B.S. in Data Analytics

Courses

DA 103 Programming in Python
This course provides hands-on-learning in leading-edge computing techniques for data science and programming in Python. Students will not only learn programming fundamentals but also leverage the large number of existing libraries available in Python to accomplish tasks with minimal code. Programming concepts are taught with rich Python examples. The course establishes a solid programming foundation for students to further pursue their data analytics studies.
Credits: 3
Every Fall and Spring

DA 118 Data Analytics using Excel
The course provides students with the opportunity to learn data processing and data analytic skills needed to execute business and professional functionalities in Microsoft Excel. Emphasis is placed on how to efficiently navigate big datasets and use the keyboard to access commands during data processing. The course provides students extensive hands-on experience in learning through practicing with datasets drawn from accounting, finance and other business scenarios. Data visualization skills are also introduced and reinforced throughout the course. At the end of the course students are expected to earn the Microsoft Office Specialist Certification in Excel.
Credits: 3
Every Semester

DA 120 Introduction to Data Analytics with R and Python
This core required course in the Data Analytics program provides a comprehensive introduction to the principles of data science that underlie the data-mining algorithms, data-driven decision-making process, and data-analytic thinking. Topics include learning commands, arithmetic operators, logical operators, and functions in the analytical languages, writing scripts, performing descriptive analytics, creating analytical graphs, and working and manipulating data sets using the two most popular analytic languages of R and Python.
Credits: 3
Every Semester

DA 125 Multivariate and Advanced Studies
This course covers advanced statistical techniques in the context of big data, such as multivariate regression, Bayesian methods, linear discriminant analysis, principal component analysis, factor analysis, and clustering as well as newer techniques, such as density estimation, neural networks, random forests, support vector machines, and classification and regression trees. Students will build a solid statistical foundation in the course for data mining and machine learning
Credits: 3
Every Fall and Spring

DA 130 Database Management with MySQL
This core required course in the Data Analytics program provides a comprehensive introduction to the principles and tools for managing and mining data, covering database management, data retrieval, data pre-processing, data analysis and mining. Students will learn enterprise database management and representative data mining algorithms. By the end of the course, the students will have mastered the essential skills and tools to approach problems data-analytically and mine data to discover knowledge and patterns.
Credits: 3
Every Semester

DA 131 Data Structures and Algorithms
This course provides students a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation. The concept of object-oriented programming is also introduced, including the use of inheritance, so that students can understand similarities and differences of various abstract data types and algorithmic approaches. Topics also include recursion, array-based sequences, stacks, queues, linked lists, trees, maps, hash tables, sorting and selection, text processing, and graphs.
Credits: 3
Every Fall and Spring

DA 140 Data Visualization
This course provides a comprehensive introduction and hands-on experience in basic data visualization, visual analytics, and visual data storytelling and introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making in the field of business analytics.  Modules cover the visual analytics process from beginning to end--from collecting, preparing, and analyzing data to creating data visualizations, dashboards, and stories that share critical business insights.  Students will leverage the analytical capabilities of Tableau, the industry leading visualization tool.
Credits: 3
Every Semester

DA 153 Data Analytics Ethics
This course surveys the domestic and international development of data and information privacy law and regulation in response to the growing sense of urgency around data breach and analytics ethics. The course also addresses the way in which law, legal and regulatory institutions and private sectors govern and control the flow of data and information. Topics also include ethical use of AI, oversight for algorithms, digital profiling, free speech, open government, cybersecurity, data communications. This course is designated as a "writing across the curriculum" (WAC) course offered by the program.  Students will produce substantial written work throughout the course, including case briefs, study reports, and final term paper.
Credits: 3
Every Fall and Spring

DA 155 Intro to Fintech
This course introduces Fintech through a hands-on data analytics approach and fosters students' essential fintech data analytics skills. Topics include Fintech data acquisition, visualization, and analysis, High-frequency trading (HFT) data analytics, implied volatility analytics, Blockchain in Fintech, Smart contract, machine learning in Fintech, and other state-of-the-art fintech knowledge and skills. Prerequisite: DA 120.
Credits: 3
Every Fall and Spring

DA 162 Intro to Artificial Intelligence
The course covers the basic principles of artificial intelligence. Students will learn some basic AI techniques, the problems for which they are applicable, and their limitations. The course content is organized roughly around what are often considered to be three central pillars of AI: Search, Logic, and Learning. Topics covered include basic search, heuristic search, game search, constraint satisfaction, knowledge representation, logic and inference, probabilistic modeling, and machine learning algorithms. Cross listed with AI 162.
Credits: 3
Every Fall and Spring

DA 163 Data Mining and Business Intelligence
The study of advanced PROLOG programming, including advanced topics in knowledge representation and reasoning methods, which include semantic networks, frames non-monotonic reasoning and reasoning under uncertainty. A study is made of concepts and design techniques in application areas, such as natural-language processing, expert systems, and machine learning.  Introduction is made to genetic algorithms and neural networks. Cross-listed with AI 163.
Credits: 3
Every Fall and Spring

DA 166 Computational Genomics
The course offers an introduction to basic theories, history of the field, current research areas and clinical applications of computational genomics including disease diagnosis and risk assessment, genetic counseling, microbiome testing and pharmacogenomics. The impact on personalized medicine and medical products will be highlighted and the course emphasizes the principles underlying the organization of genomes and the methods and approaches of studying them. Methods for understanding concepts such as gene regulation, evolution, complex systems, genetics, and gene phenotype relationships are covered. Topics explored include sequence alignment, comparative genomics, phylogenetics, sequence analysis, structural genomics, population genetics, and metagenomic analysis and Bioinformatics tools as provided in the BioPython library will be utilized.
Credits: 3
Every Fall and Spring

DA 250 Machine Learning and Cloud
This course covers essential component techniques in machine learning and cloud-based big data analytics skills in business via hands-on learning approaches. The machine learning skills, which cover supervised, unsupervised and semi-supervised learning components, are emphasized via using tensorflow, sklearn, Spark Mlib and Amazon machine learning services to solve state-of-the-art massive data problems in business. AWS-based big analytics is covered in a comprehensive, deep, and hands-on ways, and Microsoft Azure and Google cloud technologies are also introduced. This class provides a series of case studies for students to understand machine learning and cloud computing resolutions for big data analytics better. Students are required to use state-of-the-art machine learning and big data analytics tool to solve real-world business problems and present their results.
Credits: 3
Every Semester

DA 260 Deep Learning
This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. Deep learning is behind many recent advances in artificial intelligence, including Siri speech recognition, Face book tag suggestions, and self-driving cars. A range of topics are covered which include basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to various problem domains (e.g., speech recognition, computer vision, handwriting recognition, etc.) Cross-listed with AI 260. Prerequisite: DA 250
Credits: 3
Every Fall and Spring

DA 265 Introduction to Modern Cryptography
The course will offer a thorough introduction to modern cryptography, focusing on models and proofs of security for various basic cryptographic primitives and protocols including key exchange protocols, commitment schemes, digital signature algorithms, oblivious transfer protocols and public-key encryption schemes. Applications to various problems in secure computer and information systems will be briefly discussed including secure multiparty computation, digital content distribution, e-voting systems, digital payment systems, and cryptocurrencies. Cross-listed with AI 265.
Credits: 3
Every Fall and Spring

DA 460 Senior Capstone Project
This core required course in the Data Analytics program trains students on the fundamental concepts needed for the role of a Business Analyst/Business Intelligence Engineer/Data Scientist in companies, and then equips students with the latest available tools to implement these concepts in answering business questions in a data driven way. This course uses hands-on project in business application of data analytics in an area of student interest, such as consumer behavior analytics, pricing analytics, marketing analytics, social media analytics, or other fields. Pre or Co-requisite of DA 120, 130, 140 and 250.
Credits: 3
Every Fall and Spring

ECO 10 Introduction to Microeconomics
This course discusses the important economic theories and concepts that facilitate understanding economic events and issues. Its main focus is on the choices made by consumers, producers, and governments, and there interactions of these choices. Topics include demand and supply, consumption, and production, competitive and non-competitive product markets, markets for resources, and welfare. This course fulfills the Power, Institutions, and Structures thematic cluster requirement in the core curriculum.
Credits: 3
On Occasion

ECO 11 Introduction to Macroeconomics
This course discusses the important economic theory and concepts that facilitate understating economic theories and concepts that facilitate understanding economic events and questions. Its main focus is on analyzing the behavior of important economic aggregates such as national income, unemployment, inflation, interest rates, exchange rates and economic growth.  The effects of the government's monetary and fiscal policies on economic growth and inflation are also examined. This course fulfills the Power, Institutions, and Structures thematic cluster requirement in the core curriculum. Prerequisite of ECO 10 is required.
Credits: 3
Every Fall, Spring and Summer

ENG 1 Writing I: Composition and Analysis
English 1 is an introductory writing course that uses interpretation and analysis of texts to promote clear thinking and effective prose. Students learn the conventions of academic writing. In addition, students learn how to adapt writing for various audiences and rhetorical situations. This course is required Writing I, an introduction to composition, teaches an understanding of writing in various disciplines through the interpretation and analysis of texts. Students will learn conventions of academic writing. Additionally, students will learn how to adapt in response to different rhetorical situations, genres, purposes, audiences, and other issues of context. Writing I is a course that provides the foundation for understanding how to make meaning from texts. This course is required of all students unless exempted by Advanced Placement credit or successful achievement on the SAT examination in writing. Students exempted by assessment or department proficiency examination must take an upper-level English course in substitution after completing ENG 2. Special sections are offered for students in the Program for Academic Success (P sections), for non-native speakers (F sections), and for students identified as needing more personalized attention (S sections). No Pass/Fail option.
Credits: 3
Every Fall, Spring and Summer

ENG 2  Writing II: Research and Argumentation
Writing II, a course in research and argumentation, focuses on scholarly research and the citation of information supporting sustained, rhetorically effective arguments. Building on the work of Writing I, this course addresses sensitivity to complex rhetorical and stylistic choices. Students will learn to use sources and resources effectively and ethically, including library holdings and databases, in service of scholarly arguments grounded in research. This course is required for all students unless exempted by Advanced Placement credit. Special sections are offered for students in the Program for Academic Success (P sections) and for non-native speakers (F sections). No Pass/Fail option. Prerequisite of ENG 1 is required.
Credits: 3
Every Fall, Spring and Summer

FY  First-Year Seminar and Post 101
Provide an emphasis upon the intellectual transition to college, first-year seminars focus on oral communication and critical reading skills taught in the context of theme-oriented academic courses specifically designed to meet the needs of first-year students. The content of these courses varies by discipline, but each course is limited to twenty students and linked in a learning community with a section of Post 101. First-Year Seminars involve intensive faculty mentoring and provide a source of support and insight to students who are encountering the new responsibilities connected to college life. First-Year Seminars can also be used to fulfill major requirements or can be used as electives, including, in many cases, liberal arts electives. Post 101 is best understood a one-credit course preparing first-year students for the challenges of college life. It emphasizes engagement with the campus community as a preparation for engagement with the world as an active, informed citizen. Weekly hour-long class meetings emphasize a holistic approach to learning and introduce students to the behavior, foundational skills, and intellectual aptitudes necessary for success.
Credits: 4
Every Semester

MTH 5 Linear Mathematics for Business and Social Science
Mathematical models for business, linear programming, matrix algebra and applications are covered. Prerequisite of Math 4 or 4S is required. Not open to students who have taken MTH 8, except for Business Administration, Accountancy, or Dual Accountancy Students.
Credits: 3
Every Fall, Spring and Summer

MTH 22 Applied Linear Algebra
This course is an introduction to linear algebra that stresses applications and computational techniques. Topics covered include matrices, systems of linear equations, determinants, vector spaces and linear transformations, eigenvalues and eigenvectors. This course can fulfill an additional requirement the Scientific Inquiry and the Natural World thematic cluster of the core curriculum alongside the laboratory science requirement. Prerequisite of MTH 8 is required.
Credits: 3
Every Spring

MTH 23 Foundations of Statistical Analysis
This course is a thorough introduction to statistics as an applied mathematical science that covers discrete and continuous probability distributions, estimation procedures, hypothesis testing, linear regression and tests of correlation, sampling theory and the design of experiments. Cannot be taken for credit by any student who has completed or is currently taking MTH 19 or MTH 41/BIO 141. Prerequisite of MTH 8 is required. Not open to students who have taken MTH 19, 41 or BIO 141.
Credits: 3
Every Fall

Post 101 and FY  First-Year Seminar
Provide an emphasis upon the intellectual transition to college, first-year seminars focus on oral communication and critical reading skills taught in the context of theme-oriented academic courses specifically designed to meet the needs of first-year students. The content of these courses varies by discipline, but each course is limited to twenty students and linked in a learning community with a section of Post 101. First-Year Seminars involve intensive faculty mentoring and provide a source of support and insight to students who are encountering the new responsibilities connected to college life. First-Year Seminars can also be used to fulfill major requirements or can be used as electives, including, in many cases, liberal arts electives. Post 101 is best understood a one-credit course preparing first-year students for the challenges of college life. It emphasizes engagement with the campus community as a preparation for engagement with the world as an active, informed citizen. Weekly hour-long class meetings emphasize a holistic approach to learning and introduce students to the behavior, foundational skills, and intellectual aptitudes necessary for success.
Credits: 4
Every Semester