Biology - Major in Biology Program

Required courses:
CSC 120 Introduction to Programming with Python

This course introduces the fundamentals of computer programming using the Python programming language. Topics include: variables, types, and assignment; iteration and conditional execution; functions, modules, and structured design; complex types and data structures. Students will also learn to use AI tools to assist with programming tasks. 3 credits.

CSC 121 Advanced Python with Data Science Applications

This course continues the exploration of the Python programming language, with an emphasis on techniques for writing larger, more complex software. Topics include class design, module and package organization, data handling, networking, the use of third-party packages and frameworks, and web services and application deployment. Examples from machine learning and data science will be considered. Prerequisite: CSC 120. 3 credits.

DSC 340 Machine Learning & Predictive Analytics

Study of various modeling techniques including linear mixed models and neural networks using the statistical programming language R or Python. This course also applies Bayesian techniques to predictive models. DSC 340 covers the statistical content from the SOA e-module ATPA-Advanced Topics in Predictive Analytics Prerequisite:Take CSC-120; MAS-270 or MAS-372; MAS-222 or MAS-261. 3 credits.

DSC 350 Deep Learning

An examination of machine learning techniques derived from research in artificial neural networks such as feedforward, recurrent, and convolutional neural networks. Applications to problems in supervised, unsupervised, and reinforcement learning environments, such as image or speech recognition will be considered. Prerequisites: CSC 121, MAS 372. Prerequisite: CSC 121; MAS 270 or 372; MAS 222 or 261. 3 credits.

MAS 111 Analysis I

A calculus sequence for department majors and other students desiring a rigorous introduction to elementary calculus. Fulfills requirement: Quantitative Reasoning. Prerequisite: MAS 102 at LVC, or satisfactory score on the math readiness test. This test is administered during New Student Advising Days for incoming students or can be taken by arrangement with the math department (717-867-6080) Co-requisite: MAS 113. 4 credits.

MAS 112 Analysis II

Second semester of a calculus sequence for department majors and other students desiring a rigorous introduction to elementary calculus. Fulfills requirement: Quantitative Reasoning. Prerequisite: MAS 111; Corequisite: MAS 114. 4 credits.

MAS 113 Introduction to Mathematical Thinking I

An introduction to college mathematics for potential mathematical science majors. Corequisite: MAS 111. 1 credit.

MAS 114 Introduction to Mathematical Thinking II

Second semester. Introduction to college mathematics for potential mathematical science majors. Corequisite: MAS 112. 1 credit.

MAS 202 Foundations of Mathematics

Introduction to logic, set theory and proof techniques. Prerequisites: MAS 222 or ASC 281. 3 credits.

MAS 222 Linear Algebra

An introduction to linear algebra including systems of equations, vectors spaces and linear transformations. Prerequisites: MAS 112 or MAS 261. 3 credits.

MAS 261 Calculus III

Multivariate calculus including partial differentiation, multiple integration, vector fields and vector functions. Prerequisites: MAS 112 or MAS 162. 3 credits.

MAS 270 Statistics and Data Analysis

This course introduces the fundamentals of statistical data analysis. Topics include basic probability; common statistical models; significance testing; regression modeling and working with high dimensional datasets. Fulfills requirement: Quantitative Reasoning. Prerequisite: MAS 111 or MAS 161. 3 credits.

MAS 371 Statistical Inference

An introduction to the mathematical foundations of probability and statistics, with a focus on estimation, sampling distributions, and hypothesis testing. This course satisfies the Society of Actuaries' Validation through Educational Experience (VEE) criteria for mathematical statistics. Prerequisites: MAS 202; and ASC 281 or MAS 270. 3 credits.

MAS 372 Statistical Modeling

Study of various modeling techniques including regression, decision trees, unsupervised learning, and time series methods with implementation in the computer language R. The course also provides an introduction to generalized linear models and generalized additive models. MAS 372 covers the material on SOA exam SRM - Statistics for Risk Modeling. Prerequisite: MAS 270 or ASC 281. 3 credits.