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.

CSC 241 Programming II

A continuation of CSC 121, students will extend and deepen their understanding of programming using C++. The course will cover three main topics: Object-oriented software organization; design and implementation of elementary data structures and algorithms; the mathematical tools and techniques required for complexity analysis. Prerequisite: CSC 121, or by permission of the instructor. 3 credits.

CSC 242 Algorithms and Data Structures

A study of the design, analysis, and application of data structures and algorithms. Trees, graphs, heaps, hash tables, and other structures will be considered. Several mathematical techniques (e.g., complexity analysis, recurrence relations, and induction) will be studied and applied to understanding these algorithms and structures. The impact of modern computer hardware, especially caches and parallelism on the design and performance of data structures and algorithms will be considered. Prerequisite: CSC 241; MAS 111 or MAS 161. 3 credits.

CSC 285 Computational Problem-Solving I

Students will sharpen their skill at applying computational problem-solving techniques (particularly the design of data structures and algorithms) in the context of competitive programming. Graded pass/fail. Prerequisite: CSC 120, or by permission of the instructor. 1 credit.

CSC 351 Software Architecture

An examination of the role of architecture and design in the construction of large and complex software systems. After evaluating foundational principles upon which architectural systems are based, students will examine several different case students, and do projects where they implement one or more architectural systems. Prerequisite: CSC 242 and MAS 112. 3 credits.

CSC 362 Distributed Systems

This course examines the design of modern, distributed software applications. Client/Server models (from RPC designs to modern REST-based architectures) will be the focus, though other distribution models will be considered. Scalability, security, and other fundamental issues will be addressed. Students will design and implement a service and a mobile front end to that service as a semester-long team project. Prerequisite: CSC 242 and MAS 112. 3 credits.

CSC 499 Professional Experience

This course tracks the completion of Professional Experiences by students in the Computer and Data Science major. Prerequisite: MAS 112, CDS 242, and junior standing. 0 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 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 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.

2 additional language labs (CSC 18X or CSC 385 Computational Problem Solving II

Students will sharpen their skill at applying computational problem-solving techniques (particularly the design of data structures and algorithms) in the context of competitive programming. This course considers more advanced data structures and algorithmic techniques than CSC 285. Graded pass/fail. Prerequisite: CSC 242 or CSC 285; or by permission of the instructor. 1 credit.

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Take 3 courses from the following:
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.

Any CSC course at the 300 level or above, not already taken.

or

Take the following to complete the Data Science concentration:
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 372 Statistical Modeling