Master of Science in Cybersecurity – Data Science Online

Develop the technical expertise to qualify for expanded career opportunities as a cybersecurity professional. Explore the strategies and technology that are essential to network security.

Apply by: 5/8/23
Start classes: 5/22/23
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Program Overview

Learn more about the M.S. in Cybersecurity – Data Science online program

Total Tuition: $29,550
Program Duration: As few as 24 months
Credit Hours: 30

Advance your understanding of network theory and digital forensics with the online Master of Science in Cybersecurity with a Concentration in Data Science from La Salle University. This interdisciplinary program emphasizes the modern technical skills used to defend networks against cyberattacks. Learn to analyze network traffic, install firewalls and create encryption formats to ensure the safety of data and networks.

The 100 percent online coursework for this program furthers your understanding of current and emerging network security strategies for Windows and Linux networks, and all courses are taught by experienced faculty with valuable industry connections. Designed for working professionals, this master’s in cybersecurity and data science expands your career opportunities in a growing field.

Graduates of the online M.S. in Cybersecurity – Data Science program will learn how to:

  • Explain internet structures, enterprise network structures and consulting services related to network infrastructures
  • Identify and analyze federal global legislation related to security and data threats
  • Differentiate between cybercrime, cyber espionage and cyberwar
  • Analyze plans to protect personal, corporate and national infrastructures
  • Formulate plans for securing and analyzing digital forensic data
  • Explain internet structures, enterprise network structures and consulting services related to network infrastructures
  • Identify and analyze federal global legislation related to security and data threats
  • Differentiate between cybercrime, cyber espionage and cyberwar
  • Analyze plans to protect personal, corporate and national infrastructures
  • Formulate plans for securing and analyzing digital forensic data

Upon completion of the online M.S. in Cybersecurity – Data Science program, you will be prepared for a variety of cybersecurity roles, including:

  • Information Security Analyst
  • Cybersecurity Forensics Technician
  • Cybersecurity Officer
  • Chief Security Officer
  • Information Security Analyst
  • Cybersecurity Forensics Technician
  • Cybersecurity Officer
  • Chief Security Officer

Online technology programs also available:

La Salle University offers a variety of specialized technology programs. Check out all of our technology online programs.

Total Tuition: $29,550
Program Duration: As few as 24 months
Credit Hours: 30
Need More Information?

Call 844-466-5587

Call 844-466-5587

Tuition

Our affordable tuition won’t compromise your budget

Our M.S. in Cybersecurity – Data Science online program offers affordable, pay-by-the-course tuition which is the same for in-state and out-of-state students. All program and course fees are included in the total tuition cost.

Tuition breakdown:

Total Tuition: $29,550
Per Credit Hour: $985

Tuition breakdown:

Total Tuition: $29,550
Per Credit Hour: $985

Calendar

Find the start date that works best for you

La Salle University online programs are delivered in an accelerated format ideal for working professionals, conveniently featuring multiple start dates each year.

Now enrolling:

Next Apply Date: 5/8/23
Next Class Start Date: 5/22/23
TermStart DateApp DeadlineDocument DeadlineRegistration DeadlineTuition DeadlineClass End DateTerm Length
Summer 15/22/235/8/235/12/235/17/235/18/237/16/238 weeks
Fall 18/28/238/14/238/18/238/23/238/24/2310/22/238 weeks
Fall 210/23/2310/9/2310/13/2310/18/2310/19/2312/17/238 weeks

Now enrolling:

Next Apply Date: 5/8/23
Next Class Start Date: 5/22/23

Have questions or need more information about our online programs?

Ready to take the rewarding path toward earning your degree online?

Admissions

Explore the admission process for the M.S. in Cybersecurity – Data Science online program

Applications for the M.S. in Cybersecurity – Data Science are evaluated on a holistic basis. The Admissions Committee takes into account interest, aptitude and potential for achievement in graduate studies. The requirements include:

Admission Requirements:

  • Bachelor’s degree from an accredited institution
  • Minimum 3.0 GPA
  • No GMAT required

Prior to evaluation by the Admissions Committee, applicants must submit the following:

  • Transcript(s) from the college/university where you earned your bachelor’s degree and, if applicable, master’s degree. You will be notified if you need to submit additional transcripts for advising purposes.
  • Minimum 3.0 GPA
  • No GMAT/GRE required
  • A current professional resume
  • Provide a personal statement (about 500 words in length) explaining why you are interested in this program, your qualifications and how this program will assist with your professional goals

Documentation can be sent via email to [email protected]. If you need to submit official documents by mail, send them to:

La Salle University
Office of Adult Enrollment
Box 112
1900 West Olney Avenue
Philadelphia, PA 19141

Have a question? Call us at 844-466-5587.

Courses

Discover the innovative coursework for our M.S. in Cybersecurity – Data Science online program

For the M.S. in Cybersecurity – Data Science online, the curriculum is comprised of five core courses, four specialty courses and the integrated capstone course.

Duration: 8 weeks
Credit Hours: 3
Lecture/theory course considers the current methods, practices, and standards used to enable communication on computer and voice networks. This includes a study of the physical layers, architectural layers, design, operation, management, and ISO standards, with particular and telephony technologies. Both local and wide area networks are examined.
Duration: 8 weeks
Credit Hours: 3
This course introduces students to the differences between cybercrime, cyber espionage, and cyber warfare by discussing the relationship of cyber intrusions and cybersecurity to nations, businesses, society, and people. Students will use case studies to analyze the threats, vulnerabilities and risks present in these environments, and develop strategies to reduce the breaches and mitigate the damages.
Duration: 8 weeks
Credit Hours: 3
Computers have made organizations easier to run. All accounting information, inventory records, customer data, and intellectual property that an organization possesses is contained somewhere in an electronic file. As such, these electronic files are vulnerable to attacks from both employees and outsiders from around the world. This course will provide the student with an understanding of how computer fraud and manipulation is accomplished and what security measures should be instituted to prevent it.
Duration: 8 weeks
Credit Hours: 3
This course examines techniques used to conduct computer crime investigations and gather probative evidence to secure a conviction under state and federal laws. Students will simulate a computer forensic investigation: developing an investigation plan, securing the crime scene, analyzing evidence, preparing the case for court, and testifying in a moot court situation.
Duration: 8 weeks
Credit Hours: 3
Students will study and implement basic computer and network security strategies on Window and Linux networks. Students examine and analyze network traffic, including investigating wireless transmission, install firewalls and define Internet Protocol Security Controls (IPSEC). Labs include system hardening, dissecting network packet structure and creating encryption formats; managing authentication and access controls. Students study implementing a public key infrastructure and best strategies for using intrusion detection systems.
Duration: 8 weeks
Credit Hours: 3
The capstone project is an opportunity to pursue an independent learning experience focused on a specific aspect of economic crime forensics based on the student's interest. The capstone is intended to extend students beyond the coursework and cases to apply knowledge in ways that are relevant to their professional goals. Students will work on a research project or in an experiential learning environment. Each student will be required to present his/her capstone both as an oral presentation and a summary written document.
Choose 4 of the following:
Duration: 8 weeks
Credit Hours: 3
This course introduces students to mathematical models that can be employed to make informed decisions in a wide variety of data-driven fields, including (but not limited to) finance, banking, marketing, health care, retail, manufacturing, and transportation. Goals such as increasing revenue, decreasing costs, and improving overall efficiency of operations in the face of various constraints are considered. Students learn to recognize when a problem lends itself to a particular type of model, formulate the model, and use appropriate methods to solve or extract information from the model. Particular emphasis is placed on linear programming (with exposure to network models and integer programs) and the simplex method. Forecasting, inventory management, and queueing models, as well as Markov chains, are also studied. Additional topics covered include sensitivity analysis, duality, decision analysis, and dynamic programming. Software (both spreadsheets and a computer algebra system) is employed consistently throughout the course to expedite the solution and analysis process; emphasis will be placed on the practical application of models rather than on the models' mathematical properties.
Duration: 8 weeks
Credit Hours: 3
This course entails analysis and evaluation of database designs in relation to the strategic mission of the project. Topics include database systems, database architectures, and data-definition and data-manipulation languages. Also included are logical and physical database design, database models (e.g., entity-relationship, relational), normalization, integrity, query languages including SQL, and relational algebra, in addition to social and ethical considerations and privacy of data. This course incorporates case studies and a project using a relational DBMS.
Duration: 8 weeks
Credit Hours: 3
This course introduces the field of data mining, with specific emphasis on its use for Machine Learning algorithms. Techniques covered may include conceptual clustering, learning decision rules and decision trees, case-based reasoning, Bayesian analysis, genetic algorithms, and neural networks. The course covers data preparation and analysis of results. Skills in Microsoft Excel are useful.
Duration: 8 weeks
Credit Hours: 3
This course introduces students to the field of artificial intelligence (AI). Students will learn how big data and data mining techniques are utilized by machines to create the AI models used by autonomous aircraft and automobiles, personal assistants, IT security software, fraud investigations and credit bureaus. The course will review the history, present day use, and future of artificial intelligence. Through case studies and current events, students will examine the benefits and risks associated with AI. The course will cover issues related to AI and privacy, ethics, and machine bias. Neuromorphic computing, the Open Neural Network Exchange (ONNX), and data analytics will also be discussed.
Duration: 8 weeks
Credit Hours: 3
This course will require students to learn the R programming language and assess how to use it and find interesting features in data. Students will learn about R and statistical best practices and how to display data in a manner that will help you explain your findings to those who do not have a technical background. Moreover, the course introduces students to modeling and simulation. Topics may include basic queueing theory, the role of random numbers in simulations, and the identification of input probability distributions.

La Salle University is ranked in the top 50 percent of national universities nationwide by U.S. News & World Report (2023).

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