Online Undergraduate Certificate in Data Management and Analysis

This course provides a detailed overview of fundamental programming, design, and testing concepts using Python. Students are introduced to the fundamentals of Python scripting and will become proficient in writing modular Python classes. At the core of class method development, students will write Python methods using lists, dictionaries, conditional logic, and looping controls. Recommended Prior Course: ITS310. (This course is also offered through SSA. Credits earned using this option will appear on transcripts with an “S” suffix.) Course not eligible for Prior Learning Assessment (PLA) credit.

This course teaches the important skills of distinguishing types of data and communicating scientific and mathematical information in presentations by visualizing data. Students will learn ways to systematically collect, analyze, and interpret data, then translate technical, data-driven information to a lay audience. Students will examine how data analytics contributes to the formulation of policy decisions and the subsequent communication to stakeholders. Students will recognize how analytics applies to return on investment (ROI) within any career or field. Students prepare presentations using narrative strategies to create compelling, interesting talks based on data.

The course covers the basics of relational databases, including basic terminology and concepts, database integrity, and normalization. The relational model will be examined in detail in order to appreciate database structure, integrity, and manipulation. Current relational database management systems will be explored and contrasted, as will basic relational database design and SQL programming.

A study of data analysis, data production, and statistical inference. Areas of study include: surveys and designed experiments, randomization, causation, regression, and inference using hypothesis tests. This course also explores using statistical methods for the analysis of, data for an enterprise performance and quality, effectiveness, and marketability. Statistical software will be utilized to conduct a predictive analysis, analyze the results, and document the findings. The preparation of input data for analysis from a relational database using SQL is also performed. Course not eligible for Prior Learning Assessment (PLA) credit.

Investigate various statistical approaches used for data mining analyses. The preparation of data suitable for analysis from an enterprise data warehouse using SQL and the documentation of results is also covered. A simple data mining analysis project is performed to reinforce the concepts. (This course is also offered through CBE. Credits earned using this option will appear on transcripts with an “EX” suffix.)

This course provides an overview of the tools and techniques for analyzing data using statistics, R Programming, and SQL. Topics include data storage, linear regression, classification, linear models, tree-based learning, R programming, and SQL basic commands.