| Title | Winter | Spring | Summer | Fall |
| Data Management |
Designing Relational Databases (3.00 Units)
I&C SCI X426.81
If you need to learn more about the design of relational database systems, this course is for you. You'll explore how the relational database approach is used in both open and closed systems and on both mainframe and client/server platforms, and learn about the design of systems of all sizes including standalone, workgroup, departmental, and enterprise-wide applications. You'll also learn how to incorporate data from legacy systems as well as how to develop entirely new systems. You'll have the opportunity to design a rational database model in class and refine it for a real-world system or case study. Topics include an overview of database systems, defining business entities, entity relationship modeling (ERM) for top-down analysis, defining relational database tables and attributes, conversion of logical design to physical design, normalization of tables, and using structured query language (SQL) to process data and generate reports. Prerequisites: Coursework or experience in programming using a high-level programming language.
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Data Integration, Modeling, and ETL (2.50 Units)
I&C SCI X425.32
This course provides both introductory and advanced concepts and techniques for developing effective dimensional models, data integration, and the ETL process. Learn how to build a high performance dimensional data model. A good dimensional model and its physical database form the hub of a business intelligence data warehouse, serving as the target of the data integration and as the source of business intelligence data. Learn how to design dimensional models for extensibility, employ a proven dimensional design process, apply the process to several representative situations, and understand a variety of advanced dimensional modeling techniques. The Extract, Transform, and Load (ETL) process is typically the most time-consuming, misunderstood, and underestimated task in building a data warehouse and other data integration applications. The ETL process addresses and resolves the challenges of extracting data from disparate operational source systems, storing it in the data staging area, profiling data for errors, cleaning and transforming the data, and mass loading it into the target enterprise data warehouse, data marts, or operational systems. Prerequisite: Experience using a relational database (as a minimum, Microsoft Access or similar product) is highly desirable.
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Online
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to be scheduled
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Data Management (2.50 Units)
I&C SCI X425.35
Data may be an organization's most underrated asset. Organizations often invest a very small percentage of their budget on maintaining the health of their data, which may over time lead to decreasing competitiveness, loss of market share, and even violations of regulatory requirements. This course presents data management from an organizational perspective with the goal of using data management methodologies as a foundation for enterprise data architecture. The course concentrates on data definition and specification, data quality, data organization, data integration, and data equilibrium.
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| Data Analysis |
Business Intelligence & The Data warehouse Development Process (2.50 Units)
I&C SCI X427.01
Learn how to make better business decisions, use fewer resources, and improve your company's bottom line by developing and using a data warehouse. This course provides an overview of business intelligence and data warehousing and gives you a look at all the major facets of developing and using a data warehouse to make effective business decisions. Students will work on a single project to develop a comprehensive project plan and business case for a data warehouse including how to develop a dimensional model, a data staging process, and a data access process. Additional topics will include information on careers working with business intelligence and data warehousing as well as the educational requirements for this field.
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to be scheduled
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Online
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R Basics (2.50 Units)
I&C SCI X427.19
This course will focus on foundational concepts of getting started with R programming which is used for math and statistics and data analysis. The programmatic interface and graphic capabilities of R will also be explored. Several case studies will be covered and examined using R. Prerequisites: I&C SCI X425.99 Practical Math & Stats for Data Science and basic experience with programming.
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Remote
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Big Data Analysis (2.50 Units)
I&C SCI X427.07
Big data is one of the most important technology trends to fundamentally impact the way organizations operate and compete. As more and more companies collect large amounts of data through their daily operations, the ability to analyze and glean knowledge from big data has become an integral part of a successful business. This course will help students navigate through the complex layers of Big Data while providing insight on ways to effectively use technologies and architectures to create and manage big data workflows. Concepts covered include an introduction to Big Data and related technologies, discussion of Big Data Processing Architectures, explanation of major concepts behind Big Data Management, and how all of those topics are applied in Big Data Analysis. Students will gain an understanding of the characteristics of big data and techniques for working on big data platforms through hands-on exercises in the tools and systems used by data scientists and data engineers including Hadoop (HiveQL & PIG), Apache Spark, and SparkSQL.
Required prerequisites: I&C SCI X427.05 Fundamentals of Data Science.
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to be scheduled
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Online
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Data Engineering (2.50 Units)
I&C SCI X427.06
This course is designed to enhance student proficiency in data design, data management, data warehouse, data modeling, and query manipulation skills. Topics include techniques and methods for identification, extraction, and preparation of data for processing with database software. Gain an overview of the basic techniques of data engineering, including data normalization, data engineering, relational and non-relational databases, SQL and NoSQL, manipulation of data at scale (big data), algorithms for data operations. Students will work on a final project to explore, analyze, summarize and present findings in a real-world big data set.
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Remote
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to be scheduled
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| Microsoft SQL Server Database |
Introduction to SQL Programming (2.50 Units)
I&C SCI X450.72
Install SQL Server to your desktop and run complex queries using the Structured Query Language (SQL). This course will cover database table structures, column data types, and the T-SQL language components that go into querying against a database. Reading data from a table, row filtering, and column based functions will be discussed. We will also write queries that combine data from multiple tables, use conditional logic, and create aggregated result sets (sum, average, min, max). Database administration features of SQL will also be discussed. Prerequisites: Familiarity with data processing concepts and techniques.
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Online
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to be scheduled
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to be scheduled
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Online
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Intermediate SQL Programming (2.50 Units)
I&C SCI X450.79
Expand your SQL toolkit in this Intermediate SQL course. SQL is a language used in programming and designed for managing data held in a relational database management system (RDBMS). This intermediate course will focus on using MS SQL Server and T-SQL. Topics covered in this course include: Data aggregation using aggregate functions, writing sub-queries, recursive queries, and common table expressions. Students will also gain experience creating views, stored procedures, functions, and triggers using different looping and database locking mechanisms. Specific areas in the SQL Server database using data file structures, database schemas, setting properties, snapshots, data space and type usage and identity columns will be covered. SQL tuning and use of execution plans will also be addressed. Prerequisites: I&C SCI X450.72 Introduction to SQL Programming.
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to be scheduled
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to be scheduled
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to be scheduled
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| Cloud DB Courses |
Cloud Computing Essentials (2.50 Units)
I&C SCI X460.51
The primary objective of this course is to provide the techniques and practices of cloud computing. Cloud computing refers to a set of services that provide companies and application developers with the means to scale their application's resource usage through the Internet on demand. This course includes a survey of technologies deployed by Amazon, Google, and Microsoft. This course will explore how cloud computing services can provide on-demand access to data storage, computing resources, and messaging. In addition, this course will explore the current challenges facing cloud computing, mainly focusing on cloud computing models, techniques, and architectures. This course will provide students with the advanced level of knowledge and hand-on experience in designing and implementing a cloud-based software system.
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to be scheduled
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