B.S. in Data Science

Courses

EC-101. Macroeconomic Principles. 3 Credits 

Definition of economics and its methodology. Scarcity and the resulting macroeconomic problems. Measurement and determination of the level of macroeconomic activity (size and components of GNP, full employment, growth); stabilization problems (unemployment and inflation) and policies. Course Type(s): Core curriculum course. 

EC-102. Microeconomic Principles. 3 Credits 

Scarcity and the resulting microeconomic problems. Demand and supply analysis and applications. Production and cost functions. Market structures, industry and firm conduct and performance. Resource markets. Prerequisites: EC-101. 

EC-300. Statistics for Business Finance and Economics. 3 Credits 

Introduction to the use of statistics in describing and solving economic and business problems. Frequency distributions, measures of central tendency and dispersion. Basic probability theory and acceptance sampling. Confidence interval estimation and hypothesis testing. Simple regression and correlation analysis. Prerequisites: EC-101 EC-102, MA-105(9413) OR MA-123, MA-106(9414) OR MA-124. 

DS-325. Data Law Ethics and Business Intelligence. 3 Credits 

The increasing use of big data in our society raises legal and ethical questions. Business intelligence is the process of collecting and transforming raw data into meaningful and useful information for business purposes. This course explores the issues of privacy, data protection, non-discrimination, equality of opportunities and due process in the context of data-rich environments. It analyzes ethical and intellectual property issues related to data analytics and the use of business intelligence. Students will also learn the legal obligations in collecting, sharing and using data, as well as the impact of algorithmic profiling, industrial personalization and government. This course also provides an understanding of the important capabilities of business intelligence, the technologies that enable them and the management of business intelligence. Prerequisites: DS-300, DS-310. 

BA-155. Principles of Marketing. 3 Credits

Business activities involved in the flow of goods and services from production to consumption. 

BA-151. Principles of Management. 3 Credits 

An analysis of the management process. Introductory course in management. 

FN-401. Corporate Finance. 3 Credits

A study of the problems associated with the financial management of business organizations. Topics include the analysis of types of firms and markets, review of accounting, time value of money, valuation, and short-term financing. 

CS-260. Information Technology Ethics. 3 Credits

This course addresses the assessment of ethical principles within the application of information technologies to produce and store data and disseminate and use information. It will define and discuss computer ethics within a historical, current and future perspective by dealing with ethical issues in the workplace, privacy and anonymity, property rights, professional responsibility and globalization from the viewpoint of the individual, business and government. 

CS-241. Python Programming for Computer Scientists. 3 Credits

Python programming and coding practice. Object-oriented concepts. Python use in Artificial Intelligence applications. Prerequisites: CS/IS-180 OR CS-190. 

CS-190. Secure Software Development. 3 Credits

This is a programming course required for Cyber Security students. Students will learn how to write, test, and debug programs using secure programming techniques. They will learn how to identify key characteristics and design patterns for secure coding, and develop programs in a secure environment using the software development life cycle. Students demonstrate their knowledge through hands-on programs, exercises and case study assignments. 

CS-271. Decision Support Systems. 3 Credits

Concepts of Decision Support Systems: Decision Support System technologies, operations research, systems analysis, decision analysis, DBMS, artificial intelligence. Decision Support System tools: data mining, data management, EXCEL. In-depth analysis of business applications, including ERP Systems, data warehouse systems and electronic commerce. Students will be required to complete a final project on designing a computer-based decision support system. Prerequisites: CS-177 OR CS-180(12188) OR CS-190 OR BA-151 OR BA-155. 

CS-337. Statistical Computing with R. 3 Credits

In this course students explore the fundamental principles of statistical computing in R. Learners will engage in topics such as the fundamentals of R, data types, matrices, data frames, control structures, input/output, libraries (e.g. ggplot2), visualizations, statistical inference, and simulations. Prerequisites: MA-212. 

IS-380. Database and Data Administration. 3 Credits. 

This course teaches students how database systems are used and managed, and the issues associated with protecting associated data assets. In addition, it will teach the methods to protect the confidentiality, integrity, and availability of data throughout the data life cycle. Topics include: relational databases, no-SQL databases, object based vs. object oriented, big data, Hadoop / MongoDB / HBASE, data policies/quality/ ownership/warehousing, long term archival, data validation, data security (access control, encryption), database vulnerabilities, database topics/issues (indexing, inference, aggregation, polyinstantiation), hashing and encryption, database access controls (DAC, MAC, RBAC, Clark-Wilson), information flow between databases/servers and applications, database security models, security issues of inference and aggregation, and common DBMS vulnerabilities. Prerequisites: CS/IS-180 OR CS-190. 

BA-287. Introduction to Business Analytics. 3 Credits 

This course introduces students to some of the tools that businesses use to optimize their activities. Students will among others, learn how to use Excel Solver, POM QM and also conduct spreadsheet sensitivity analysis. 

DS-310. Principles of Data Mining. 3 Credits. 

Data mining refers to a set of techniques that have been designed to efficiently find important information or knowledge in large amounts of data. This course will provide students with understanding of the industry standard data mining methodologies, and with the ability of extracting information from a data set and transforming it into an understandable structure for further use. Topics covered include decision trees, classification, predictive modeling, association analysis, statistical modeling, Bayesian classification, anomaly detection and visualization. The course will be complemented with hands-on experience of using advanced data mining software to solve realistic problems based on real-world data. Prerequisites: CS-190. 

DS-210. Introduction to Data Science. 3 Credits

Data Science is a set of fundamental principles that guide the extraction of valuable information and knowledge from data. This course provides an overview and develops student’s understanding of the data science and analytics landscape in the context of business examples and other emerging fields. It also provides students with an understanding of the most common methods used in data science. Topics covered include introduction to predictive modeling, data visualization, probability distributions, Bayes’ theorem, statistical inference, clustering analysis, decision analytic thinking, data and business strategy, cloud storage and big data analytics Prerequisites: DS-310. 

Electives

IS-410. Total Business Information Systems. 3 Credits

In-depth analysis of business applications including enterprise resource planning and electronic commerce. Basic and advanced applications with emphasis on enterprise database management systems. Prerequisites: CS-231 OR IS-380. 

CS-346. Machine Learning I

Machine learning concepts include neural network and data analysis using deep learning. Classification of images and object detection using industry standard open source machine learning platforms. Programs will be written in Python within a cloud computing environment. Prerequisites: CS-231 OR CS-241. 

CS-470. Introduction to Artificial Intelligence

Knowledge representation, cognitive simulation, machine learning, natural language processing, network technology. Prerequisites: CS-346. 

CY-645. Blockchain Technology 

Students will learn what blockchain is and how it works, from a business as well as technical standpoint. They will gain insight into how blockchain will affect the future of industry / organizations. Upon course completion students will have knowledge of the following: what is blockchain and the real world problems that blockchain can solve; how blockchain works and the underlying technology of transactions, blocks, proof-of-work, and consensus building; how blockchain exists in the public domain (decentralized, distributed) yet maintain transparency, privacy, anonymity, security, and history; recognize how blockchain is incentivized without any central controlling or trusted agency; platforms such as Ethereum to build applications on blockchain; how cryptocurrency works and why people value a ‘digital’ currency; and how to design and implement blockchain for applications in the financial services, manufacturing, and retail industries. 

CS-177. Introduction to Computer Science and Cybersecurity

This course is an introduction to computer science and cybersecurity. The goal of the course is to teach basic principles and at the same time prepare students for a major in computer science/cybersecurity. Topics include: The von Neumann architecture, algorithms, data structures, hardware and software, application systems, programming, cyber security, information technology ethics, and data science. Course Type(s): Core curriculum course, Freshman Seminar. 

CS-330- Data structures and Algorithms using Python

This course explores core data structures and algorithms used in everyday applications, the trade-offs involved with choosing each data structure, along with traversal, retrieval, and update algorithms. It will be covered linked lists, stacks, queues, binary trees, and hash tables.

DS-370 – Big Data Analytics

Big Data (Structured, semi-structured, & unstructured) refers to large datasets that are challenging to store, search, share, visualize, and analyze. Gathering and analyzing these large data sets are quickly becoming a key basis of competition. This course explores several key technologies used in acquiring, organizing, storing, and analyzing big data. Topics covered include Hadoop, unstructured data concepts (key-value), Map Reduce technology, related tools that provide SQL-like access to unstructured data: Pig and Hive, NoSQL storage solutions like HBase, Cassandra, and Oracle NoSQL and analytics for big data. A part of the course is devoted to public Cloud as a resource for big data analytics. The objective of the course is for students to gain the ability to employ the latest tools, technologies and techniques required to analyze, debug, iterate and optimize the analysis to infer actionable insights from Big Data. Prerequisites: DS-310, DS-210. 

DS-340 – SQL Programming

Students will gain experience doing complex relational database queries that will help them in using any relational database system. They will also gain an understanding of ANSI standard objects and programming techniques including writing and calling stored procedures, views, triggers, functions, and using transactions, locks as well as a brief introduction to cursors.

DS-330 – Cloud Computing Eco-Systems

The fundamentals and essentials of Cloud Computing 2. The ability to adopt Cloud Computing tools and services for real life scenarios 3. An exposure to use commercial systems such as Google Apps, Microsoft Azure and Amazon Web Services etc. 4. To impart knowledge in applications of cloud computing.

MA-338. Regression Analysis. 3 Credits

In this course, students explore the applications of regression analysis and techniques of model building. Learners will engage in topics such as simple and multiple linear regression models, correlation, estimation and prediction, confidence intervals, residuals, common pitfalls of regression analysis and possible corrections, transformations, interactions, and model building. Statistical software, such as R, will be used. Prerequisites: MA-132 OR MA-212 OR MA-222 OR MA-336 OR EC-300 OR PERMISSION OF INSTRUCTOR.