B.S. in Data Analytics

Courses

DA 203 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 228 Data Analytics in 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 220 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 225 Multivariate and Advanced Statistics
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 230 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 231 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 240 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 256 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 255 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 220.

Credits: 3
Every Fall and Spring

DA 262 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 262.

Credits: 3
Every Fall and Spring

DA 263 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 263.

Credits: 3
Every Fall and Spring

DA 266 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
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 299 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 220, 230, 240 and 250.

Credits: 3
Every Fall and Spring