Bachelor of Science in Data Science

Conceptual foundation for the field of Data Science, with emphasis on ethically using Data Science skills and tools in a variety of fields.

Prerequisite: ANA 200

Develop skills to acquire and visualize data to clearly communicate Data Science insights to a variety of project stakeholders.

Prerequisite: MTH 12A and MTH 12B, or

Accuplacer test placement evaluation An introduction to statistics and probability theory. Covers simple probability distributions, conditional probability (Bayes Rule), independence, expected value, binomial distributions, the Central Limit Theorem, hypothesis testing. Assignments may utilize the MiniTab software, or text-accompanying courseware. Calculator with statistical functions is required.

Analysis of the values, ethics and ideologies in computing and their applications to current issues in computer industry within the contemporary sociocultural setting. Focuses on ethical decision-making in computing matters. Students develop an ethical outlook on a wide variety of workplace issues in computing through case study, debate and readings.

Prerequisite: ANA 200 and ANA 230

Students will apply Data Acquisition techniques for different kinds of data, including structured and unstructured data collected from a variety of sources.

Prerequisite: ANA 310

Application of the Data Management and Governance Process for Analytics including: Data Structure, Privacy, Security, and working with Customer-Centered Databases. Evaluation of how these data relate and aggregates in databases, data marts, data warehouses, and data lakes and how they are used by analytical decision tools will be explored through case studies and projects.

Prerequisite: MTH 209A

This continuation of MTH 209A includes concepts of measurement, geometry, probability and statistics, elementary synthetic and Euclidean Geometry. Computer programming in BASIC is introduced. Methods are incorporated whenever possible. However, both MTH 209A and MTH 301 are content/concept courses as prescribed by State regulations, not methods courses. Calculator may be required.

Prerequisite: ANA 320 and MTH 330

Develop skills to clean, transform, and prepare raw data for exploratory statistical Analysis. Transform and merge multiple data sources into a single useable data set for analysis. The progression will then focus on standardizing variable formats, investigating outliers, analyzing missing data, and in general conduct a thorough exploration of the dataset. This process will highlight the limitations, strengths, and potential biases of the dataset and how to reduce these biases.

Prerequisite: MTH 216B, or MTH 215, or Accuplacer test placement

(Cross listed and equivalent to CSC208) An introduction to limits and continuity. Examines differentiation and integration concepts with applications to related rates, curve sketching, engineering optimization problems and business applications. Students may not receive credit for both MTH220 and CSC208.

Prerequisite: ANA 330

Apply Data Mining Methods to reduce data dimensionality and build predictive models for linear regression and classification trees. Hands on work on practical data mining problems will be part of the course curriculum.

Prerequisite: ANA 340

The process of data modeling and optimization will be continued with association analysis, cluster analysis, and other unsupervised learning methods. Hands-on work on practical data mining problems will be part of the course curriculum.

Prerequisite: MTH 215, or MTH 216A and MTH 216B

(Cross listed and equivalent to CSC331) This course studies combinatory and graph theory as the theoretical foundation for today’s advanced technology. It analyzes algorithms, logic, circuits, number bases, and proofs. Ample applications (graphs, counting problems, Turing Machines, codes) examine the ideas of Euler, Boole, Floyd, Warshall, Dijkstra, Church and Turing, Shannon, Bernoulli. Graphing calculator is required. Students may not receive credit for both MTH325 and CSC331.

Prerequisite: MTH 220 and MTH 325

An examination of systems of linear equations and matrices, elementary vector-space concepts and geometric interpretations. Discusses finite dimensional vector spaces, linear functions and their matrix representations, determinants, similarity of matrices, inner product, rank, eigenvalues and eigenvectors, canonical form and Gram-Schmidt process. Computer software will demonstrate computational techniques with larger matrices. Graphing calculator or appropriate software may be required.

Prerequisite: ANA 350

Develop the skills to acquire, organize, and manage data with open-source Python tools including Jupyter notebooks, Panda, and NumPy.

Prerequisite: ANA 420

Develop Python skills to create high-quality visualizations and deploy interactive dashboards to effectively communicate data, methods, analysis, and results to maximize value for stakeholders of a Data Science project.

Prerequisite: Prior completion of all Major Prep, Major, and Concentration classes in BS Data Science program are to be completed before registering for this course.; ANA 485, or CYB 456, or BIO 471

Initiation of the Data Science team project to encompass all parts of the Data Science Life
Cycle. Team building, team collaboration, and conflict resolution are implemented in the proposal of a Data Science project. Technical aspects of Data Acquisition, Data Management, Data Preparation, Data Mining, Data Modeling, and visualization are proposed in a presentation to project advisors and stakeholders.

Prerequisite: ANA 499A

Continuation of Data Science Team project. Data Acquisition, Data Cleaning, and Analytic Methodology are implemented and presented to project advisors and stakeholders in a written project report.

Prerequisite: ANA 499B

Completion of Data Science Team project. Technical aspects of Data Analysis, Data Mining, Data Modeling, and Data Visualization are implemented and presented to project advisors and stakeholders in a written project report.

Prerequisite: CSC 252, or CSC 272

Covers the key concepts and methodologies required for object-oriented design, evaluation and development with focus on practical techniques such as use-case, and scenario based analysis. Coverage of Unified Modeling Language (UML) and domain analysis design. Exposure to software development process models and software management and security.

Prerequisite: CSC 300; CSC 331

An overview of common data structures such as lists, stacks, queues, trees, and graphs. A discussion of various implementations, efficiency and applications of data structures. Course examines efficient storage structures such as Hash tables and Binary Search Tree. Coverage of searching, sorting and graph algorithms along with their implementation and efficiency analysis.

Prerequisite: CSC 335

This course presents an introduction to algorithm design strategies and their application in solving some commonly encountered problems in computing. Topics include asymptotic behavior of algorithms, algorithm designs such as brute force and exhaustive search, divide-and-conquer, dynamic programming, greedy techniques, backtracking as well as branch and bound approach. A discussion of Intractability and NP–complete problems. The course includes an introduction to the theory of parallel and distributed computing.

Prerequisite: CSC 300

A survey of principles, structure, analysis, and techniques of database design and implementation. Topics include physical and logical design, normalization, database models, security, integrity and queries.

Prerequisite: CSC 335

An introduction to problem solving using modern artificial intelligence techniques. The course explores the latest challenges in the theory, practice, applications and implications of AI in the modern world with a focus on data science and machine learning. Examines the role of heuristics in problem solving. Concepts such as agents, production systems, and natural language communication are studied.

Prerequisite: ANA 430

Develop Python Skills to create Machine Learning models for supervised and unsupervised learning in a variety of Data Science applications.

Prerequisite: ANA 480

Develop Python Skills to create models for deep learning and neural networking.

Introduction to the scientific method and the basic principles of the life sciences. Examination of cellular, organismal, population, and community biology based on the unifying concept of evolution. This course may not be taken for credit if BIO161 and/or BIO162, or their equivalents, have been completed.

Recommended Preparation: MTH 204, or MTH 215, or MTH 216A and MTH 216B

Fundamentals of inorganic and organic chemistry, including bonding and basic types of reactions. An introduction to nuclear, biological and environmental chemistry. Basic principles and calculations of chemistry with emphasis in the areas of atomic structure, molecular structure and properties, equilibrium, thermodynamics, oxidation-reduction and kinetics.

Prerequisite: BIO 100 and CHE 101, or BIO 162 and CHE 142

Principles of genetics and heredity. Topics include linkage and pedigree analysis, DNA replication and repair, gene expression and regulation, inheritance of traits, genetic engineering, relationship of genetics to human health, and application of genetics to understanding the evolution of species.

Prerequisite: BIO 305

A survey of Molecular Biology focused on gene structure, organization, regulation and expression. Topics in Genetic Engineering and Genome Evolution are covered, as well as DNA replication, recombination, transcription and post- transcriptional mechanisms in both Eukaryotic and Prokaryotic cells.

Corequisite: BIO 470A; Prerequisite: BIO 161 with a minimum grade of C-. Student must have passed the class with a C- or better; BIO 162 with a minimum grade of C-. Student must have passed the class with a C- or better; BIO 163 with a minimum grade of C-. Student must have passed the class with a C- or better

Analysis of biotechnology-related information using software tools to store, manipulate, and extract information from protein and nucleic acid sequence data. Topics include genome annotation, gene and protein prediction, sequence alignment, and analysis of aligned sequences in the description of patterns of protein or species relationships and gene expression.

Corequisite: BIO 470

Techniques essential to bioinformatics. Topics include practical knowledge of databases, basic commands in Unix and R, sequence alignment and annotation, and gene-expression quantification.

Corequisite: BIO 471A; Prerequisite: BIO 470

Advanced analysis of Biotechnology-related information using programming tools to store, manipulate, and extract information from protein and nucleic acid sequence data. Topics include: Genome Annotation, Gene and Protein prediction, Sequence Alignment, and Analysis of Aligned Sequences in the description of patterns of Protein or Species relationships and Gene Expression.

Corequisite: BIO 471; Prerequisite: BIO 470A

Advanced techniques are essential to Bioinformatics. Topics include: practical knowledge of databases, libraries in Python and/ or R, verifying and evaluating analyses, developing a research project, and communicating results to Biologists.

This course provides an introduction to basic network concepts including local area networks, wireless networks, and wide area networks. Network security concepts are also introduced. Students will explore secure router configurations.

An introductory survey course that explores the fundamental concepts of cybersecurity. Coverage includes the concepts of confidentiality, integrity, and availability, cybersecurity policy, and the ethical and legal aspects of cybersecurity.

Prerequisite: CYB 202; CYB 204

This course introduces the fundamental concepts of cloud computing and virtualization. The core cloud deployment and service models will be covered. A comparison of public and private cloud deployments will be conducted. The concepts of devops and continuous integration will be introduced.

Prerequisite: CYB 340

An examination of the tools and methods for incident response. Topics include preparation data collection, incident analysis preserving data, and recovery. The legal and ethical aspects of incident response will also be covered.

A detailed examination on the concepts of network defense and the various tools to protect and monitor a network. Students will learn how to implement an Intrusion Detection System, conduct network monitoring traffic analysis, and honeypots. Development of associated policy will also be covered.

Prerequisite: CYB 453

A detailed examination of the collection and analysis of Computer and Network Log Data to detect cyber-attacks. Students will utilize a Security and Information Event Management (SIEM) tool to analyze various data. This course will focus on using a SIEM like Splunk or the ELK stack.

Prerequisite: CYB 455

A survey of modern Data Analytics tools and techniques to analyze and solve cybersecurity problems. Students will apply Machine Learning Techniques for log analysis and to solve a cybersecurity problem.