Bachelor of Science in Computer Science: Big Data Analytics Emphasis

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Degree Level

Bachelor's

Class Setting

Campus

Area of Study

Business

Program

BS in Computer Science: Big Data Analytics

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Country

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Overview

Analyze Big Data and Assess Algorithms With a BS in Computer Science

The Bachelor of Science in Computer Science with an Emphasis in Big Data Analytics program at Grand Canyon University is designed to help you acquire comprehensive knowledge in data science and computer software design. You will explore the intricacies of big data analysis, and evaluate and enhance existing algorithms and methodologies while practicing ways to develop your own. This program aims to actively engage you in continuous skill development, ensuring a robust understanding and application of these essential concepts.

As a student within the College of Engineering and Technology at GCU, you will be presented with various objectives, including:

An ability to apply knowledge of computing, mathematics and statistics
The capability to analyze a problem and identify and define the scientific methodologies appropriate to its solution
Proficiency in designing, implementing and evaluating computer-based systems, processes or programs
Collaborative skills in team environments
An understanding of all responsibilities related to computer science and big data analytics work

$103,500

Median annual wage for data scientists in May 2022^{(See disclaimer 1)}

Bachelor of Science in Computer Science with an Emphasis in Big Data Analytics

Offered By

College of Engineering and Technology

Class Settings

Campus

Tuition Rate

Campus: $8,250 per semester [More Info]

Cost of Attendance

Course Information

Credits: 128

Campus: 15 weeks

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Transfer Credits

Up to 90 credits, only 84 can be lower division

Credits: Fill out the Lopes Eval to find out what will transfer

Admission Requirements

Admission Requirements (Bachelor's)

16+ years old
High School Graduate
3.0+ Unweighted GPA

OR 2.5+ Unweighted GPA and

ACT: 19
SAT: 1000*

Admission requirements may differ based on degree level, program and modality, or transfer status. Some programs of study may require a higher GPA and/or other qualifying criteria for admission. Please review full admission and program requirements in the University Policy Handbook.
*Math and reading only on a 1600 point scale (test date after 3/1/2016). SAT score of 1380 required for 2400 point scale (test date before 3/1/2016).

Why GCU

Earn Your Computer Science Degree With a Big Data Analytics Focus at GCU

Big data refers to a very large volume of diverse data that can be used and collected by computing systems. Heavy volumes of data can bring with them their own challenges, such as difficulties in classifying complex datasets. Organizations rely on big data analytic experts to classify and organize big data and derive valuable insights from it.^{(See disclaimer 2)} Within this big data analytics emphasis, you will be taught how to use and design methods and software applications that mine and analyze data from a variety of sources. You will also be expected to acquire a strong foundation in data science and data analytics software development.

All Computer Science Emphasis Programs

Business Entrepreneurship

Program Opportunities

You will have opportunities to acquire a framework of knowledge in computer science and other sciences, with a deep dive into calculus, linear algebra, discrete math and statistics. You will progress through sequentially arranged coursework that is in-depth in nature and broad in scope. This multidisciplinary bachelor’s in computer science covers relevant topics in chemistry, biology and human-computer interaction.

Take Big Data Analytics Courses on Campus

This bachelor’s degree in computer science is available in person on our campus in Phoenix. As a member of our supportive learning community, you’ll take classes taught by instructors who are knowledgeable in their fields, and you’ll have opportunities to participate in academically engaging discussions with your peers. Work through this multidisciplinary curriculum while being immersed within our lively campus culture that offers an incredible range of amenities and learning support resources.

Christian Worldview

Capstone

Coursework

Explore Big Data Analytics Course Topics

In this degree, you will be taught how to work in a variety of information systems and how to create systems that analyze data with greater efficiency. The goal of this program is to graduate work-ready professionals who can be prepared to improve and support an organization's processes and extraction of actionable information from data collected.

In order to provide you with opportunities to develop industry-aligned competencies, our big data analytics courses offer a blend of theoretical and hands-on curriculum that cover topics such as:

Large-scale data processing

Statistical analysis

Computer programming using discipline-specific tools and methods

Data mining and interpretation

Pattern analysis

System architecture and organization

Information assurance and security

Data modeling

Social network theory

Search engine design

Careers

Career Paths for Graduates With a Bachelor’s Degree in Computer Science

After graduating with this degree, you may have the capacity to adopt new developments in computer science technologies and may seek work in applying theoretical foundations to solve practical problems in fast-paced, high-tech environments. This BS in Computer Science degree will teach you communication and collaboration skills to work with organizational stakeholders.

With a firm foundation of computer science and big data analytics competencies, you may be prepared to pursue work opportunities as:

Computer and information system managers

Computer programmers

Software developers

Information security analysts

Software quality assurance analysts and testers

Data scientists

Computer and information research scientists

Computer network support specialists

Database architects

Web developers

Web and digital interface designers

Postsecondary computer science teachers

FAQ

BS in Computer Science: Big Data Analytics Emphasis FAQs

If you’re eager to pursue a STEM career, then a bachelor’s degree in computer science may be a good choice for you. The following FAQs and answers can provide a starting point for more career-related research.

Are big data careers worth it?

Big data analytics professionals can provide key insights for a wide range of industries and practically every department in an organization — from marketing and sales to human resources and beyond. The many companies that manage large amounts of data typically require big data experts to help all the numbers make sense.^{(See disclaimer 2)}

With this in mind, there are increased job opportunities for well-educated, big data professionals in various organizations. According to the U.S. Bureau of Labor Statistics (BLS), the job growth for data scientists is projected to grow by an estimated 35% from 2022 to 2032, much faster than average, accounting for an estimated increase of 59,400 jobs in the field.^{(See disclaimer 3)}

What is the difference between big data and data science?

While these two work together, they are not exactly the same. Big data analytics refers to using software systems to manage large amounts of data, discover patterns and use that data to enhance a company’s competitive edge in the market. Data science, on the other hand, is the field of study that explores data analytics. Data science, which is an interdisciplinary field of study combining computer science, business and statistics, seeks to establish a framework for developing algorithms and applying statistical analysis to extract meaningful information from big data.^{(See disclaimer 4)}

Does big data analytics require coding?

What degree is best for data analytics?

There are multiple degree offerings within a computer science discipline that may cover areas of data analytics. However, a bachelor's in computer science with a big data analytics emphasis is focused on teaching you the specific math, analytics and methodologies you may need to pursue a career in data analytics. With your bachelor’s, you can then go on to explore further education options and potentially position yourself for advancement in the field of data analytics. You may decide to pursue a Master of Science in Data Science degree, during which you will be expected to perform deeper research and identify applications for modern-day data systems.

Why is big data analytics important?

Virtually every type of industry imaginable can benefit from investing in big data analytics. For all types of companies, applying insights derived from data analytics can help identify opportunities for growth, improved operational efficiency, cost reductions and smarter, more customer-friendly product/service planning. Data analytics can even be used to improve society, such as by analyzing information pertaining to outbreaks of diseases and offering insights into how best to handle these public health crises. This area of computer science can contribute to positive improvements in education, banking, marketing, government and even entertainment.^{(See disclaimer 6)}

Courses

Program Curriculum

Credit Summary

General Education Requirements34-40 credits

Major88 credits

Open Elective Credits0-6 credits

Degree Requirements128 credits

General Education Requirements

General Education coursework prepares Grand Canyon University graduates to think critically, communicate clearly, live responsibly in a diverse world, and thoughtfully integrate their faith and ethical convictions into all dimensions of life. These competencies, essential to an effective and satisfying life, are outlined in the General Education Learner Outcomes. General Education courses embody the breadth of human understanding and creativity contained in the liberal arts and sciences tradition. Students take an array of foundational knowledge courses that promote expanded knowledge, insight, and the outcomes identified in the University's General Education Competencies. The knowledge and skills students acquire through these courses serve as a foundation for successful careers and lifelong journeys of growing understanding and wisdom.

Competency

4 Total Credits

Course Description

Upon completion of the Grand Canyon University's University Foundation experience, students will be able to demonstrate competency in the areas of academic skills and self-leadership. They will be able to articulate the range of resources available to assist them, explore career options related to their area of study, and have knowledge of Grand Canyon's community. Students will be able to demonstrate foundational academic success skills, explore GCU resources (CLA, Library, Career Center, ADA office, etc), articulate strategies of self-leadership and management and recognize opportunities to engage in the GCU community.

Competency

9-12 Total Credits

Competency

4 Total Credits

Competency

11-12 Total Credits

Course Description

Graduates of Grand Canyon University will be able to use various analytic and problem-solving skills to examine, evaluate, and/or challenge ideas and arguments (mathematics, biology, chemistry, physics, geology, astronomy, physical geography, ecology, economics, theology, logic, philosophy, technology, statistics, accounting, etc.). Students are required to take 3 credits of intermediate algebra or higher.

Competency

6-8 Total Credits

Course Description

Graduates of Grand Canyon University will be able to demonstrate awareness and appreciation of and empathy for differences in arts and culture, values, experiences, historical perspectives, and other aspects of life (psychology, sociology, government, Christian studies, Bible, geography, anthropology, economics, political science, child and family studies, law, ethics, cross-cultural studies, history, art, music, dance, theater, applied arts, literature, health, etc.). If the predefined course is a part of the major, students need to take an additional course.

Required General Education Courses

MAT-261

4 Total Credits

Course Description

This course presents the fundamentals of algebra and trigonometry with some applications; it provides the background and introduction for the study of calculus. Topics include review of linear equations and inequalities in one and multiple variables; functions and their graphs; polynomial, rational, exponential, logarithmic, and trigonometric functions; systems of equations and matrices; and sequences and series. Slope and rate of change are introduced to set up the concepts of limits and derivatives. There is an emphasis on both an understanding of the mathematical concepts involved as well as their applications to the principles and real-world problems encountered in science and engineering. Technology is utilized to facilitate problem analysis and graphing. Prerequisite: MAT-134 or MAT-154.

MAT-262

4 Total Credits

BIO-181

3 Total Credits

Course Description

This course is a study of biological concepts emphasizing the interplay of structure and function, particularly at the molecular and cellular levels of organization. Cell components and their duties are investigated, as well as the locations of cellular functions within the cell. The importance of the membrane is studied, particularly its roles in controlling movement of ions and molecules and in energy production. The effect of genetic information on the cell is followed through the pathway from DNA to RNA to protein. Co-requisite: BIO-181L.

BIO-181L

1 Total Credits

Course Description

This lab course is designed to reinforce principles learned in BIO-181 through experiments and activities which complement and enhance understanding of macromolecules, cell membrane properties, cellular components, and their contribution to cell structure and function. Assignments are designed to relate cellular processes such as metabolism, cell division, and the flow of genetic information to cell structure. Co-requisite: BIO-181.

CST-320

4 Total Credits

Course Description

This course reviews the basic principles, tools, and techniques used in computer applications that enable communication, visualization, access to information, learning and entertainment. Students learn the methods of designing, implementing and evaluating techniques for effective communication in a technical, business, education or entertainment context. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on experiencing and implementing concepts discussed in the lecture. Students create applications that communicate ideas efficiently and are easy to use. This is a writing intensive course. Prerequisites: CST-201, MAT-262, and (CST-217 or CST-341).

STG-390

4 Total Credits

Course Description

This writing intensive course provides an insight into professional communications and conduct associated with careers in science, engineering and technology. Students learn about the changing modes of communication in these disciplines recognizing the advances in digital communications. They gain practical experience developing and supporting a thesis or position through written, oral, and visual presentations prepared and delivered individually and in groups. Students will explore concepts and issues in professional ethics and conduct such as privacy, discrimination, workplace etiquette, cyber-ethics, network and data security, identity theft, ownership rights and intellectual property.

Core Courses

CST-105

4 Total Credits

CST-210

4 Total Credits

PHY-121

3 Total Credits

Course Description

This course is a calculus-based study of basic concepts of physics, including motion; forces; energy; the properties of solids, liquids, and gases; and heat and thermodynamics. The mathematics used includes algebra, trigonometry, and vector analysis. A primary course goal is to build a functional knowledge that allows students to more fully understand the physical world and to apply that understanding to other areas of the natural and mathematical sciences. Conceptual, visual, graphical, and mathematical models of physical phenomena are stressed. Students build critical thinking skills by engaging in individual and group problem-solving sessions. Prerequisite: MAT-262 or higher. Co-Requisite: PHY-121L.

PHY-121L

1 Total Credits

Course Description

This calculus-based course utilizes lab experimentation to practice concepts of physical principles introduced in the PHY-121 lecture course. Students are able to perform the proper analysis and calculations to arrive at the correct quantifiable result when confronted with equations involving gravity, sound, energy, and motion. Prerequisite: MAT-262 or higher. Co-Requisite: PHY-121.

MAT-264

4 Total Credits

CST-215

4 Total Credits

CST-201

4 Total Credits

Course Description

This course covers classical algorithms and data structures, with an emphasis on implementation and application in solving real-world computational problems. The course focuses on algorithms for sorting, searching, string processing, and graphs. Students learn basic strategies to evaluate divide-and-conquer, recursive backtracking, and algorithm efficiency. Hands-on activities focus on writing code that implements concepts and algorithm implementation techniques. Prerequisite: CST-210 or CST-239 or CST-135 or CST-250 or CST-227.

CST-217

4 Total Credits

Course Description

This course provides students with the technical skills required to design and implement a database solution using a SQL server. Students use data definition language (DDL) to create and delete database objects, and data manipulation language (DML) to access and manipulate those objects. Students gain hands-on experience with database design, data normalization, SQL sub-queries, creating and using views, understanding and working with data dictionaries, and loading and unloading databases. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on writing code that implements concepts discussed in the lecture course, specifically creating databases and SQL queries. Prerequisite: CST-105.

MAT-345

4 Total Credits

Course Description

This course is intended primarily for mathematics, science, and engineering students. The goal of the course is to impart the concepts and techniques of modern linear algebra (over the real scalar field) with a significant level of rigor. Students write clearly about the concepts of linear algebra (definitions, counterexamples, simple proofs), and apply theory to examples. The course emphasizes the practical nature of solutions to linear algebra problems. Students implement some of these solutions, where appropriate, as computer programs. Prerequisite: MAT-264 or MAT-253

CST-307

4 Total Credits

Course Description

This course introduces current trends in computer architecture with a focus on performance measurement, instruction sets, computer arithmetic, design and control of a data path, pipelining, memory hierarchies, input and output, and a brief introduction to multiprocessors. The laboratory reinforces and expands learning of principles introduced in the lecture course. Hands-on activities focus on writing assembly language code that implements concepts discussed in the lecture course, focusing on registers, processes, threads, and I/O management. Prerequisites: (CST-210 and CST-215), or EEE-315.

MAT-374

4 Total Credits

Course Description

This course covers the role of statistics in engineering, probability, discrete random variables and probability distributions, continuous random variables and probability distributions, joint probability distributions, random sampling and data description, point estimation of parameters, statistical intervals for a single sample, and tests of hypotheses for a single sample. Prerequisite: MAT-253 or MAT-264.

ENT-436

4 Total Credits

CST-305

4 Total Credits

Course Description

This course covers applications of differential equations in modeling and simulation. Students use mathematical models for continuous and discrete simulation, and develop applications for complex systems across a variety of domains. Students learn how to represent a system by a model and then execute the model to generate and statistically analyze data. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on writing code that implements differential equation based modeling algorithms and visual simulations. Prerequisite: CST-201, CST-215, MAT-264.

CST-315

4 Total Credits

Course Description

This course explains the concepts, structure, and mechanisms of modern operating systems. The course covers computational resources, such as memory, processors, networks, security, and how the programming languages, architectures, and operating systems interact. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on writing a shell that implements process management, file management, and I/O management. Prerequisite: CST-307 or SWE-350.

CST-301

4 Total Credits

CST-425

4 Total Credits

Course Description

This course introduces modern theories of machine learning and design & implementation models for large scale quantitative, image, and text information systems. The machine learning and information retrieval includes methodologies such as Boolean, vector space, probabilistic, inference net, and language modeling. Students will acquire hands-on experience by implementing models such as clustering algorithms, automatic text categorization, and experimental evaluation. As an introduction to data science theory and techniques, students will experiment with supervised and unsupervised learning algorithms, intelligent text summarization, topic detection, tagging, and tracking. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on implementing techniques for efficiently managing and manipulating very large data sets and build machine learning models. Prerequisites: MAT-374 and (CST-217 or SYM-400) and MAT-345.

STG-451

2 Total Credits

Course Description

The first capstone course provides students the opportunity to work in teams to tackle real world applied research and design projects in their chosen area of interest. Students develop a project proposal, conduct a feasibility study, learn to protect intellectual property, develop teamwork skills, budgets, and a schedule for completing the project. Students conduct extensive research, integrate information from multiple sources, and work with a mentor through multiple cycles of feedback and revisions. Students use this course to further develop technical writing and business presentation skills. This is a writing intensive course. Prerequisites: CST-307, CST-315 and department approval.

ITT-305

2 Total Credits

CST-310

4 Total Credits

Course Description

This course covers 2D and 3D concepts, algorithms, and implementation methods using shader-based programming. Main topics covered include coordinate systems, transformations, material simulation, and animation. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on writing vertex shaders and fragment shaders to implement light equations for coloring effects, textures, materials, and animation. Prerequisites: CST-201, MAT-262, and MAT-345.

CST-405

4 Total Credits

Course Description

This course reviews the concepts and tools used in the development of compilers. Students synthesize topics covered in previous courses: formal languages, data structures, and computer architecture. The course reinforces the principles of software engineering and development through a complete cycle of building a working compiler. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on writing a compiler including a lexer, parser, semantic analyzer, code generator, and optimizer. Prerequisites: CST-301 and MAT-374.

CST-435

4 Total Credits

Course Description

This course provides a comprehensive introduction to neural networks and deep learning. The location, retrieval, and conversion of raw data into usable information is accomplished by implementing a variety of neural network models. Students implement deep learning algorithms for organizing and searching very large data collections, like those typically found in enterprise databases and on websites. Students use clustering and categorization to generate various information taxonomies based on document ranking, evaluation, and classification. The laboratory reinforces and expands deep learning principles introduced in the lecture. Hands-on activities focus on using neural networks for performing data mining on a large business database and extracting trends and actionable information. Prerequisites: MAT-374 and (CST-217 or SYM-400)and MAT-345.

STG-452

2 Total Credits

Course Description

The second capstone course provides students the opportunity to implement and present the applied research project designed, planned, and started in the first capstone course. The capstone project is a culmination of the learning experiences while a student in the Computer Science program. Students conduct extensive research, integrate information from multiple sources, and work with a mentor through multiple cycles of feedback and revision. This is a writing intensive course. Prerequisite: Successful completion of STG-451 with a grade of C or better.

ITT-306

2 Total Credits

CST-440

4 Total Credits

Course Description

This course focuses on very large web-based sources of information such as social networks and semantic networks. Students analyze dynamic data and trends, connections (links), and patterns of self-organization. Students then utilize intelligent inferential techniques to interpret patterns in the collected information and translate them into actionable items. Hands-on experiences include marketing, organizational structure, security, and human analytics. Prerequisites: MAT-374 and (CST-217 or SYM-400) and MAT-345.

CST-461

4 Total Credits

GCU cannot and will not promise job placement, a job, graduate school placement, transfer of GCU program credits to another institution, promotion, salary, or salary increase. Please see the Career Services Policy in the University Policy Handbook.
Please note that this list may contain programs and courses not presently offered, as availability may vary depending on class size, enrollment and other contributing factors. If you are interested in a program or course listed herein please first contact your University Counselor for the most current information regarding availability.
Please refer to the Academic Catalog for more information. Programs or courses subject to change

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The earnings referenced were reported by the U.S. Bureau of Labor Statistics (BLS), Data Scientists as of May 2022, retrieved on Nov. 6, 2023. Due to COVID-19, data from 2020 and 2021 may be atypical compared to prior years. The pandemic may also impact the predicted future workforce outcomes indicated by the BLS. BLS calculates the median using salaries of workers from across the country with varying levels of education and experience and does not reflect the earnings of GCU graduates as data scientists. It does not reflect earnings of workers in one city or region of the country. It also does not reflect a typical entry-level salary. Median income is the statistical midpoint for the range of salaries in a specific occupation. It represents what you would earn if you were paid more money than half the workers in an occupation, and less than half the workers in an occupation. It may give you a basis to estimate what you might earn at some point if you enter this career. You may also wish to compare median salaries if you are considering more than one career path. Grand Canyon University can make no guarantees on individual graduates’ salaries as the employer the graduate chooses to apply to, and accept employment from, determines salary not only based on education, but also individual characteristics and skills and fit to that organization (among other categories) against a pool of candidates.
Segal, T. (2022, Nov. 29). What is Big Data? Definition, How it Works, and Uses. Investopedia. Retrieved on Nov. 6, 2023.
COVID-19 has adversely affected the global economy and data from 2020 to 2022 may be atypical compared to prior years. Accordingly, data shown is effective September 2023, which can be found here: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook, Data Scientists, retrieved on Nov. 6, 2023.
Geeks for Geeks. (2023, March 27). Difference Between Big data and Data Science. Retrieved on Nov. 6, 2023.
BrainStation. (n.d.). What Does a Data Analyst do? Retrieved on Nov. 6, 2023.
Coursera. (2023, June 15). What is Big Data Analytics? Definition, Benefits, and More. Retrieved on Nov. 6, 2023.

Bachelor of Science in Computer Science: Big Data Analytics Emphasis

Analyze Big Data and Assess Algorithms With a BS in Computer Science

Earn Your Computer Science Degree With a Big Data Analytics Focus at GCU

Program Opportunities

Explore Big Data Analytics Course Topics

Career Paths for Graduates With a Bachelor’s Degree in Computer Science

Graduate From an Institutionally Accredited University

BS in Computer Science: Big Data Analytics Emphasis FAQs

Program Curriculum

General Education Requirements

Required General Education Courses

Core Courses