Course Descriptions for
Data Science

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Computer Science Related

CSCI 100, INTRODUCTION TO COMPUTING, 3 credits
An introduction to the fundamental concepts of computing. Designed to develop the student's understanding of how the computer works, its capabilities, limitations, and applications. Topics include types of computers, the central processing unit, data representation and storage, operating systems, applications software, and networks. To give life to these concepts, students will be introduced in laboratory sessions to specific applications software for word processing and data storage and manipulation. Prerequisite: None.

CSCI 110, INTRODUCTION TO COMPUTER SCIENCE I, 4 credits.
Three hours lecture and 2 hours lab. This course provides an introduction to the discipline of computing, emphasizing problem solving techniques, algorithm development, and software design concepts and their realization as computer programs. Topics will include control structures, iteration, recursion, data types, and procedural abstraction and their implementation in a high-level language. Prerequisite: Calculus eligibility. Co-requisite: Math 130. Students should enroll concurrently in CSCI 110-L section.

CSCI 110-L, INTRODUCTION TO COMPUTER SCIENCE I LABORATORY "Not for credit"
Closely coordinated experiences in a closed, supervised laboratory to accompany CSCI 110, in which the student should enroll concurrently.

CSCI 120, INTRODUCTION TO COMPUTER SCIENCE II, 4 credits
Three hours lecture and 2 hours lab. Software design techniques needed for solving larger problems are introduced, including abstract data types, requirements and specifications, complexity analysis, and file organizations. The course includes an introduction to basic data structures (stacks, queues, trees, and graphs) and transformations (searching and sorting). The entire problem-solving procedure from design to debugging and validation is described. Prerequisite: Students should enroll concurrently in CSCI 120-L section.

CSCI 120-L, INTRODUCTION TO COMPUTER SCIENCE II LABORATORY, "Not for credit."
Closely coordinated experiences in a closed, supervised laboratory to accompany CSCI 120, in which the student should enroll concurrently.

CSCI 201-202, SOPHOMORE SEMINAR, 0.5 credit/semester
The seminar course will provide opportunities to enhance student learning and exposure via invited speakers, discussion groups, demonstrations, laboratory assistance, and outside investigation. To be taken each semester.

CSCI 241, DATA STRUCTURES AND ALGORITHMS. 4 credits
This course continues the study of data structures and the design and analysis of algorithms. It will include an introduction to algorithm design techniques, including greedy algorithms and divide and conquer. Prerequisites: CSCI 120.

CSCI 280, MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS, 4 credits

Mathematical Modeling of Biological Systems is an interdisciplinary course at the interface of mathematics, biology and computer science. In this course students will utilize mathematical theory while working on addressing biological problems, thus coming to understand the interplay between mathematical theory and practice. The topics will include developing mathematical models with the goal of building upon intuitive understanding using mathematical approaches. Students will use computer technology to merge data from their lab bench experiments with mathematical models to determine how various changes impacted an overall organism and its functions.

Prerequisite: Math 120-Calculus I or Math 121 Biomathematics.

CSCI 287P, INTRODUCTION TO PYTHON, 1 credit

Python is one of the most popular programming languages used in bioinformatics and data sciences. This course introduces a collection of powerful, open-source, tools needed to analyze data and to conduct data science. The following tools will be introduced in this course: Numpy, Scipy, Matplotlib and Pandas.

CSCI 287R, INTRODUCTION TO R, 1 credit

This course is designed to prepare students from an early stage of their career with research and professional skills needed to succeed in the field of bioinoformatics, biology and data science, and to train them well to utilize the computational tools for applying the computational methods in their research work.

CSCI 287X, INTRODUCTION TO LINUX, 1 credit

Linux is the most widely used operating system, especially in mobile devices and high performance computers. This course introduces Linux/Unix family of operating systems. Commands, utilities, file systems and basic shell scripting.

CSCI 301-302, JUNIOR SEMINAR, 0.5 credit/semester
The seminar course will provide opportunities to enhance student learning and exposure via invited speakers, discussion groups, demonstrations, laboratory assistance, and outside investigations. To be taken each semester.

CSCI 312, DATABASE MANAGEMENT, 4 credits
Principles, tools, and techniques of database design, with emphasis on concepts and structures necessary to design and implement a database management system. The relational, network, and hierarchical models of database design along with relational algebras, data independence, logical and physical views, directory maintenance, and query languages will be studied. Prerequisite: CSCI 241.

CSCI 360, COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS, 4 credits
In-depth study of design principles and protocols for computer and communication networks based on the OSI layered model. Transmission of bits on optical fibers and transmission lines, data link protocols, local area networks, Ethernet, addressing, routing, flow control, TCP/IP networks, and network applications. Prerequisite: CSCI 230 or consent of instructor.

CSCI 370, SOFTWARE ENGINEERING, 4 credits
Introduction to the basic concepts and major issues of software engineering. A continued emphasis on problem solving concepts is integrated with a treatment of the software life cycle, requirements, specification and verification and validation issues. The students working in teams will design, implement, and present a substantial software project. Prerequisite: CSCI 241 and consent of instructor.

CSCI 380, MACHINE LEARNING, 4 credits

This course provides a general introduction to machine learning, datamining, and artificial intelligence (AI). Topics include (but not limited to): (i) Supervised learning (parametric/non-parametric algorithms, linear regression, support vector machines, neural networks, and deep learning). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender). (iii) Best practices in machine learning (data pre-processing, data cleaning, bias/variance theory; innovation process in machine learning and AI). We will review several case studies in order to investigate interdisciplinary real-life projects.”

CSCI 390, SPECIAL TOPICS, 3-4 credits
Lectures on topics of current interest. Topics vary according to the needs and interests of students and faculty. Suggested topics include Artificial Intelligence, Expert Systems and Robotics, Compiler Design, Networks, and Computer Graphics. Prerequisite: Consent of Instructor. May be taken twice, maximum.

CSCI 398, INDEPENDENT STUDY, 1-4 credits
Independent study is available for motivated students who wish to pursue the study of a topic not covered by the normal course offerings in the University. Arrangements for independent study may be made by means of a written proposal, signed and approved by the instructor, the department chair, and the Provost; to be filed with the Registrar at the time of registration. Topics suggested for independent study include, but are not limited to: UNIX system administration, graph theory, performance evaluation, and network application programming.

CSCI 410, SENIOR SEMINAR, 2 credits
Presentation of student research. Prerequisite: Senior standing or consent of Department Chair.

Mathematics Related

MATH 100, INTERMEDIATE ALGEBRA
4 credits, non-degree only. For students with a 2-year algebra sequence from high school or whose score on the mathematics placement test is not satisfactory for CORE 130 or MATH 101. Review of basic concepts with emphasis on sets and the real numbers, equations and inequalities, polynomials and factoring, and radical and rational expressions. The above topics are introduced in their basic setting.

MATH 101, COLLEGE ALGEBRA, 4 credits*
Fundamental concepts of algebra are reviewed, but emphasis is on an introductory study of exponential and logarithmic functions and an advanced study of algebraic equations and inequalities, algebraic functions and their graphs, systems of equations and inequalities, and series. Additional topics will be covered following completion of the topics listed above. The course will make use of technology (calculators and computers) and mathematical modeling for solving both hypothetical and real-life problems. The course is recommended for students needing more algebraic experience before taking advanced courses in their discipline (for example, quantitative courses not requiring trigonometry). Prerequisites: Grade of C or better in MATH 100 or successful completion of a 2-year sequence in high school algebra and a suitable score on a mathematics placement test.

MATH 110, PRECALCULUS, 4 credits*
Emphasizes functions and their graphs, exponential and logarithmic functions, trigonometry, trigonometric functions and applications, systems of equations and inequalities, matrices and determinants. The course will make use of technology and mathematical modeling for problem solving. This is a one semester preparation for calculus, involving all of the elementary functions. Prerequisites: Grade of C or better in MATH 101.

MATH 120, CALCULUS I, 4 credits*
Topics included are limits, derivatives, and integration of algebraic, trigonometric, exponential, and logarithmic functions, with applications. The course will use technology for exploration and problem-solving. Prerequisites: Grade of C or better in MATH 110 .

MATH 123, BIOMATHEMATICS I
Biomathematics I serves as an alternative, equivalent option for Calculus I. Biomathematics I will cover topics from Calculus I with application to modelling, with a focus on the application to biological phenomena, including dynamical systems in biology; limits and infinite sums; differential calculus; optimization; polynomial approximation; computational methods, integration, fundamental theorem of calculus and applications of integration.

MATH 125 (formerly MATH 115), DISCRETE MATHEMATICS, 4 credits*
A calculus based course designed for students interested in computer science. No previous experience is required. The course will provide the mathematical foundations necessary for other computer science courses. Topics covered include introduction to formal logic, techniques of proofs, recursion and recurrence relations, elementary analysis of algorithms, counting principles, relations and functions, graphs and trees, and graph algorithms. Prerequisite: Grade of C or better in MATH 110.

MATH 130, CALCULUS II, 4 credits*
The second of a three semester sequence. Topics included are techniques and applications of integrations, improper integrals, conic sections, polar coordinates, parametric equations, and infinite series. Prerequisite: Grade of C or better in MATH 120.

MATH 210, CALCULUS III, 4 credits. The third course in three semester sequence*
Topics included are vector valued-functions, partial derivatives, double and triple integrals, solid analytic geometry and vectors in space and applications, and topics in vector calculus (line integrals, surface integrals, Green’s Theorem, Divergence Theorem, and Stokes’ Theorem). A computer algebra system will be used for problem-solving and to gain new insight and understanding. Calculus Prerequisite: Grade of C or better in MATH 130.

MATH 220, INTRODUCTION TO HIGHER ALGEBRA, 4 credits*
An introduction to fundamental mathematical techniques used in upper-level mathematics courses and other disciplines. The course presents the principles of mathematical logic and uses them to examine standard methods of direct and indirect proof, including mathematical induction. Topics include techniques from elementary number theory and the naive set theoretic approach to functions and relations. An axiomatic development of some structures is introduced, as well as systems of linear equations and matrices. Prerequisite: Grade of C or better in MATH 120 or permission of the instructor.

MATH 230: BIOMATHMATICS II, 4 credits
The use of mathematical theory in the context of biologically relevant projects, developing mathematical models with the goal of building upon intuitive understanding using mathematical approaches. Using technology and software-enabled analysis, students will learn to merge data from their lab bench experiments with mathematical models to determine how various changes impacted an overall organism and its functions. The students will have hands-on training with a number of computational tools, including deterministic and stochastic modeling, Monte Carlo Simulations, data mining and data analysis, needed in approaching their projects. This course will serve students with career goals in pursuing graduate degree in Biology or in Applied Mathematics.

MATH 235: BIOINFORMATICS, 4 credits
Bioinformatics is an interdisciplinary research area in the interface between the biological and computational sciences. The course familiarizes students with data structure and architectural strategies for organizing complex data sets, and with available computational tools for interrogating these data sets. Students will also be introduced to strategies for exploring not only particular content in already established databases, but also how algorithms are developed to identify patterns of data distribution, and examine possible relationships among data, e.g. networks.

MATH 240, LINEAR ALGEBRA, 4 credits*
Systems of linear equations and matrices, abstract vector spaces and linear transformations, orthogonality, eigenvalues, eigenvectors, and diagonalization. Some attention is given to the development of abstract reasoning and a variety of applications of linear algebra in natural and social science. Prerequisite: Grade of C or better in MATH 130 or concurrent enrollment in MATH 130.

MATH 270, ORDINARY DIFFERENTIAL EQUATIONS, 4 credits*
This course introduces first order differential equations with applications, higher order differential equations with applications, series solutions oflinear equations, Laplace transforms, and systems of linear first order differential equations. Prerequisite: Grade of C or better in MATH 130.

MATH 280, MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS, 4 credits

Prerequisite: Math 120-Calculus I or Math 121 Biomathematics.

MATH 320, ALGEBRAIC STRUCTURES, 4 credits*
A systematic study of groups, rings and fields complete with substructures, homorphisms, endomorphism, isomorphisms and automorphism. Prerequisite: Grade of C or better in MATH 240.

MATH 353, INTRODUCTION TO REAL ANALYSIS, 4 credits*
A rigorous treatment of limits and continuity with an introduction to a topology for the reals, completeness of the reals, differentiability and integrability and sequences, series and the theory of convergence. Prerequisite: Grade of C or better in MATH 210 and Grade of C or better in MATH 240.

MATH 390, SPECIAL TOPICS IN MATHEMATICS, 4 credits
Topics vary, depending on the interest of students and staff. Prerequisite: Permission of the instructor.

MATH 395, SENIOR SEMINAR, 1 credit
Open only to senior mathematics majors. Under the supervision of a full time member of the mathematics faculty, the student will prepare a seminar to be presented orally and in writing.

MATH 398, INDEPENDENT STUDY, 3-4 credits
A student may complete any mathematics requirement by independent study and periodic evaluations by a full-time member of the Mathematics faculty. Prerequisite: Approval of Program Coordinator.

*A grade of C- is not acceptable in the prerequisite course

Statistics Related

NSCI 360, STATISTICS, 4 credits.

An introduction to probability and statistical inference in an interdisciplinary setting. Liit theorems. Sampling, hypothesis-testing, regression. Non-parametric methods. Enough theory is presented to introduce the intellectual foundations of statistical method, but the main emphasis is on applications in the natural sciences. Prerequisite: MATH 130 or equivalent.

HSS 280-01, METHODS AND STATISTICS FOR SOCIAL RESEARCH, 4 credits

An introduction to the principles of research techniques commonly used in the social sciences. The course will include analysis of data, including the construction of tables and graphs and the calculations of descriptive statistics, such as measures of central tendency, variability, and correlation. Students will critically read examples of research reports and research topics, using existing records in the Library and in computer databases. Prerequisite: CORE 131 – Mathematics, MATH 101, or higher.

BAD 260, APPLIED STATISTICS, 4 credits

The course familiarizes the student with the application of descriptive and inferential statistical analysis to business, management, and economic problems. Topics include tabular and graphical methods, measures of location and dispersion, probability, discrete and continuous probability distributions, sampling and sampling distributions, interval estimation, hypothesis testing, simple linear regression, correlation, comparison of two populations, and time series analysis. Prerequisite: Math 110 – Precalculus.

MATH 390-01, BOSTATISTICS, 4 credits

The study of statistics explores the collection, organization, analysis, and interpretation of numerical data. When the focus is on the biological and health sciences, we use the term biostatistics. Biostatistics is the scientific discipline concerned with application of statistical methods to problems in biology. It is an applied discipline with roots lying in mathematics, but its branches touch all areas of biology, extending, for example, from basic cellular physiology to community surveys designed to study the frequency and cause of home accidents. The field of biostatistics has evolved as a medium of statistical expression to parallel the increase in knowledge of biological and mathematical sciences. Mathematics is the fundamental study on which biostatistics rests, and mathematical skills and attitudes are an important part of the training of a biostatistician. Modern biologists need the powerful tools of data analysis hence, a basic data analysis course for all biology and premedical students with strong emphasis on intuitive understanding to convey meaning rather than formulas. Lectures will incorporate interesting examples drawn from medical and biological literature giving students a tool to learn about the human and natural world. Questions based on real biological and medical data will demonstrate how statistics can extract scientific insight from data. There are opportunities for calculation practice to take students through important procedures step-by-step along with a barrage of new jargon and multitudinous statistical tests. Starting from an intuitive foundation is extremely valuable as well as focusing on data rather than mathematical foundations of statistics. This biostatistical treatment does not demand a mathematical competence beyond college algebra.

A computer is essential for most calculations and students will be required to use the R computer package. Modern biology uses a larger toolkit than the one available a generation ago. Time permitting, students will have opportunities to explore more rigorous topics residing in biological and medical literature as extra- credit group projects.

Course Descriptions for Data Science

Course Descriptions for
Data Science