## DATA SCIENCE

## DATA SCIENCE

The field of Interdisciplinary Data Science (IDS) deals with the theories, methodologies and tools of applying statistical concepts and computational techniques to various data analysis problems related to science, engineering, medicine, business, etc. The objective is to inspect, clean, transform and model data in order to discover useful information, suggest conclusions and support decision-making. It is an emerging topic that plays a critical role in almost every discipline of today’s science and technology and has become an indispensable component.

Interdisciplinary data science is a highly interdisciplinary field. Its methodologies are mostly derived from statistics theories. The computational algorithms for implementing these statistical methodologies are based upon numerical computation and optimization, and are often executed on a large-scale hardware platform composed of massive computing units and storage devices. When applying data analysis to a specific application problem, it further requires disciplinary knowledge and expertise. To accomplish these ambitious goals, there is an immediate need to “invent” a radically new degree program that can break down the traditional boundaries between disciplines and, consequently, facilitate fundamental breakthroughs and innovations.

### Major Requirements

*(Not every course listed is offered every semester, and the course list will be updated periodically. Please refer to the online Course Catalog for Courses offered in 2019-2020.)*

__Divisional Foundation Courses__

Option 1: only applicable to Class of 2022 who have taken INTGSCI 101 & 102

Course Code | Course Name | Course Credit |
---|---|---|

MATH 101 | Calculus (was Mathematical Foundations 1) | 4 |

MATH 201 | Multivariable Calculus (was Mathematical Foundations 2) | 4 |

INTGSCI 101 | Integrated Science 1 | 4 |

INTGSCI 102 | Integrated Science 2 | 4 |

Option 2: only applicable to Class of 2022 who have taken INTGSCI 101

Course Code | Course Name | Course Credit |
---|---|---|

MATH 101 | Calculus (was Mathematical Foundations 1) | 4 |

MATH 201 | Multivariable Calculus (was Mathematical Foundations 2) | 4 |

INTGSCI 101 | Integrated Science 1 | 4 |

And choose two from the following courses |
||

BIOL 110 | Integrated Science – Biology | 4 |

CHEM 110 * | Integrated Science – Chemistry | 4 |

CHEM 120 * | Core Concepts in Chemistry: An Environmental Perspective | 4 |

PHYS 121 | Integrated Science - Physics | 4 |

* Students can choose one from CHEM 110 and CHEM 120 but not both. |

Option 3: Applicable to Class of 2023 and any student who has not taken INTGSCI 101

Course Code | Course Name | Course Credit |
---|---|---|

MATH 101 | Calculus (was Mathematical Foundations 1) | 4 |

MATH 201 | Multivariable Calculus (was Mathematical Foundations 2) | 4 |

BIOL 110 | Integrated Science – Biology | 4 |

CHEM 110 | Integrated Science – Chemistry | 4 |

PHYS 121 | Integrated Science - Physics | 4 |

__Interdisciplinary Courses__

Course Code | Course Name | Course Credit |
---|---|---|

COMPSCI 201 | Introduction to Programming and Data Structures | 4 |

STATS 302 | Principles of Machine Learning | 4 |

STATS 303 | Statistical Machine Learning | 4 |

STATS 401 | Data Acquisition and Visualization | 4 |

STATS 402 | Interdisciplinary Data Analysis | 4 |

**Disciplinary Courses**

Course Code | Course Name | Course Credit |
---|---|---|

MATH 202 | Linear Algebra | 4 |

MATH 205 | Probability and Statistics (was Mathematical Foundations 3) | 4 |

STATS 210 | Probability, Random Variables and Stochastic Processes | 4 |

COMPSCI 301 | Algorithms and Databases | 4 |

MATH 304 | Numerical Analysis and Optimization | 4 |

MATH 305 | Advanced Linear Algebra | 4 |

**Electives**

Courses listed in the table below are recommended electives for the major and the course list will be updated periodically. Students can also select other courses in different divisions as electives.

Course Code | Course Name | Course Credit |
---|---|---|

STATS 102 | Introduction to Data Science | 4 |

COMPSCI 207 | Image Data Science | 4 |

STATS 304 | Bayesian and Modern Statistics | 4 |

COMPSCI 302 | Computer Vision | 4 |

COMPSCI 303 | Search Engines | 4 |

COMPSCI 304 | Speech Recognition | 4 |

COMPSCI 401 | Cloud Computing | 4 |

STATS 403 | Deep Learning | 4 |

STATS 404 | Probabilistic Graphical Models | 4 |

COMPSCI 402 | Artificial Intelligence | 4 |

### Career Path

This major prepares graduates for advanced study in computer science, math, and statistics and for careers in fields such as science, engineering, health care, finance and economics as well as quantitative social science.