from the humanities, social sciences, and natural sciences. Introduces ethical, legal, and privacy issues. Aims to transform students from passive consumers of conclusions about data that other people have made to informed, empowered, and critical readers and producers of data-driven insights. Open to students from any discipline with any level of experience in computer science and/or statistics (including no experience at all).

Introduction to Data Science

DS-UA 112 *Prerequisite: Data Science for Everyone (DS-UA 111), or Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2), or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or Introduction to Computer Science (CSCI-UA 101), or permission of the program. Lecture and laboratory. Offered every semester. 4 points.*

Fundamental principles and techniques of the field. Students examine real-world examples and cases so as to place data science techniques in context, to develop data-analytic and inferential thinking, and to illustrate that the discipline is as much an art as it is a science. Students gain hands-on experience with the Python programming language and its associated data analysis libraries. Examines ethical implications surrounding privacy and data sharing, as well as algorithmic decision making for given data science solutions.

**Causal Inference**

DS-UA 201 *Prerequisite: Data Science for Everyone (DS-UA 111) or permission of the program. Recommended corequisite: Introduction to Data Science (DS-UA 112). Lecture and laboratory. Offered once a year. 4 points.*

Provides students with the tools for understanding the relationship between cause and effect. Begins with design and implementation of the data-gathering process (the experiment) and then identifies preconditions required for A to cause B and discusses threats to the validity of less-than-perfect experiments. Other topics: quasi-experiments, regression discontinuities, and contemporary advanced approaches.

**Responsible Data Science**

DS-UA 202 *Prerequisite: Introduction to Data Science (DS-UA 112). Lecture and laboratory. Offered every semester. 4 points.*

The first wave of data science focused on accuracy and efficiency: on what we can do with data. The second wave is about responsibility: what we should and should not do. Addresses the issues of ethics and responsibility in data science, including legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Emphasizes a holistic treatment of the data science lifecycle, beginning with data discovery and acquisition and progressing through data cleaning, integration, querying, analysis, and result interpretation.

**Machine Learning for Language Understanding**

DS-UA 203 *Identical to LING-UA 52. Prerequisites: at least one course with a substantial Python programming component, such as Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or an advanced CSCI-UA or other programming course; Calculus I (MATH-UA 121) or higher, or equivalent; and completion of the probability and/or statistics requirement; or permission of the instructor. Offered every spring. 4 points*.

Covers widely-used machine learning methods for language understanding—with a special focus on 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. Introduces the many approaches that researchers use to teach language to computers. Students gain skills to design and build computational models, to design experiments to test those models, and to read and evaluate results from the scientific literature.

**Advanced Topics in Data Science**

DS-UA 301 *Prerequisites: Introduction to Data Science (DS-UA 112) and completion of the probability and/or statistics requirement, or permission of the program. Lecture and laboratory. Offered every semester. 4 points.*

Time series, deep learning, and other advanced machine learning topics. Provides the theoretical underpinnings of advanced data science techniques, as well as hands-on activities to build a practical toolkit.

## Courses in Other Departments

Consult departmental sections in this Bulletin for full descriptions.

#### COMPUTER SCIENCE

**Introduction to Computer Programming (No Prior Experience) **

CSCI-UA 2 *Prerequisite: three years of high school mathematics or equivalent. No prior computing experience is assumed. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3. Offered in the fall and spring. 4 points.*

**Introduction to Computer Programming (Limited Prior Experience)**

CSCI-UA 3 *Prerequisite: a background in Python or other programming language, or a score of 1 or 2 on the AP Computer Science exam. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Not open to students who have placed into CSCI-UA 101. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3. Offered in the fall and spring. 4 points.*

**Database Design and Implementation**

CSCI-UA 60 *Prerequisite: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Not open to students who have taken Data Management and Analysis (CSCI-UA 479). Offered in the spring. 4 points.*

**Introduction to Computer Science**

CSCI-UA 101 *Prerequisite: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam, or departmental permission assessed by placement exam. Offered in the fall and spring. 4 points.*

**Data Structures**

CSCI-UA 102 *Prerequisite: Introduction to Computer Science (CSCI-UA 101). Offered in the fall and spring. 4 points.*

**Computer Systems Organization**

CSCI-UA 201* Prerequisite: Data Structures (CSCI-UA 102). Offered in the fall and spring. 4 points.*

**Operating Systems**

CSCI-UA 202* Prerequisite: Computer Systems Organization (CSCI-UA 201). Offered in the fall and spring. 4 points.*

**Basic Algorithms**

CSCI-UA 310* Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and Calculus I (MATH-UA 121) or Mathematics for Economics I (MATH-UA 211) or equivalent. Offered in the fall and spring. 4 points.*

**Programming Tools for the Data Scientist **

CSCI-UA 381 *Prerequisites: Data Science for Everyone (DS-UA 111) or equivalent proficiency in Python, and either Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Lecture and laboratory. Offered every semester. 4 points.*

**Introduction to Machine Learning**

CSCI-UA 473 *Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148), and completion of the data science probability and/or statistics requirement. 4 points.*

**Predictive Analytics**

CSCI-UA 475 *Prerequisites: Computer Systems Organization (CSCI-UA 201), Basic Algorithms (CSCI-UA 310), and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148). Offered every year. 4 points.*

**Processing Big Data for Analytics Applications**

CSCI-UA 476 *Prerequisites: Computer Systems Organization (CSCI-UA 201) and Basic Algorithms (CSCI-UA 310). Familiarity with Linux commands and SQL is helpful but is not required. Offered every year. 4 points.*

**Data Management and Analysis
**CSCI-UA 479

*Prerequisite: Data Structures (CSCI-UA 102). Not open to students who have taken Database Design and Implementation*

*(CSCI-UA 60).*

*Offered in the fall and spring. 4 points.*

**Special Topics in Computer Science**

CSCI-UA 480* Topics determine prerequisites. 4 points.*

#### MATHEMATICS

**Discrete Mathematics
**MATH-UA 120

*Prerequisite: a score of 650 or higher on the mathematics portion of the SAT or on either SAT Subject Test in Mathematics, an ACT mathematics score of 30 or higher, a score of 3 or higher on the AP Calculus AB exam or AB subscore, a score of 3 or higher on the AP Calculus BC exam, a grade of C or higher in Algebra and Calculus (MATH-UA 9) or equivalent, or a passing score on the departmental placement exam. Offered every term. 4 points.*

**Calculus I **

MATH-UA 121 *Prerequisite: a score of 650 or higher on the mathematics portion of the SAT or on either SAT Subject Test in mathematics, an ACT mathematics score of 30 or higher, a score of 3 or higher on the AP Calculus AB exam or AB subscore, a score of 3 or higher on the AP Calculus BC exam, a grade of C or higher in Algebra and Calculus (MATH-UA 9) or equivalent, or a passing score on the departmental placement exam. Offered every term. 4 points.*

**Calculus II **

MATH-UA 122 *Prerequisite: Calculus I (MATH-UA 121) or equivalent with a grade of C or better, a score of 4 or higher on the AP Calculus AB or BC exam, or a passing score on the departmental placement exam. Offered every term. 4 points.*

**Calculus III**

MATH-UA 123* Prerequisite: Calculus II (MATH-UA 122) or equivalent with a grade of C or higher, a score of 5 on the AP Calculus BC exam, or a passing score on the departmental placement exam. Offered every term. 4 points.*

**Linear Algebra **

MATH-UA 140 *Prerequisite: a score of 650 or higher on the mathematics portion of the SAT or on either SAT Subject Test in mathematics, an ACT mathematics score of 30 or higher, a score of 3 or higher on the AP Calculus AB exam or AB subscore, a score of 3 or higher on the AP Calculus BC exam, a grade of C or higher in Algebra and Calculus (MATH-UA 9) or equivalent, or a passing score on the departmental placement exam. Offered every term. 4 points.*

**Honors Linear Algebra
**MATH-UA 148

*Prerequisite: a score of 650 or higher on the mathematics portion of the SAT or on either SAT Subject Test in mathematics, an ACT mathematics score of 30 or higher, a score of 3 or higher on the AP Calculus AB exam or AB subscore, a score of 3 or higher on the AP Calculus BC exam, a grade of A-minus or higher in Algebra and Calculus (MATH-UA 9) or equivalent, or a passing score on the departmental placement exam. Offered in the fall and spring. 4 points.*

**Mathematics for Economics I **

MATH-UA 211 *Prerequisite: same as for Calculus I (MATH-UA 121). Offered every term. 4 points.*

**Mathematics for Economics II**

MATH-UA 212 *Prerequisite: completion of Mathematics for Economics I (MATH-UA 211) with a C or higher, or placement by departmental exam. Offered every term. 4 points.*

**Theory of Probability**

MATH-UA 233 *Prerequisite: a grade of C or higher (B or higher strongly recommended) in Calculus III (MATH-UA 123) or Honors Calculus III (MATH-UA 129) or Mathematics for Economics III (MATH-UA 213) (for economics majors) or equivalent, and a grade of C or higher in Linear Algebra (MATH-UA 140) or equivalent. The course is intended for mathematics majors and other students with a strong interest in mathematics and requires fluency in multi-variable integration. Not open to students who have taken Probability and Statistics (MATH-UA 235). Offered every term. 4 points.*

**Mathematical Statistics**

MATH-UA 234 *Prerequisite: a grade of C or higher in Theory of Probability (MATH-UA 233) or equivalent. Not open to students who have taken Probability and Statistics (MATH-UA 235). Offered in the fall and spring. 4 points.*

**Probability and Statistics **

MATH-UA 235 *Prerequisite: a grade of C or better in Calculus II (MATH-UA 122) or Mathematics for Economics II (MATH-UA 212) or equivalent. Not open to students who have taken Theory of Probability (MATH-UA 233) or Mathematical Statistics (MATH-UA 234). Offered in the spring. 4 points.*

**Numerical Analysis**

MATH-UA 252 *Prerequisite: a grade of C or higher in both Calculus III (MATH-UA 123) or Honors Calculus III (MATH-UA 129) or Mathematics for Economics III (MATH-UA 213) (for economics majors) and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148), or equivalent. Offered in the fall and spring. 4 points.*

**Analysis**

MATH-UA 325 *Prerequisites: a grade of C or higher in both Calculus III (MATH-UA 123) or Honors Calculus III (MATH-UA 129) or Mathematics for Economics III (MATH-UA 213) (for economics majors) and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148), or equivalent. Offered in the fall and spring. 4 points.*