This course introduces the student to fundamentals of database analysis, design and implementation. Emphasis is on practical aspects of business process analysis and the accompanying database design and development. Topics covered include: conceptual design of databases using the entity relationship model, relational design and normalization, SQL and PL/SQL, web based database design and implementation using Oracle or some other modern Database Management Systems. Students may be required to work with an organization in understanding their business requirements, developing a detailed set of requirements to support business processes and designing and implementing a web based database application to support their day-to-day business operations and decision making. Students will acquire hands-on-experience with a state-of-the-art database management system such as Oracle or Microsoft SQLServer and web-based development tools. 

Units: 3
Textbook: Carlos Coronel and Steven Morris, "Database Systems: Design, Implementation and Management"

Corporations today are said to be data rich but information poor. For example, retailers can easily process and capture millions of transactions every day. In addition, the widespread proliferation of economic activity on the Internet leaves behind a rich trail of micro-level data on consumers, their purchases, retailers and their offerings, auction bidding, music sharing, so on and so forth. Data mining techniques can help companies discover knowledge and acquire business intelligence from these massive datasets. This course will cover data mining for business intelligence. Data mining refers to extracting or “mining” knowledge from large amounts of data. It consists of several techniques that aim at discovering rich and interesting patterns that can bring value or “business intelligence” to organizations. Examples of such patterns include fraud detection, consumer behavior and credit approval. The course will cover the most important data mining techniques—classification, clustering, association rule mining, visualization, prediction. 

Units: 3
Textbook: None.

The objective of this course is to introduce you to Business Intelligence (BI) techniques and understand how to use them to gain insights into emerging social media technologies and deal with "Big Data." You will be exposed to managerial, strategic and technical issues associated with developing and deploying Business Intelligence and Web Analytics Solutions. You will learn how to gather strategic decision making requirements from businesses, develop key performance indicators (KPIs) and corporate performance management metrics, understand how external sources of data (using Web 2.0 technologies and social media sites) can be harnessed to design and implement business dashboards. Topics covered include the principles of dimensional modeling, techniques for extraction of data (ETL) from source systems, data profiling, data transformation methods, data staging and quality, data warehouse implementation, online search engine optimization, online advertising, web metrics, and social media analytics. You will also learn how to apply analytical techniques to sift through large amounts of data and provide actionable business insights. Using tools such as Google Analytics you will learn to measure, redesign and improve websites to attract visitors. The course will delve into different metrics used to evaluate effectiveness of websites. You will also learn to collect and analyze data from social media sites such as Facebook, Twitter, YouTube, Flickr and Foursquare so that these sites can be used effectively by businesses. Specifically you will learn to build, visualize and analyze graphs and networks using different kinds of datasets to understand how to harness social media effectively for business use. 

Units: 3
Textbook: Ralph Kimball and Margy Ross, "The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling,"