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	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. A first-semester programming course, providing an overview of computer systems and an introduction to problem solving and software design using procedural object-oriented programming languages. Coverage includes the software life cycle, as well as algorithms and their role in software design. Students are expected to design, implement, and test a number of programs. 3 hours lecture, 2 hours activity. (002281)
CSCI 211	Programming and Algorithms II	4.0	FS
Prerequisite: CSCI 111 with a grade of C or higher. A second semester object-oriented programming course in computer science that emphasizes problem solving. This course continues the study of software specification, design, implementation, and debugging techniques while introducing abstract data types, fundamental data structures and associated algorithms. Coverage includes dynamic memory, file I/O, linked lists, stacks, queues, trees, recursion, and an introduction to the complexity of algorithms. Students are expected to design, implement, test, and analyze a number of programs. 3 hours lecture, 2 hours activity. (002282)
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. Limits and continuity. The derivative and applications to related rates, maxma and minima, and curve sketching. Transcendental functions. An introduction to the definite integral and area. 4 hours discussion. This is an approved General Education course. (005506)
MATH 121	Analytic Geometry and Calculus	4.0	FS
Prerequisite: MATH 120. The definite integral and applications to area, volume, work, differential equations, etc. Sequences and series, vectors and analytic geometry in 2 and 3-space, polar coordinates, and parametric equations. 4 hours discussion. (005507)
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. Basic concepts of probability and statistics with emphasis on models used in science and technology. Probability models for statistical estimation and hypothesis testing. Confidence limits. One- and two-sample inference, simple regression, one- and two-way analysis of variance. Credit cannot be received for both MATH 314 and MATH 315. 4 hours discussion. This course requires the use of a laptop computer and appropriate software. (005533)
MATH 456	Applied Statistical Methods II	3.0	S2
Prerequisites: MATH 314 or MATH 315. Advanced topics in applied statistics including multiple and logistic regression, multivariate methods, multi-level modeling, repeated measures, and others as appropriate. The statistical programming language R is used. Appropriate for biology, agriculture, nutrition, business, psychology, social science and other majors. 3 hours discussion. (005570)
MATH 490	Data Science Capstone	1.0 -3.0	FS
Prerequisites: MATH 485, senior standing, approved project, enrollment in the Data Science Certificate Program. Students work independently to provide a service in the form of a data product to a local business, researcher, or community member. Students provide status reports at weekly meetings and present their finished project to a group of peers at the end of the semester in an appropriate venue such as at an undergraduate seminar series or poster symposium. You may take this course more than once for a maximum of 6.0 units. (021898)

1 course selected from:

SUBJ NUM	Title	Units	Semester Offered
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. Offers an intensive introduction to discrete mathematics as used in computer science. Topics include sets, relations, propositional and predicate logic, basic proof methods including mathematical induction, digital logic circuits, complexity of algorithms, elementary combinatorics, and solving linear recurrence relations. 3 hours discussion. (005550)
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. Offers an intensive introduction to discrete mathematics as used in computer science. Topics include sets, relations, propositional and predicate logic, basic proof methods including mathematical induction, digital logic circuits, complexity of algorithms, elementary combinatorics, and solving linear recurrence relations. 3 hours discussion. (005550)
MATH 235	Elementary Linear Algebra	3.0	FS
Prerequisites: MATH 121. Matrices, determinants, cartesian n-space (basis and dimension of a subspace, rank, change of basis), linear transformations, eigenvalues. Numerical problems will be emphasized. 3 hours discussion. (005553)

1 course selected from:

SUBJ NUM	Title	Units	Semester Offered
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. Data Science is the science of learning from data in order to gain useful predictions and insights. The course provides an overview of the wide area of data science, with a particular focus on the tools required to store, clean, manipulate, visualize, model, and ultimately extract information from various sources of data. Topics include the analytics life cycle, data integration and modeling in R/Python, relational databases and SQL, text processing and sentiment analysis, and data visualization. Emphasis is placed on reproducible research, code sharing, version control, and communicating results to a non-technical audience. 3 hours discussion. (021756)
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. Data Science is the science of learning from data in order to gain useful predictions and insights. The course provides an overview of the wide area of data science, with a particular focus on the tools required to store, clean, manipulate, visualize, model, and ultimately extract information from various sources of data. Topics include the analytics life cycle, data integration and modeling in R/Python, relational databases and SQL, text processing and sentiment analysis, and data visualization. Emphasis is placed on reproducible research, code sharing, version control, and communicating results to a non-technical audience. 3 hours discussion. (021756)

1 course selected from:

SUBJ NUM	Title	Units	Semester Offered
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. Getting connected to current events in Data Science and building an online presence. Ethics of predictive analytics and privacy and open data. Reporting and dissemination of research using interactive dashboards and web-publishing. Introduction to current scalable technologies to handle Big Data. Introduction to advanced statistical analysis and machine learning techniques for Data Science. 3 hours lecture. (021890)
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. Getting connected to current events in Data Science and building an online presence. Ethics of predictive analytics and privacy and open data. Reporting and dissemination of research using interactive dashboards and web-publishing. Introduction to current scalable technologies to handle Big Data. Introduction to advanced statistical analysis and machine learning techniques for Data Science. 3 hours lecture. (021890)

1 course selected from:

SUBJ NUM	Title	Units	Semester Offered
CINS 370	Introduction to Databases	3.0	FS
Prerequisite: CSCI 211 with a grade of C or higher. This course provides an introduction to the theory and methodology for database design and implementation. Topics may include a survey/lecture component as well as a project component. The survey component covers entity- relationship modeling, relational algebra and calculus theories, data definition and data manipulation languages such as SQL, file structures, transactions, concurrency control, recovery, tuning and optimization, and object-oriented databases. The project entails requirements definition, design, and implementation of a database application. 2 hours discussion, 2 hours activity. (002338)
CSCI 344	Shell Programming	3.0	SP
Prerequisite: CSCI 211 with a grade of C or higher. This course examines the tools that allow software engineers to automate frequently performed operations and workflows, manipulate text and data, and develop software more quickly and easily than compiled languages. Shell programming in BASH or a similar shell, text processing languages such as sed and awk, and a scripting language such as Python or Ruby are covered. This course is recommended for students pursing careers in software development and/or information systems. 2 hours discussion, 2 hours activity. (002330)
CSCI 580	Artificial Intelligence	3.0	FS
Prerequisite: CSCI 311 with a grade of C or higher. An introduction to the basic principles, techniques, and applications of Artificial Intelligence. Coverage includes knowledge representation, logic, inference, problem solving, search algorithms, game theory, perception, learning, planning, and agent design. Students will program with AI language tools. Additional areas may include expert systems, machine learning, natural language processing, and computer vision. 3 hours discussion. (002360)
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). An introduction to computational methods for Next Generation Sequencing data analysis. Topics include mapping sequenced reads back to a reference genome; approximate string matching; intro to biostatistics; probability distribution, hypothesis testing; identification of SNPs (single polymorphisms); analysis of RNA-seq data; mapping RNA-seq reads, identification of splice-junctions, analysis of gene expression; genome-wide associative analysis of methylation and gene expression. 3 hours discussion. (021619)
MATH 344	Graph Theory	3.0	S1
Prerequisites: MATH 121; CSCI 217, MATH 217, or MATH 330. An introduction to graph theory and network theory. Directed graphs, trees, connectivity, duality, coloring, and planarity are studied both from a theoretical perspective as well as with respect to efficient algorithms. 3 hours discussion. (005591)
MATH 461	Numerical Analysis	3.0	SP
Prerequisites: MATH 220 or MATH 260; completion of computer literacy requirement. Approximation; numerical integration; numerical solution of ordinary and partial differential equations; interpolation and extrapolation. 3 hours discussion. (005584)
MATH 480	Mathematical Modeling	3.0	SP
Prerequisites: MATH 235, MATH 260. The translation of real world phenomena into mathematical language. Possible applications include population and competing species models, mathematical theories of war, traffic flow, river pollution, water waves and tidal dynamics, probabilistic and simulation models. 3 hours discussion. (005592)