Data Analyst and Database Administrator

Data analytics and database management design, develop, and administer data management solutions, as well as develop and implement data administration policies, standards, and models. They are employed in information technology consulting firms and in information technology units throughout the private and public sectors.

Course Outline

SQL Server Administration

This course module provides students with the knowledge and skills required to install, configure, administer, and troubleshoot the client-server database management system of Microsoft SQL Server.

SQL Server Design

This course module provides students with the knowledge and technical skills required to program a database using Microsoft SQL Server. This course is designed for those who are responsible for implementing database objects and programming SQL Server databases using Transact-SQL.

Probability and Statistics

This course module provides an introduction to probability and statistics with applications. Topics include: basic probability models; combinatorics; random variables; discrete and continuous probability distributions; statistical estimation and testing; confidence intervals; and an introduction to linear regression.

SAS for Data Analysis

This course module covers how to plan and write simple SAS programs to solve common data analysis problems and provides practice running and debugging those programs in an interactive SAS session. . It also provides comparisons of manipulation techniques, and resource cost benefits are designed to help programmers choose the most appropriate technique for their data situation.

Data Warehousing

This course module covers the issues involved in planning, designing, building, populating, and maintaining a successful data warehouse. Students learn the reasons why data warehousing is a compelling decision-support solution in today’s business climate.

Data Mining

This course module presents systems and methods for mining varied data and discovering knowledge from data. After detailing a data mining system architecture and tasks, the course examines and compares specific methods in data mining, such as concept learning, decision trees, Bayesian and belief networks, neural networks, case-based reasoning, statistical methods such as cluster analysis and multidimensional analysis, and text and multimedia mining. Several applications are detailed, and tools to build new applications are provided.

Introduction to Business Intelligence

This course module covers the design principles and best practices when planning, implementing, and deploying a business intelligence architecture and solution.