# The Certificate in Data Science

Data Science is at the three-way intersection of Statistics, Computer Science, and a third domain such as Business or Biology. Data scientists have the computational skills to extract different types and quantities of data from multiple sources, ensure consistency of the data, create visualizations, and build data products for the information consumer to use. They have the statistical knowledge to build mathematical models and ensure the validity of the data and results. They also have the domain knowledge to gain domain specific insights from the data, and to communicate the results of their work in a verbal or dashboard-like format to non-technical stakeholders. This certificate provides students with the necessary proficiency in both the Statistics and Computer Science domains and a framework to apply these skills in an interdisciplinary manner.

Students must have completed Introduction to Data Science (CSCI/MATH 385) with a grade of C or higher before applying for admission to the program.

## Course Requirements for the Certificate: 36-38 units

The following courses, or their approved transfer equivalents, are required of all candidates for this certificate.

**7 courses required:**

SUBJ NUM | Title | Sustainable | Units | Semester Offered | Course Flags |
---|---|---|---|---|---|

CSCI 111 | Programming and Algorithms I | 4.0 | FS | ||

Prerequisite: MATH 109, MATH 119 (or high school equivalent), or MATH 120; or a passing score on the Math department administered calculus readiness exam. | |||||

CSCI 211 | Programming and Algorithms II | 4.0 | FS | ||

Prerequisite: CSCI 111 with a grade of C or higher. | |||||

MATH 120 | Analytic Geometry and Calculus | 4.0 | FS | GE | |

Prerequisites: GE Mathematics/Quantitative Reasoning Ready; both MATH 118 and MATH 119 (or college equivalent); first-year freshmen who successfully completed trigonometry and precalculus in high school can meet this prerequisite by achieving a score that meets department guidelines on a department administered calculus readiness exam. | |||||

MATH 121 | Analytic Geometry and Calculus | 4.0 | FS | ||

Prerequisite: MATH 120. | |||||

MATH 314 | Probability and Statistics for Science and Technology | 4.0 | FS | ||

Prerequisites: MATH 121; and one of the following: CINS 110, CSCI 111, MATH 130 (may be taken concurrently), MATH 230 or MECH 208. | |||||

MATH 456 | Applied Statistical Methods II | 3.0 | S2 | ||

Prerequisites: MATH 314 or MATH 315. | |||||

MATH 490 | Data Science Capstone | 1.0 -3.0 | FS | ||

Prerequisites: MATH 485, senior standing, approved project, enrollment in the Data Science Certificate Program. |

**1 course selected from:**

SUBJ NUM | Title | Sustainable | Units | Semester Offered | Course Flags |
---|---|---|---|---|---|

CSCI 217 | Discrete Mathematics | 3.0 | FS | ||

Prerequisites: GE Mathematics/Quantitative Reasoning Ready, CSCI 111 with a grade of C or higher (may be taken concurrently), MATH 119 (or equivalent). This course is also offered as MATH 217. | |||||

MATH 217 | Discrete Mathematics | 3.0 | FS | ||

Prerequisites: GE Mathematics/Quantitative Reasoning Ready, CSCI 111 with a grade of C or higher (may be taken concurrently), MATH 119 (or equivalent). This course is also offered as CSCI 217. | |||||

MATH 235 | Elementary Linear Algebra | 3.0 | FS | ||

Prerequisites: MATH 121. |

**1 course selected from:**

SUBJ NUM | Title | Sustainable | Units | Semester Offered | Course Flags |
---|---|---|---|---|---|

CSCI 385 | Introduction to Data Science | 3.0 | FA | ||

Prerequisites: CSCI 111, MATH 130, or MATH 230; MATH 109 or MATH 120. This course is also offered as MATH 385. | |||||

MATH 385 | Introduction to Data Science | 3.0 | FA | ||

Prerequisites: CSCI 111, MATH 130, or MATH 230; MATH 109 or MATH 120. This course is also offered as CSCI 385. |

**1 course selected from:**

SUBJ NUM | Title | Sustainable | Units | Semester Offered | Course Flags |
---|---|---|---|---|---|

CSCI 485 | Advanced Topics in Data Science | 3.0 | SP | ||

Prerequisites: CSCI 385 or MATH 385; MATH 456 (may be taken concurrently). This course is also offered as MATH 485. | |||||

MATH 485 | Advanced Topics in Data Science | 3.0 | SP | ||

Prerequisites: CSCI 385 or MATH 385; MATH 456 (may be taken concurrently). This course is also offered as CSCI 485. |

**1 course selected from:**

SUBJ NUM | Title | Sustainable | Units | Semester Offered | Course Flags |
---|---|---|---|---|---|

CINS 370 | Introduction to Databases | 3.0 | FS | ||

Prerequisite: CSCI 211 with a grade of C or higher. | |||||

CSCI 344 | Shell Programming | 3.0 | SP | ||

Prerequisite: CSCI 211 with a grade of C or higher. | |||||

CSCI 580 | Artificial Intelligence | 3.0 | FS | ||

Prerequisite: CSCI 311 with a grade of C or higher. | |||||

CSCI 582 | Bioinformatics | 3.0 | SP | ||

Prerequisites: CSCI 311 with a grade of C or higher; MATH 105, MATH 314, or MATH 350 (may be taken concurrently). | |||||

MATH 344 | Graph Theory | 3.0 | S1 | ||

Prerequisites: MATH 121; CSCI 217, MATH 217, or MATH 330. | |||||

MATH 461 | Numerical Analysis | 3.0 | SP | ||

Prerequisites: MATH 220 or MATH 260; completion of computer literacy requirement. | |||||

MATH 480 | Mathematical Modeling | 3.0 | SP | ||

Prerequisites: MATH 235, MATH 260. |