Master of Science in Computer Information Science with a Concentration in Data Science Online

Apply technical solutions to real-world problems with the techniques and methodologies you learn in our online M.S. in Computer Information Science – Data Science.

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

Find out more about our online M.S. in Computer Information Science – Data Science

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

Master software application and engineering methods to solve real-world problems in our online Master of Science in Computer Information Science with a Concentration in Data Science. This flexible program is designed for working adults and allows you to focus on the strategies and skills that align with your desired career path toward management, policy work or technical applications.

This CIS M.S. in Data Science offers a comprehensive study of data science, including mobile development, database services, enterprise and web solutions, delving into how these domains work together. Demand for computer and information technology experts is projected to increase much faster than average occupational growth as companies increasingly rely on cloud computing, big data and information security.

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

  • Create, plan, implement and test a technical problem solution
  • Develop problem definitions and solution designs
  • Create solutions specific to current technologies, including mobile development, database services and web services
  • Create client-side and server-side designs for problem solutions
  • Create, plan, implement and test a technical problem solution
  • Develop problem definitions and solution designs
  • Create solutions specific to current technologies, including mobile development, database services and web services
  • Create client-side and server-side designs for problem solutions

Upon completion of the online M.S. in Computer Information Science – Data Science, you will be prepared for a wide range of roles, including:

  • Software Developer
  • Business Analyst
  • Information Analyst
  • Software Developer
  • Business Analyst
  • Information Analyst

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

Pay your tuition as you go

Our M.S. in Computer Information Science – 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

Fit the start date that works best for you into your schedule

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

Application to this online master’s program is simple

Applications for the M.S. in Computer Information Science – 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

Find out more about the topics you will explore

For the M.S. in Computer Information Science – 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
This course addresses the design and development of standards-based client interfaces for Web applications. The course includes Web-based standards and toolsets that support these standards. Application development emphasizes client Web interface scripting to serve as a general introduction to computer programming. The specific toolset used will depend on the types of interfaces to be developed, considering technology trends. Examples of possible tools include XHTML, CSS, and JavaScript. This course may be waived if the student has prior experience in client interface development.
Duration: 8 weeks
Credit Hours: 3
This course focuses on the development of Web services for use by many different types of Web applications. The course develops basic programming techniques to implement the server side function of the application. The course uses a non-Windows interface for the tools set.
Duration: 8 weeks
Credit Hours: 3
This course encompasses programming models that support database access, including ADO.NET. It covers client/server and multitiered architectures; development of database applications; Internet and intranet database design and implementation; database-driven Web sites; and use of XML syntax related to databases. Examples of the possible tool sets for this tool set are PHP and mySQL on either a Linux or Windows server. The course also considers privacy of data and data protection on servers.
Duration: 8 weeks
Credit Hours: 3
This course covers development of mobile applications and integration with existing systems on the devices. Students will extend development of mobile solutions with enhancements to views, layouts, and intents including interaction with the location-based services, messaging services, multimedia interfaces, and sensors available on the mobile device. The applications will manage data sources, both locally and from database providers. The applications will be tested in an emulation environment and prepared for deployment in a mobile marketplace.
Duration: 8 weeks
Credit Hours: 3
Special Topics as assigned by student/faculty.
Duration: 8 weeks
Credit Hours: 3
Students culminate their learning with a capstone project under the supervision of a faculty advisor. Some students partner with an external company or work on a project associated with their employer as a project deliverable for that company. Prerequisite: All Core courses.
Choose 4 of the following:
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.
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.

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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