Data Analytics Bachelor's Degree

View courses for our Data Analytics Bachelor's degree. Download the course catalog for more information.

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Course listings are subject to change. Please see our course catalog and/or addendum for most current listings.


Data Analytics Bachelor's Degree Course List

General Education Courses

Lower Division

English Composition (Required course)

  • English Composition

The course objective is to learn the core skills of English composition and how to apply those skills to become effective writer and engaged reader; gain proficiency with all of the steps in the writing process while creating original compositions in the narrative, the informative, and the argumentative modes; and learn how to read in an active, inquisitive manner and analyze the rhetorical situation of a text or the student’s own compositions.

Prerequisite:None

Course ID: ENC 1101
Credits: 4

Communication (Select 3 courses)

Humanities (Select 2 courses)

Math/Natural Sciences (Required courses)

  • Advanced Algebra
  • Discrete Mathematics for Data Analytics
  • Essential Statistics and Analytics

Students will learn about topics including functions and functional notation, domains and ranges in relation to functions, graphing functions and relations, and various function operations. Students will be able to solve linear equations and inequalities as well as quadratic equations and higher-order polynomial equations. This course will review algebraic technique as well as polynomials, factoring, exponents, roots, and radicals.

Prerequisite:Satisfactory score on placement exam

Course ID: MAC 1106
Credits: 5


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.

Prerequisites:None

Course ID: QMB 2600C
Credits: 4


In this course students will be introduced to descriptive analytics. They will develop basic statistical literacy along with the ability to analyze and evaluate real-life problems using statistical methods. Students will learn to organize and present quantitative data by means of graphical and numerical methods. Topics include descriptive statistics, basic probability theory, discrete and continuous probability distributions, and sampling distributions.

Prerequisites:Satisfactory score on placement exam or passing grade in Practical Math or Combined Basic and intermediate Algebra

Course ID: STA 1625
Credits: 4

Social Sciences (Select 2 courses)

Major and Core Courses

Lower Division

  • C++ Programming
  • Database Fundamentals for Programmers
  • Introduction to Business
  • Fundamentals of Data Analytics
  • Software Design Using C#
  • Object-Oriented Programming Using Java
  • Introduction to Linux in Analytics
  • Data Platforms
  • Fundamentals of Data Visualization
  • Introduction to Data Warehousing
  • Introduction to Analytics Environments
  • Open Source Scripting Languages

This course is designed to teach the student C++ programming utilizing object-oriented terminology. C++ expressions, decisions, and loops within the C++ realm are explored and practiced. This first course in a two course sequence ends with an analysis of functions and classes and how these elements are used in different programming projects.

Prerequisite:None

Course ID: COP 1350C
Credits: 4


This course covers relational databases and their efficient design. The course will include the definition of tables and indexes, logical and physical design, the E-R model, and transaction management. The use of Structured Query Language (SQL) will be emphasized.

Prerequisite:None

Course ID: COP 1532C
Credits: 3


This course is a study of the characteristics and functions of business in a free enterprise environment and how business impacts the economy in which we live. Characteristics studied may include opportunities, organizations, management, marketing, analysis and any other activities related to general ownership and operation.

Prerequisites:None

Course ID: GEB 1011
Credits: 4


In this course, students will be introduced 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:None

Course ID: QMB 1000C
Credits: 3


In this course, students will be introduced to fundamental aspects of programming and proper C# software design concepts. Students will gain an understanding of how computational techniques and software engineering processes are applied in solving a variety of problems. Topics include the use of flowcharts, pseudocode, UML diagrams, and the C# language to implement solutions.

Prerequisites:None

Course ID: QMB 1100C
Credits: 3


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.

Prerequisites:QMB 1100C Software Design Using C#

Course ID: QMB 1200C
Credits: 4


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.

Prerequisites:None

Course ID: QMB 2000C
Credits: 4


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.

Prerequisites:None

Course ID: QMB 2100C
Credits: 4


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.

Prerequisites:None

Course ID: QMB 2200C
Credits: 4


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.

Prerequisites:None

Course ID: QMB 2300C
Credits: 4


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.

Prerequisites:None

Course ID: QMB 2400C
Credits: 4


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.

Prerequisites:None

Course ID: QMB 2500C
Credits: 4

General Education Courses

Upper Division

Communication (Select 2 courses)

Humanities (Select 1 course)

Math/Natural Sciences (*Required course, select 1 additional course)

  • Inferential Statistics and Analytics

In this course students will be introduced to statistical methods used for predictive analytics. They will continue to build on their previous statics knowledge while strengthening their abilities to analyze and solve real-life problems using statistical methods. Topics may include, but are not limited to, estimation, hypothesis testing, correlation and regression, chi-square tests, and analysis of variance.

Prerequisite:Passing grade in Developmental Education coursework or placement determined by Rasmussen College entrance placement exam

Course ID: STA 3215*
Credits: 4

Social Sciences (Select 1 course)

Major and Core Courses

Upper Division

  • Fundamentals of Enterprise Architecture
  • Introduction to Business Intelligence
  • Emerging Trends in Technology
  • Business Project Management
  • Enterprise Resource Reporting
  • Operations Management
  • Introduction to Data Analytics
  • Foundations of Analytics Platforms, Environments, and Software
  • Introduction to Scripting
  • Introduction to Data Visualization
  • Data Elements
  • Applied Business Intelligence
  • Advanced Analytics Platforms, Environments, and Software
  • Data Quality in Analytics
  • Data Analysis and Optimization
  • Data Visualization Implementation and Communication
  • Data Analytics Capstone

This course is the study of business enterprise analysis, design, planning and implementation. It places focus on working with stakeholders, modeling business data flows and interfaces, determining the information security risk for an organization, and re-engineering business processes. Topics include current software development methodologies, business process modeling, and enterprise information security methodologies. This course will prepare students to work with stakeholders to ensure that information technology is in alignment with the goals of the business.

Prerequisite:None

Course ID: CDA 3315C
Credits: 4


This course is the study of the skills and techniques for analyzing business performance data to provide support for business planning. It places focus on using query development, reporting, and analytical tools to help guide business decision-making. Topics include statistical analysis, basic database design, and business process modeling. This course will prepare students to utilize information to support decision-making.

Prerequisite:None

Course ID: CTS 3265C
Credits: 4


This course is the study of emerging technologies. It places focus on technology impact on business and society in general. Topics include the relationship between emerging technologies and business opportunities, analysis of costs and savings of implementing particular technologies, legal and ethical issues affecting technology, challenges of adapting new technologies, and impacts of technology.

Prerequisite:None

Course ID: CTS 4557
Credits: 3


This course provides students with the essential elements and foundational standards used to manage projects, programs and portfolios in any organization. Students will develop project scope and scheduling skills as well as assess program bidding and proposal processes. They will evaluate the impact of scope definition, and explore how to manage teams, expectations and project stakeholders.

Prerequisite:None

Course ID: GEB 3422
Credits: 4


In this course students will develop an understanding of advanced enterprise resource reporting and business intelligence and how businesses can use them to support decision-making. Major electronic techniques and tools for classifying, segmenting, and analyzing business information will be examined. Students will learn how to integrate enterprise resource tools into standard business processes.

Prerequisite:None

Course ID: IDC 3152
Credits: 4


In this course students examine the operations function of managing people, information, technology, materials, and facilities to produce goods and services. Specific areas covered will include: designing and managing operations; purchasing raw materials; controlling and maintaining inventories; and producing goods or services that meet customers' expectations. Quantitative modeling will be used for solving business problems.

Prerequisite:None

This course is offered in a competency-based format for some programs.

Course ID: MAN 3504
Credits: 4


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.

Prerequisite:None

Course ID: QMB 3000
Credits: 4


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:None

Course ID: QMB 3100
Credits: 4


This course serves as an introduction to the scripting process as it relates to data extraction and transformation processes.

Prerequisite:None

Course ID: QMB 3200
Credits: 4


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

Course ID: QMB 3300
Credits: 4


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

Course ID: QMB 4000
Credits: 4


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

Course ID: QMB 4100
Credits: 4


This course prepares the student for 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 job. 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

Course ID: QMB 4200
Credits: 4


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

Course ID: QMB 4300
Credits: 4


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.

Prerequisite:QMB 4000 Data Elements; QMB 4300 Data Quality in Analytics

Course ID: QMB 4400
Credits: 4


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

Course ID: QMB 4500
Credits: 4


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:None

Course ID: QMB 4900
Credits: 3

Lower Division General Education Credits: 45

Lower Division Major and Core Credits: 45

Upper Division General Education Credits: 24

Upper Division Major and Core Credits: 66

Total Bachelor's Degree Credits: 180

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