Position yourself to meet the soaring demand for data scientists and data analysts.
With the use of smartphones, email, loyalty cards, social networking and search engines, big data is everywhere and growing. Companies need people like you to manage and analyze information to make smart business decisions.
Our STEM-designated analytics track provides the comprehensive array of skills you need to succeed as an analytics professional working across the data life cycle.
Are you ready to meet the world's big data challenges? The analytics track will help you:
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This course is designed to provide students with a basic understanding of database management systems (DBMS) and the skills needed to design and implement a relational database. Students will be introduced to data modeling concepts, modeling tools, the process of transforming conceptual models into relational database designs, and finally the steps needed to implement those designs. Emphasis is placed on Entity-Relationship diagramming, data normalization, database administration, and data definition, data manipulation and query development using Structured Query Language (SQL). Other topics covered include: object-oriented databases, database security and integrity, web/database integration, application development in a Client/Server environment, distributed databases, data warehousing, data mining and knowledge management via the Internet to support electronic commerce. Readings, lectures, interactive case assignments and a database design project reinforce the role of DBMS in supporting organizational systems, transaction processing and decision support applications.
Semesters offered: Fall 2020
This course covers the fundamental concepts in statistics that are essential for business and data analytics. Probability Theory and Sampling Theory are the two foundations of both descriptive and predictive forms of analytics. Building from these foundations, students are introduced to the statistical concepts of data analysis. Topics covered include: descriptive statistics, probability theory, discrete and continuous probability distributions, sampling theory, estimation, hypothesis testing, distribution fitting using chi-square tests, simple and multiple linear regression, introduction to causal modeling and predictive data analytics. MS-Excel based data modeling will be used extensively throughout the exposition of the concepts.
Semesters offered: Fall 2020
* MGQ 606 is required for students without a business background. Student may instead enroll in MGS 647 (supervised research with an MSS faculty), if they have a business background.
Total credits: 6
If you don't have a business background, you may apply to the program, but will be required to take MGG 503, Introduction to Business.