rasmussen.edu | 888-5-RASMUSSEN COURSE DESCRIPTIONS QMB 1200C Object-Oriented Programming Using Java 60 hours, 4 credits In this course, students will learn about object- oriented programming (OOP) concepts. Students will implement various OOP concepts in the Java programming language. Topics include structured programming, creation and use of classes, class relationships, and the integration and modification library functions, classes, and interfaces. Prerequisite: QMB 1100C Software Design Using C# QMB 2000C Introduction to Linux in Analytics 60 hours, 4 credits In this course, students will learn how to install the Linux operating system. Students will also learn basic shell commands used in Linux including command-line utilities. Students will be able to implement shell scripts, deploy various software components, and archive and compress files. Prerequisite: None QMB 2100C Data Platforms 60 hours, 4 credits This course introduces students to multiple data platforms. The course will compare the differences in how to perform various data operations on structured and unstructured data. Students will also interpret the results of those operations to solve business problems. Prerequisite: None QMB 2200C Fundamentals of Data Visualization 60 hours, 4 credits This course is an introduction to the concepts and tools used in current visualization methodologies. Students will be able to understand the software and other processes used to produce visualizations. Topics covered will include report design, human perception of visualization, and chart selections rules. Prerequisite: None QMB 2300C Introduction to Data Warehousing 60 hours, 4 credits This course is the study of integrated enterprise data warehouse systems. Topics include migration of relational and unstructured data, analytics platforms and components, and the integration of analytics and business intelligence processes in data warehouses. This course prepares students for future exploration of targeted ecosystems and platforms encountered in advanced analytics and business intelligence courses. Prerequisite: None QMB 2400C Introduction to Analytics Environments 60 hours, 4 credits This course is the study of analytic environments including the platforms, systems, and components used to facilitate the building of analytics environments. Topics include an exploration of the terms used in analytics, analytics tools, and business intelligence and integrated processes used in analytic environments. Prerequisite: None QMB 2500C Open Source Scripting Languages 60 hours, 4 credits This course is an introduction to modern scripting languages used in data analytics processes with an emphasis on open-source scripting languages. The purpose of the course is to prepare students to be able to build scripts that perform the various steps used in data analytics. Prerequisite: None QMB 2600C Discrete Math for Data Analytics 40 hours, 4 credits In this course, students will study sets, logic, counting, probability, number theory, and graph theory. Topics include set theory, truth tables, proofs, induction, natural numbers, basic algorithms, and graphs. The emphasis is on mathematical thinking and reasoning. This course will prepare students to apply abstract thinking in their prospective career fields. Prerequisite: MAC 1106 Advanced Algebra QMB 3000 Introduction to Data Analytics 60 hours, 4 credits This course is an introduction to the concepts and tools used in current analytics practices. Students will be able to identify common tools, terms, and ideas. Topics covered will include visualization, data quality, platforms, and scripting. Prerequisites: Expected to be completed in the first term; COP 1350C C++ Programming; COP 1532C Database Fundamentals for Programmers QMB 3100 Foundations of Analytics Platforms, Environments, and Software 60 hours, 4 credits This course is the study of different types of environment. It places focus on developing and deploying Extract Transform Load (ETL) jobs. It also includes topics related to various types of analytics tools. This course will prepare the student for development ETL jobs in an enterprise environment. The student will also learn about the various analytic tools. Prerequisite: STA 1625 Essential Statistics and Analytics QMB 3200 Introduction to Scripting 60 hours, 4 credits This course serves as an introduction to the scripting process as it relates to data extraction and transformation processes. Prerequisite: None QMB 3300 Introduction to Data Visualization 60 hours, 4 credits This course explores data visualization tools and techniques. It emphasizes the best ways to communicate data to the intended audience. Students learn about tools that aid in visualizing data and how to develop objective depiction of data using an editorial thinking approach. This course will prepare students for the challenges of having to analyze data and communicate results to audiences with various skill levels and preferences. Prerequisite: None QMB 4000 Data Elements 60 hours, 4 credits This course reviews the concepts, standards, and functions used to identify data elements necessary for an efficient data preparation process. Prerequisite: QMB 3200 Introduction to Scripting QMB 4100 Applied Business Intelligence 60 hours, 4 credits This course allows students to apply skills and techniques for analyzing existing business performance data to provide support for business planning. It places focus on planning an end-to-end business intelligence process, platform, database, and analytical tool usage. Students will learn about processing and analyzing data, quality assurance and regulatory adherence, and preparing data for consumption. Students will create visualizations to help guide business decision-making. Prerequisite: CTS 3265C Introduction to Business Intelligence QMB 4200 Advanced Analytics Platforms, Environments, and Software 60 hours, 4 credits This course is for the student of advanced analytics. It places focus on developing and deployed Extract Transform Load (ETL) jobs for large data sets. Topics will include how to configure the environment to run the advanced analytic job. It places focus on real-time analytics as well. This course will prepare students for developing advanced analytics and ETL jobs. It also prepares students about how to deploy the advanced analytics in the enterprise environment. Prerequisite: QMB 3100 Foundations of Analytics Platforms, Environments, and Software QMB 4300 Data Quality in Analytics 60 hours, 4 credits Quality data allows for quality analysis. In this course, students will learn how to identify common types of data quality issues including missing data, incorrect data, outliers, normalization, and duplication. This course will prepare students to prepare data for analytics projects. Prerequisite: None QMB 4400 Data Analysis and Optimization 60 hours, 4 credits This course will allow students to run data extracts and scripts to demonstrate a complete data analysis process, while requiring the identification and application of data element requirements, scripting modifications, and preparation techniques that could improve analysis results. Prerequisites: QMB 4000 Data Elements; QMB 4300 Data Quality in Analytics QMB 4500 Data Visualization Implementation and Communication 60 hours, 4 credits This course focuses on the study of data sets which relate to meeting client needs. It includes methods used to evaluate data such as benchmarking, scoring, and ranking. Students learn the difference between correlation and causation. Students will explore techniques for visualizing both quantitative and qualitative data. This course will prepare students with the skills to derive business insights and make meaningful inferences from data sets. Prerequisite: QMB 3300 Introduction to Data Visualization QMB 4900 Data Analytics Capstone 60 hours, 3 credits This course allows students to demonstrate their skills and techniques for analyzing generalized business data to provide support for business planning. It places focus on planning an end-to-end business analytics process; platform, database, and analytical tool usage; processing and analyzing data; quality assurance and regulatory adherence; preparing data for consumption; and visualization creation to help guide business decision-making. Prerequisite: Expected to be the final upper-level core course completed QMB 5000 Foundations of Data Science 40 hours, 4 credits This course introduces students to the core concepts, processes, and tools of data science, while exploring the basics of common techniques in the data science field. In this course, students will develop the skills needed to apply the early aspects of the life cycle of analytics. Students will review the different types of data sources and explore various data models and algorithms. Students will also use basic tools to complete an analysis and collaborate within teams to evaluate case studies and explore ways in which stakeholder’s needs are met through data science. Prerequisite: Expected to be completed in the student’s first quarter QMB 5100C Data Science Languages 60 hours, 4 credits In this course, students will improve their knowledge of the most current programming languages in data science including relevant data structures, functions, and methods of invoking application programming interfaces (APIs), and techniques that support the construction of large-scale data science applications. Prerequisite: Expected to be completed in the student’s first quarter QMB 5200C Advanced Database Management 60 hours, 4 credits In this course, students will improve their database design skills while obtaining more experience writing complicated SQL queries for relational databases. Students will also gain a strong exposure to technologies and software that work with databases to support higher levels of data integration. In addition, students will be exposed to alternatives to relational databases and understand the advantages and disadvantages of each, most notably, document databases and graph-based databases. Prerequisite: None QMB 5300C Statistical Methods 60 hours, 4 credits In this course, students will learn basic statistical methods through the use of linear model theory and regression. Students will learn how to apply statistical techniques to improve the performance of data analysis systems. Although R or Python programming is necessary to carry out assignments, this course does not offer programming instructions. Students are expected to have basic R or Python skills and to improve upon them throughout the course. Prerequisite: None 126 ALL CONTENT IS SUBJECT TO CHANGE BY ADDENDUM
