Data Science Minor
The data science minor covers the foundations of data science from both a theoretical and practical perspective. It is directed at undergraduates enrolled in any major at Seaver College.
The minor has two tracks: applied and advanced. The applied track is intended for students to gain a more practically guided knowledge of data science, while the advanced track has a more rigorous mathematics component and culminates with an in-depth course on machine learning. Computer Science/Mathematics majors may elect to take only the advanced track of the data science minor and must complete both computer science elective courses, CoSc 425 and CoSc 465.
Minor Tracks
Applied Track
COSC 101 - Programming Principles I with Python (3)
Introduction to programming with the JavaScript language. Data classes: number, string, and Boolean. HTML/CSS interface. Programming constructs: sequential, conditional, iterative, nested conditional, nested iterative. Run-time analysis. Functions: parameter passing mechanisms, function libraries. Data structures: one- and two- dimensional arrays, objects.
COSC 121 - Programming Principles II (3)
Introduction to object-oriented programming with the C++ Language. Recursion -- basic algorithms, array searching and sorting. Dynamic storage allocation -- pointer types, linked lists and binary search trees as abstract data types. Classes -- objects, abstract classes, inheritance and polymorphism, linked lists and binary trees as classes.
COSC 200 - Computational Methods for Image Analysis (4)
Signal and color/pixel/voxel image representation. Medical imaging acquisition. Introduction to Deep Learning. Filtering and convolution, low-pass/high-pass filters, bilateral filtering. Local feature detection and description. Morphological image processing, radon transform, geometric image transformation, interpolation. Image comparison/categorization.
COSC 210 - Introduction to Machine Learning (4)
Linear regression/classification. Decision trees. Support vector machines. Spectral methods, k- means clustering. Neural networks. Deep learning, gradient descent, back- propagation, error evaluation/cross-validation.
COSC 220 - Applied Data Science (3)
Project-based class using data science on a real-world problem from start (design, data- preparation/pre-processing) to finish (report of the results). This culminating practicum course for the Applied Data Science minor builds on one or more of the prerequisites. Topics include, but are not limited to, data mining, text mining, and applied machine learning.
Plus one of the following courses related to statistics: Math 316, Math 350, Psyc
250, Soc 250, PoSc 250, Econ 212, BA 216, Com 240.
Advanced Track
COSC 101 - Programming Principles I with Python (3)
Introduction to programming with the JavaScript language. Data classes: number, string, and Boolean. HTML/CSS interface. Programming constructs: sequential, conditional, iterative, nested conditional, nested iterative. Run-time analysis. Functions: parameter passing mechanisms, function libraries. Data structures: one- and two-dimensional arrays, objects.
COSC 121 - Programming Principles II (3)
Introduction to object-oriented programming with the C++ Language. Recursion -- basic algorithms, array searching and sorting. Dynamic storage allocation -- pointer types, linked lists and binary search trees as abstract data types. Classes -- objects, abstract classes, inheritance and polymorphism, linked lists and binary trees as classes.
COSC 200 – Computational Methods for Image Analysis (4)
Signal and color/pixel/voxel image representation. Medical imaging acquisition. Introduction to Deep Learning. Filtering and convolution, low-pass/high-pass filters, bilateral filtering. Local feature detection and description. Morphological image processing, radon transform, geometric image transformation, interpolation. Image comparison/categorization.
COSC 210 - Introduction to Machine Learning (4)
Linear regression/classification. Decision trees. Support vector machines. Spectral methods, k-means clustering. Neural networks. Deep learning, gradient descent, back- propagation, error evaluation/cross-validation.
Math 260 - Linear Algebra (4)
Systems of linear equations and linear transformations; matrix determinant, inverse, rank, eigenvalues, eigenvectors, factorizations, diagonalization, singular value, decomposition; linear independence, vector spaces and subspaces, bases, dimensions; inner products and norms, orthogonal projection, Gram-Schmidt process, least squares; applications; numerical methods, as time follows.
COSC 230 – Advanced Machine Learning (3)
Graph theory. Probabilistic graphical models. Bayes theorem. Parametric discrete Gaussian message passing. Non-parametric message passing.
Plus one of the following courses related to statistics: Math 316, Math 350, Psyc
250, Soc 250, PoSc 250, Econ 212, BA 216, Com 240.
What Natural Science Division Grads Are Doing
Data collected from the 2023 class within six months of graduation.
Employed Full-Time or Part-Time
Admitted to Graduate School
Full-Time Volunteer or Other Activity
Total Settled Graduates
Why Pepperdine
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Located in Malibu, CA
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