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2021 - 2022 Academic Catalog

Applied Mathematics

The Lasell University Bachelor of Science in Applied Mathematics degree program provides students with the opportunity to develop the conceptual understanding, computational and technology skills, and drive required to use quantitative reasoning and analysis effectively in their careers and personal lives. The primary goal of this program is to facilitate the development of problem-solving skills which transcend the confines of the field of mathematics. The multi-disciplinary approach to this program is built upon a strong foundation in the combined disciples of mathematics, data science, and physics.  In addition to this, students are presented with meaningful educational experiences based on the knowledge perspectives of the Lasell University core curriculum: creativity and aesthetics, scientific inquiry and problem solving, individuals and society, and global and historical perspectives.

The connected learning philosophy of the college is emphasized by a semester off-campus field experience that will provide professional interaction and training in a student's chosen area of career focus. 

Academic standards for the Applied Mathematics program include grades of "C" or better in all MATH, DSCI, & PHYS courses.


Goals and Outcomes
The following goals and learning outcomes delineate what we strive for students to achieve when they complete the major program of study in Applied Mathematics:

·       Develop the logical thinking and analytical skills needed for mathematical problem solving.

·       Develop the quantitative reasoning and analysis skills needed for mathematical modeling.

·       Apply mathematical analysis and problem-solving skills in a range of applications in biological, physical, social sciences, and in public or private services.

·       Develop real world skills in computer technology, software, and algorithmic processes necessary in quantitative analysis and mathematical modeling.

Communicate effectively and to function well in multidisciplinary projects.

Course Code Course Title Credits
Concentration Courses
DSCI102 Introduction to Computer Science 3
DSCI306 Advanced Python Programming 3
DSCI309 Biostatistics 3
MATH205 Calculus I 4
MATH206 Calculus II 4
MATH208 Statistics 3
MATH212 Finite Mathematics 3
MATH215 Discrete Math 3
MATH305X Advanced Statistics 3
MATH307 Calculus III 4
MATH325 Linear Algebra 3
MATH399 Capstone Seminar 3
MATH499 Internship 3
PHYS111 General Physics I (KP) 4
PHYS112 General Physics II (KP) 4

Major Requirements: 50 credits

Core Curriculum:  24 credits

(Mathematics, Quantitative Reasoning, and Scientific KP met through major requirements) 

 

Unrestricted Electives: 46 credits

 

Total Program Credits: 120

A minimum of 120 credits is required for graduation. This total includes the Core Curriculum Requirements as described elsewhere in this catalog. Some courses required for the major meet Core Curriculum requirements. 
For a complete explanation of graduation requirements, see Graduation Requirements in the Undergraduate Academic Policies section of this catalog.

DSC151 - CSC II Programming for Everyone I

This course, built in collaboration with Google, provides a gentle, but thorough, introduction to programming using Python. You will learn the core concepts and techniques needed to create programs and perform basic data analysis. By the end of this course, you’ll be ready to pursue further study in computer science and unlock more advanced programming courses. This online class has optional live sessions. Prerequ. DSCI150

DSCI102 - Introduction to Computer Science

This introduction to computer science, emphasizes problem solving and data analysis skills along with computer programming skills. Using Python, students learn design, implementation, testing, and analysis of algorithms and programs. And within the context of programming, they will learn to formulate problems, think creatively about solutions, and express those solutions clearly and accurately. Problems will be chosen from real-world examples such as graphics, image processing, cryptography, data analysis, astronomy, video games, and environmental simulation. Students get instruction from a world-class computer science professor, delivered remotely through video and interactive media and attend class for collaborative team projects to solve real-life problems. Prior programming experience is not a requirement for this course. Formerly: INTC102

DSCI103 - Fundamentals of Information Technology

This course provides students with the fundamental skills and concepts required to maintain, support, and work efficiently with personal computers. It will assist students in preparing for the Digital Transformation. The course is organized around the five important uses of technology in business – IT concepts, Infrastructure, Applications and Software Development, Database fundamentals, and Security and Cloud Computing

DSCI105 - Data Warehouse and Business Intelligence

This course begins with the introduction of a data warehouse. Students will learn the concepts, tools and application of data warehouse for business reporting and online analytical processing. Students will also learn how to create visualizations and dashboards, and descriptive analytics. The material builds from the concepts learned in basic statistics courses. Core tools used in this course include Microsoft Excel, and SAS Visual Analytics. Excel will be used to teach the basics of visualizations – like bar charts, line charts etc. in order to ramp-up the students’ expertise into SAS Visual Analytics. SAS Visual Analytics will be used as a tool to introduce students to data warehousing, and building basic visualizations. Students will also be exposed to Facts and Dimensions.

DSCI151 - CSC II Programming for Everyone I

This course, built in collaboration with Google, provides a gentle, but thorough, introduction to programming using Python. You will learn the core concepts and techniques needed to create programs and perform basic data analysis. By the end of this course, you’ll be ready to pursue further study in computer science and unlock more advanced programming courses. This online class has optional live sessions.

DSCI152 - BCS I Intro to Blockchain Technologies

Blockchain and Cryptocurrency have become two words that are on everyone’s lips in recent years, but what are they? This course is your gateway to the world of decentralized networks: the world of the blockchain. You’ll learn how a blockchain works, what it does and why people care about both it and cryptocurrency. You’ll even learn a bit of programming and how to set up your own node and get on the blockchain yourself. This online class has optional live sessions.

DSCI200 - Intro to Cybersecurity

The Internet has changed dramatically; so have the activities that are dependent on it in some shape or form. Understanding the need for security, it’s influence on people, businesses and society, as well as business drivers is critical. The course also covers malicious attacks, threats and vulnerabilities common to the world of security, as well as access controls, and methods to assess and respond to risks. Hands-on labs accompany the various concepts that are taught.

DSCI201 - Analytics using SAS Visual Analytics

This course focuses on building and enhancing skills from the Data Warehousing and Business Intelligence course. Students will expand their concepts of Business Intelligence, Visualizations, Dashboards, and Descriptive Analytics. The core tool used in this course is SAS Visual Analytics. Students will create visualizations, dashboards, and export reports to be able to present to the class. Prerequisite: DSCI105.

DSCI202 - Business Analytics

This course provides the conceptual and technical foundations of various aspects of Data Analytics. The purpose is to prepare students with foundation skills in Big Data, a skill widely needed and valued across the business world. The course will expose students to the data analytics practices executed in the business world and explores key areas of the analytical process, how data is created, stored, accessed, and how organizations work with data and creates the environment in which analytics can flourish. This course will provide students with a strong foundation in all the areas that support analytics and will help them to better position themselves for success within any organization. This course provides the conceptual and technical foundations of various aspects of Big Data Analytics, including cloud computing, NoSQL Databases, predictive and prescriptive analytics. Prerequisite: MATH208 or MATH209.

DSCI203 - OS + Algorithms

An introduction to the theory and structure of modern operating systems, including hardware abstraction, process management, memory management, system performance, and security. Specific attention to multi-threaded processing, semaphores, locking and inter-process communication. Prerequisites: DSCI102 and DSCI103 (formerly INTC102/INTC103).

DSCI204 - How to Think Like a Data Scientist

This course introduces students to the importance of gathering, cleaning, normalizing, visualizing and analyzing data to drive informed decision-making, no matter the field of study. Students will learn to use a combination of tools and techniques, including spreadsheets, SQL and Python to work on real-world data sets using a combination of procedural and basic machine learning algorithms. They will also learn to ask good, exploratory questions and develop metrics to come up with a well-thought-out analysis. Presenting and discussing an analysis of data sets chosen by the students will be an important part of the course. Prerequisites: DSCI102 and DSIC103 (formerly INTC102/103).

DSCI205 - Data Communication & Networks

This course introduces students to the fundamental concepts of computer networks and data communication, including a survey of major protocols, standards, and architectures. Students will use the concepts and terminology of data communications in describing how software applications and network services communicate with one another. Students will read and analyze network traces to monitor communications, diagnose issues, and evaluate protocols. Prerequisites: DSCI102 and DSCI103 (formerly INTC102/103).

DSCI207 - Cryptology

A course that covers fundamental mathematical concepts from modern algebra, number theory, and other areas of mathematics. Provides a foundation for the understanding of classical encryption systems and modern encryption methods. Emphasis on the mathematical underpinnings germane to cryptology. Prepares students for advanced study of modern cryptography. Experience implementing encryption, decryption and crypt-analytic methods on a variety of systems. Prerequisites: DSCI102, MATH208 and MATH209.

DSCI210 - Information Systems

This course provides a conceptual survey of general systems theory followed by a conceptual and technological survey of the structure of distributed information systems architectures, operating systems, network operating systems, peripheral technology and user interfaces. Interoperability between these architectural components will be explored and current technology and trends in each architectural element will be reviewed. This course will de-emphasize, although not ignore, mainframe architectures in favor ofinformation architectures more applicable to client/server computing. The various interacting categories of client/server computing as well as the benefits and implications of such a system will be fully explored.Prerequisite: DSCI200

DSCI301 - Big Data Analytics

This course provides the conceptual and technical foundations of various aspects of Big Data Analytics. The purpose is to help students acquire foundation skills in Big Data – which can be used to further their specialization in a niche within Big Data. Upon completion of the course students should be able to understand: What Big Data, Cloud Computing and NoSQL Databases are; Various components and architecture of Big Data Analytics; Different types of Analytics including Text, Descriptive, Predictive and Prescriptive; and how Big Data Analytics is used in different contexts. Students should also be able to use Analytics and Dashboards to present actionable Insights. This course will use SAS Visual Analytics as one of the tools for illustrating the volume of Big Data, and how it can be used to harness actionable insights. Students will use datasets to create visualizations and actionable insights. Prerequisites: DSCI102, DSCI105 and DSCI201.

DSCI302 - IT Security & Risk Management

This course focuses on the concepts, terminology and practice of network security. Topics include the fundamental goals of network security and practical applications of wired and wireless network security techniques such as applications of cryptology in network protocols, authentication, access control, network security devices such as firewalls and intrusion detection and prevention systems, incident response, log analysis, honeypots and honeynets. Prerequisites: DSCI102 and DSCI103.

DSCI303 - Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. The course covers issues both theoretical and practical. Students will be presented with algorithms and approaches in such a way that can ground them in larger systems as they learn about a variety of topics, including statistical supervised and unsupervised learning methods, randomized search algorithms and reinforcement learning. Prerequisites: DSCI102, DSCI103 and DSCI204.

DSCI304 - Marketing Analytics

The course provides the conceptual and technical foundations of various marketing metrics and research methods. The purpose of the course is to allow students to acquire practical marketing skills in Data Analysis via hands-on experience. Prerequisites: BUSS220 and DSCI202.

DSCI305 - Information Assurance and Management

This course focuses on management of the information assurance process. Topics include human factors in reducing security breaches, security incident detection and response, remediation, management's role in information assurance, and other considerations in framing and implementing information assurance policies. Prerequisites: DSCI102 and DSCI103.

DSCI306 - Advanced Python Programming

This course provides students with the opportunity to write useful Python applications in the ETL, web, and data analysis domains and knowledge of industry-standard tools and techniques for working within a development team. The course goes further into Python’s powerful advanced features, such as user-defined classes, object-oriented design, decorators, and generators. Students will learn to employ the most widely used algorithms and libraries to solve common problems in the field and gain a working familiarity with statistical analysis and visualization using Pandas, NumPy, and Matplotlib. Query and parse HTML, XML, and JSON are used. Students will learn to apply industry-standard tools and techniques for working within a development team, such as Git for versioning and code review. The course concludes with a discussion of common interview questions and pathways for gaining experience and eventually securing a position in the field. Prerequisites: DSCI102, DSCI202 and DSCI204.

DSCI307 - Analytics Elec w/SAS

Analytics Elec w/SAS

DSCI308 - Predictive & Prescriptive Analytics

In this course, students will be introduced to the fundamentals of the art and science of Predictive Analytics as it relates to improving business performance. This hands-on course covers the key concepts necessary to extract stored data elements, understand what they mean from a business point of view, transform their formats, and derive new relationships among them to produce a dataset suitable for analytical modeling. At the end of the course, participants will be tasked with using these skills to produce a fully processed data set compatible for building powerful predictive models that can be deployed to increase profitability. Prerequisite: DSCI303.

DSCI309 - Biostatistics

This course introduces students to research method techniques and common statistical applications of importance to healthcare managers. Emphasis is placed on the study of statistical techniques for problem-solving and decision-making including the theoretical and applied statistical and quantitative skills required to understand, conduct and evaluate managerial research. Students will learn to distinguish between types of research (quantitative and qualitative) with an emphasis on the use of quantitative analysis in healthcare organizations. Basic research methods are described, including surveys, observational studies, experimental and quasi-experimental design; and the use of primary and secondary data sets. Statistical techniques for analyzing and interpreting data will include descriptive statistics, hypothesis testing, probability, sampling, t-tests, ANOVA, chi-square analysis, correlation, and linear regression.Prerequisite: MATH208 or MATH209

DSCI310 - Cyberlaw & Cybercrime

This course includes extensive discussion of the legal constraints, both civil and criminal, that underlie acceptable behavior using computers and networks today. Prerequisites: BUSS205 & DSCI103

DSCI311 - CSC V Application Development I

Modern development relies on frameworks which provide developers with powerful tools to speed up development. If you want to build apps, you need to understand how to use frameworks. This course, which has been built in collaboration with Google, will introduce you to Django - a framework used for data-driven web applications. You’ll learn the fundamentals of Django, improve your database management skills, and begin developing your own apps. This online class has optional live sessions.

DSCI402 - Analytics with R

This course introduces students to R, a widely used statistical programming language. Students will learn to manipulate data objects, produce graphics, analyze data using common statistical methods, and generate reproducible statistical reports. They will also gain experience in applying these acquired skills in various public policy areas.Prerequisites: DSCI102, DSCI202 & DSCI204

DSCI403 - Advanced Predictive Analytics

Acquire in-depth knowledge on advanced predictive analytics topics and apply those to real-world situations. These scenarios illustrate the significant role that predictive analytics plays. You pay particular attention to developing your ability to effectively interpret the outcomes of statistical models. You also focus on time series data analysis and survival analysis using the SAS system. Prerequisite: DSCI308

DSCI405 - Computer Forensics

This course provides student with the opportunity to perform basic forensic techniques and use appropriate media analysis software. Basics of security, structure and protocols of network operating systems and devices are covered as students will work to gather evidence in a networked environment and to image and restore evidence properly without destroying value. Students will practice gaining evidence from a computer system while maintaining its integrity and a solid chain of custody. Within the laboratory, students will gain hands-on experience in the use of current investigative tools. Prerequisites: DSCI205 & DSCI310

DSCI409 - Project & Program Management

This course allows students to develop the competencies and skills for planning and controlling projects and understanding interpersonal issues that drive successful project outcomes. Focusing on the introduction of new products and processes, students will examine the project management life cycle, define project parameters, matrix management challenges, effective project management tools and techniques, and take on the role of a project manager. This course is designed to guide students through the fundamental project management tools and behavioral skills necessary to successfully launch, lead, and realize benefits from projects in both for-profit and non-profit organizations. Prerequisites: Senior Standing and internship.

DSCI410 - CSM V Product Development

Creating software products is more than just writing code, it also requires an analysis of what your customers want, and how to meet their needs. As a result, understanding product development is key to a successful career in technology. By the end of this course (built in collaboration with Google), you will understand how product teams and processes work, and learn how to develop an idea into an actual product that delights your users. This online class has optional live sessions.

DSCI499 - Internship Data Science

This is a hand-on experience in a data science work or research setting that offers students an opportunity to apply concepts, theories, and practices learned in the classroom in a supervised setting. Students must successfully complete a minimum of 150 hours of field experience in addition to course assignments. Prerequisite: Permission of Program Chair. Requirement for Cybersecurity and Data Analytics Majors

DSCI701 - Ethical,Soc & Cult Implications of Data

An introduction to the ethical and social consequences of collecting, curating, and analyzing data in academia, public and private contexts. A socio-technical stance is taken in unpacking issues of algorithmic biases, fairness, transparency, and accountability. Additionally, students develop a strong understanding of responsibilities and issues associated with the culture of the sports science field. Students will develop an understanding of the importance of effectively and accurately communicate data in sports environments. Students will practice building effective work environments and develop innovative training principles through the use of key sport science concepts.

DSCI703 - Applied Cloud Comput for Data Inten Sci

This course covers data science concepts, techniques, and tools to support big data analytics, including cloud computing, SQL, parallel algorithms, nonrelational databases, and high-level language support. The course applies the MapReduce programming model and virtual-machine utility computing environments to data-driven discovery and scalable data processing for scientific applications.

DSCI704 - Data Analytics

This course covers the concepts of information technology used in manipulating, storing, and analyzing big data. Students gain an understanding of the tools used for statistical analysis, R, Python, and several machine learning algorithms for application in an industry setting. Emphasis is on designing, implementing, and developing machine learning algorithms. Focus is placed on interpretation and visualization of results.

DSCI705 - Visualization Design, Analysis, and Eval

This is an introductory course in design and evaluation of interactive visualizations for data analysis. Topics include human visual perception, visualization design, interaction techniques, and evaluation methods. Students develop projects to create their own web-based visualizations and develop competence to undertake independent research in visualization and visual analytics. Pre-requisite: Data Analytics

MATH106 - Mathematical Reasoning

This course is the foundational course for mathematical and quantitative reasoning at Lasell College. Mathematical reasoning is the critical skill that enables a student to solve real-world problems involving quantitative analysis by making use of particular mathematical skills. Through the development of their mathematical reasoning skills, students will recognize the power of mathematics in its own right as well as its relevance in the real world. Students will develop and enhance their mathematical reasoning skills through a project/application-based curriculum supported by readily available current technological tools and topics that will include, but not be limited to, the following: solving systems of equations, linear programming, statistical, and graphical data analysis.

MATH107 - College Geometry

This course is an introduction to the essentials of Euclidean geometry. Topics covered include: reasoning in mathematics, the relationship between algebra and geometry, analytic geometry, proofs and constructive triangles, circles, quadrilaterals, polygons, surfaces and solids and historical notes about famous geometricians. Prerequisite: MATH 106 with a grade of C or better or demonstrated competency through placement testing.

MATH108X - Mathematics of Design

This course explores elements of mathematics within the design field from the incorporation of algebra to concepts of geometry. Students will have the opportunity to integrate numerical fluency, proportional reasoning, data interpretation, algebraic reasoning and communicating quantitative information through group problem solving and class discussions. Topics include pattern drafting, layouts cutting, revenue, cost, and profit modeling, measurement systems, Euclidean geometry, and spatial reasoning.

MATH110X - Introduction to Logic

An introduction to symbolic logic, including sentential and predicate logic. Its purpose is to familiarize you with certain formal methods for representing and evaluating arguments and reasoning. These methods can be used for any subject matter. The focus is on translating English statements into symbolic notation, and evaluating arguments for validity using formal proof techniques.This course is recommended for data science students, math majors, students who are contemplating graduate school admissions tests, and for general knowledge and application (so, for instance, all computer programming is based on fundamental logic rules and applications). s

MATH116 - Merchandising and Financial Mathematics

This course focuses on retail mathematics. Topics include simple and compound interest, the time-value of capital, annuities, amortization, sinking funds, bond and investment, business problem-solving and decision making. Other topics include profit, loss, and break-even analysis, pricing, inventory, and merchandise planning. The course introduces basic theories of statistics. Prerequisite: MATH 106 with a grade of C or better or through placement testing.

MATH202 - Applied Mathematics for Business

This course will be a “Choose Option across Management, Marketing, Entrepreneurship, Event Management, Hospitality Management, Accounting and Resort and Casino Management Majors. This course will introduce a variety of mathematical principles and techniques that emphasize applications in business and economics. Topics covered include: systems of linear equations, exponential and logarithmic functions, linear programming, as well as the development and applications of rates of change. Prerequisite: MATH106

MATH203 - Precalculus

This course prepares students for the study of calculus, physics and other courses requiring precalculus skills. Included is solving systems of equations, the analysis and graphing of linear, quadratic, polynomial, exponential, logarithmic, rational functions, the unit circle, and triangle (right and non-right) trigonometry. Prerequisite: MATH 106 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed 205, 206, or any 300 level mathematics course successfully.

MATH205 - Calculus I

This course is an introduction to limits, continuity, and methods of differentiation. Application to problems in business management and physical science is emphasized. Prerequisite: MATH 203 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed MATH 206, or any 300 level mathematics courses.

MATH206 - Calculus II

This is a continuation of Calculus I. Includes graphical and analytic integration, partial differentiation, and solving differential equations. Applications include business, biological sciences, and physical sciences. Prerequisite: MATH 205 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed MATH 320, MATH 328, or MATH 330.

MATH207 - Applied Trigonometry

This course is an in-depth study of trigonometry with attention to theory, proofs, modeling, and history. Trigonometric and related functions are used to model, analyze, and solve real-life problems. Applications are chosen from disciplines such as agriculture, architecture, astronomy, biology, business, chemistry, earth science, engineering, medicine, meteorology, and physics. Topics covered include a review of trigonometric functions, right triangle trigonometry, analytic trigonometry, vectors and dot products, complex number theory, trigonometric forms of complex numbers, exponential, logarithmic and trigonometric models, Gaussian and logistic growth models, conic sections, and polar equations of conics. Prerequisite: MATH 205 with a grade of C or better.

MATH208 - Statistics

This is an introductory course in descriptive and inferential statistics. Topics include: data analysis, and graphical methods of describing data, measures of central tendency and variability, probability, the normal distribution, sampling distributions, confidence intervals, hypothesis testing, correlation, and regression analysis. Prerequisites: MATH 106 with a grade of C or better or demonstrated competency through placement testing and ENG 102.

MATH209 - Business Statistics

This is an introductory course in descriptive and inferential statistics focused on applications in business. Topics include: data analysis, and graphical methods of describing data, measures of central tendency and variability, time-series analysis, trend and seasonality analysis, simple and multiple correlation and regression analysis, sales and cost forecasting, probability, expected monetary value, and the Normal distribution. Prerequisites: MATH 106 with a grade of C or better or demonstrated competency through placement testing and ENG 102. With permission of the instructor only.

MATH212 - Finite Mathematics

The focus of this course is to develop mathematical models and to demonstrate the utility of various mathematical techniques that are most applicable to the creation of computer algorithms. Topics include functions and models, linear regression, solving systems of linear equations using matrices, matrix algebra and Leontief Input-Output models, linear programming (graphical and simplex methods), principle of duality, estimated and theoretical probability and Markov Chains. Prerequisite: MATH205 with a grade of C or better.

MATH215 - Discrete Math

Topics will include elementary logic and set theory, equivalence relations, functions, counting arguments, inductively defined sets, recursion, graphs and trees, Boolean algebra and combinatorial circuits, and countability arguments. Prerequisite: MATH203 with a C or better.

MATH303X - Problem Solving

This course will be an exploration into the mathematics exemplified in high quality high school and undergraduate mathematics competitions and mathematical research. The emphasis will be placed on building a repertoire of mathematical strategies and tactics, then applying these methods in unfamiliar situations. Topics will include: Combinatorics, Binomial Theorem, Conditional Probability, Roots of Unity, Symmetric Polynomials, Polynomial Interpolation, and topics in Euclidean and non-Euclidean Geometry. Students will hone their ability to solve mathematical problems through hands-on practice and obtain an understanding of the strategies, tactics, and tools of the problem solver as illustrated by the textbook and the instructor. Strategies and tools for solving problems include, but are not limited to: •Draw a Diagram•Systematic Lists•Eliminate Possibilities•Matrix Logic•Look for a Pattern•Guess and Check•Sub Problems•Unit Analysis•Solve An Easier Related Problem•Physical Representations•Work Backwards•Venn Diagrams•Finite Differences

MATH304 - Mathematics for Educators

This course engages students in mathematical concepts through examples, investigations, and active problem-solving explorations. Content is drawn from subject matter knowledge required for elementary and early childhood licensure, with emphasis on number theory and operations. This course is for students seeking elementary or early childhood licensure.

MATH305 - Advanced Statistics

Quantitative statistical tools for modern data analysis are used across a range of disciplines and industries to guide organizational, societal and scientific advances. Using data sets from across a variety of fields, the focus will be on applications and analysis. Topics include two sample confidence intervals, Chi Square tests, multiple regression analysis, ANOVA, non- parametric tests, sampling, and simulation. Prerequisite: Math 208 or Math 209

MATH306X - Mathematical Content Knowledge for Ed

This course engages students in hands-on, in-depth, practical applications of the mathematical reasoning and computational techniques taught in MATH 304. This course is for students seeking elementary or early childhood licensure. Prerequisite: Permission of Education Program Director

MATH307 - Calculus III

This course is an introduction to sequences and series, parametric and polar curves, vector functions, advanced techniques of differentiation and integration. Prerequisite: MATH 206 with a grade of C or better.

MATH320 - Differential Equations

This is an introduction to the many ways of solving various types of differential equations with emphasis on theory, methods of solution and applications. Topics include solutions of first, second and simple higher order differential equations, homogeneous and non-homogeneous equations. Prerequisite: MATH 206 with a grade of B- or better.

MATH322X - Special Topics in Mathematics

Special Topics in Mathematics

MATH325 - Linear Algebra

This is an introductory course in linear algebra blending the requirements of theory, problem solving, analytical thinking, computational techniques, and applications. Topics include in-depth treatment of matrix algebra, linear systems, vector spaces, linear transformations, determinants. Applications and modeling of real phenomena in transportation systems, economics, connectivity of networks, and graph theory. Prerequisite: MATH 206 with a grade of C or better.

MATH338 - Mathematical Statistics

In this introduction to statistical theory, the roles probability and statistics play in business analysis and decision making are investigated. Topics include probability distributions, statistical inference, sampling distribution theory, and applications. Prerequisite: Math 206 with a grade of C or better.

MATH399 - Capstone Seminar

In this capstone course, Students investigate mathematics from a variety of fields and choose a topic for a mathematics project in their Field of Application. Mathematical methods for analysis, modeling, prediction, and/or problem solving are discussed. Students demonstrate knowledge of a substantial area of mathematics and present their work at a department seminar or the Connected Learning Symposium.

MATH499 - Internship

The internship seminar is a work or research experience where students combine theory and practice.

MATH706X - Mathematical Content Knowledge for Ed

This course engages students in hands-on, in-depth, practical applications of the mathematical reasoning and computational techniques for teachers. This course is for students seeking elementary or moderate disabilities licensure.

PHYS105 - Introduction to Astronomy (KP)

Introduction to astronomy for the non-science major with a focus on our place within the universe. Topics include the formation and evolution of stars and planetary systems, our Solar System, the Milky Way Galaxy, and the large scale structure of the universe

PHYS106 - How Things Work (KP)

This course explores how things from our everyday lives work according to the rules of nature. The principles that influence how objects fall, cars move, scales weigh, planes fly, stoves heat, copiers copy give insight into the workings of the universe. Connections between our immediate surroundings and the universe at large are illustrated.

PHYS107 - Modern Science & Technology (KP)

This course introduces the history of Science from antiquity to the present and demonstrates how the various areas of science work together to develop the technology and the materials we are familiar with in our daily lives.  Topics include role of measurement and experiments and revolutions of modern science (advances in chemistry, biology, astronomy and technology).Students will conduct inquiry-based projects focusing on areas of interest. The goal of this course is to help students develop the practices of science such as asking researchable questions, planning and carrying out investigations, analyzing data and other related skills that will enhance their quality of life and professional success.

PHYS108 - Science of Sport (KP)

This course will look at how certain basic principles of science govern the major operations of many different sports. Students will conduct inquiry-based projects focusing on areas of interest. The goal of this course is to help students develop the practices of science such as asking researchable questions, planning and carrying out investigations, analyzing data and other related skills that will enhance their quality of life and professional success.

PHYS110 - Physical Geology (KP)

This course focuses on teaching the principles of geology and earth history, leading to a fundamental understanding of earth systems and processes. Students will also engage in a semester-long scientific writing project focusing on a National Park of choice that inspires them.

PHYS111 - General Physics I (KP)

This is the first semester of a one-year course that surveys the field of physics at a non-calcu­lus level. Topics include motion in one and two dimensions, force, uniform circular motion, work and energy, and statics of rigid bodies. The laws of thermodynamics are introduced. Laboratory experiments are conducted to complement the material covered in lecture. Prerequisite: MATH 203 or equivalent with a grade of C or better. Corequisite: PHYS111L, PHYS111R.

PHYS112 - General Physics II (KP)

This is a continuation of PHYS111. Topics include waves motion, electric potential, electric current, resistance, capacitance, and magnetism. Geometrical and wave optics are introduced. Atomic and quantum theory are also included. Laboratory experiments are conducted to com­plement the material covered in lecture. Prerequisite: PHYS 111 with a C or better. Corequisite: PHYS112L, PHYS112R.

Cristina Haverty

Dean, School of Health Sciences; Associate Professor of Athletic Training

Office: Science and Technology Center

Kimberly Farah

Professor of Chemistry

Office: STC 306

Neil Hatem

Professor of Mathematics

Office: Science and Technology Center

Joanna Kosakowski

Professor Emerita

Ron Laham

Assistant Professor of Exercise Science

Office: Science and Technology Center

Stephen Sarikas

Professor of Biology

Office: Science and Technology Center

DSCI102 - Introduction to Computer Science

This introduction to computer science, emphasizes problem solving and data analysis skills along with computer programming skills. Using Python, students learn design, implementation, testing, and analysis of algorithms and programs. And within the context of programming, they will learn to formulate problems, think creatively about solutions, and express those solutions clearly and accurately. Problems will be chosen from real-world examples such as graphics, image processing, cryptography, data analysis, astronomy, video games, and environmental simulation. Students get instruction from a world-class computer science professor, delivered remotely through video and interactive media and attend class for collaborative team projects to solve real-life problems. Prior programming experience is not a requirement for this course. Formerly: INTC102

DSCI306 - Advanced Python Programming

This course provides students with the opportunity to write useful Python applications in the ETL, web, and data analysis domains and knowledge of industry-standard tools and techniques for working within a development team. The course goes further into Python’s powerful advanced features, such as user-defined classes, object-oriented design, decorators, and generators. Students will learn to employ the most widely used algorithms and libraries to solve common problems in the field and gain a working familiarity with statistical analysis and visualization using Pandas, NumPy, and Matplotlib. Query and parse HTML, XML, and JSON are used. Students will learn to apply industry-standard tools and techniques for working within a development team, such as Git for versioning and code review. The course concludes with a discussion of common interview questions and pathways for gaining experience and eventually securing a position in the field. Prerequisites: DSCI102, DSCI202 and DSCI204.

DSCI309 - Biostatistics

This course introduces students to research method techniques and common statistical applications of importance to healthcare managers. Emphasis is placed on the study of statistical techniques for problem-solving and decision-making including the theoretical and applied statistical and quantitative skills required to understand, conduct and evaluate managerial research. Students will learn to distinguish between types of research (quantitative and qualitative) with an emphasis on the use of quantitative analysis in healthcare organizations. Basic research methods are described, including surveys, observational studies, experimental and quasi-experimental design; and the use of primary and secondary data sets. Statistical techniques for analyzing and interpreting data will include descriptive statistics, hypothesis testing, probability, sampling, t-tests, ANOVA, chi-square analysis, correlation, and linear regression.Prerequisite: MATH208 or MATH209

MATH205 - Calculus I

This course is an introduction to limits, continuity, and methods of differentiation. Application to problems in business management and physical science is emphasized. Prerequisite: MATH 203 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed MATH 206, or any 300 level mathematics courses.

MATH206 - Calculus II

This is a continuation of Calculus I. Includes graphical and analytic integration, partial differentiation, and solving differential equations. Applications include business, biological sciences, and physical sciences. Prerequisite: MATH 205 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed MATH 320, MATH 328, or MATH 330.

MATH208 - Statistics

This is an introductory course in descriptive and inferential statistics. Topics include: data analysis, and graphical methods of describing data, measures of central tendency and variability, probability, the normal distribution, sampling distributions, confidence intervals, hypothesis testing, correlation, and regression analysis. Prerequisites: MATH 106 with a grade of C or better or demonstrated competency through placement testing and ENG 102.

MATH212 - Finite Mathematics

The focus of this course is to develop mathematical models and to demonstrate the utility of various mathematical techniques that are most applicable to the creation of computer algorithms. Topics include functions and models, linear regression, solving systems of linear equations using matrices, matrix algebra and Leontief Input-Output models, linear programming (graphical and simplex methods), principle of duality, estimated and theoretical probability and Markov Chains. Prerequisite: MATH205 with a grade of C or better.

MATH215 - Discrete Math

Topics will include elementary logic and set theory, equivalence relations, functions, counting arguments, inductively defined sets, recursion, graphs and trees, Boolean algebra and combinatorial circuits, and countability arguments. Prerequisite: MATH203 with a C or better.

MATH305X - Advanced Statistics

Quantitative statistical tools for modern data analysis are used across a range of disciplines and industries to guide organizational, societal and scientific advances. Using data sets from across a variety of fields, the focus will be on applications and analysis. Topics include two sample confidence intervals, Chi Square tests, multiple regression analysis, ANOVA, non- parametric tests, sampling, and simulation. Prerequisite: Math 208 or Math 209

MATH307 - Calculus III

This course is an introduction to sequences and series, parametric and polar curves, vector functions, advanced techniques of differentiation and integration. Prerequisite: MATH 206 with a grade of C or better.

MATH325 - Linear Algebra

This is an introductory course in linear algebra blending the requirements of theory, problem solving, analytical thinking, computational techniques, and applications. Topics include in-depth treatment of matrix algebra, linear systems, vector spaces, linear transformations, determinants. Applications and modeling of real phenomena in transportation systems, economics, connectivity of networks, and graph theory. Prerequisite: MATH 206 with a grade of C or better.

MATH399 - Capstone Seminar

In this capstone course, Students investigate mathematics from a variety of fields and choose a topic for a mathematics project in their Field of Application. Mathematical methods for analysis, modeling, prediction, and/or problem solving are discussed. Students demonstrate knowledge of a substantial area of mathematics and present their work at a department seminar or the Connected Learning Symposium.

MATH499 - Internship

The internship seminar is a work or research experience where students combine theory and practice.

PHYS111 - General Physics I (KP)

This is the first semester of a one-year course that surveys the field of physics at a non-calcu­lus level. Topics include motion in one and two dimensions, force, uniform circular motion, work and energy, and statics of rigid bodies. The laws of thermodynamics are introduced. Laboratory experiments are conducted to complement the material covered in lecture. Prerequisite: MATH 203 or equivalent with a grade of C or better. Corequisite: PHYS111L, PHYS111R.

PHYS112 - General Physics II (KP)

This is a continuation of PHYS111. Topics include waves motion, electric potential, electric current, resistance, capacitance, and magnetism. Geometrical and wave optics are introduced. Atomic and quantum theory are also included. Laboratory experiments are conducted to com­plement the material covered in lecture. Prerequisite: PHYS 111 with a C or better. Corequisite: PHYS112L, PHYS112R.