Data Science, M.S.

Learn how to design algorithms to uncover the secrets hiding in big data.

More and more industries are looking for knowledgeable individuals who can mine tremendous amounts of data to glean powerful and profitable business intelligence.

In fact, the entire business models of companies like Google and Facebook rely solely on this data mining and predictive analysis. But companies can only act on the actionable information they have available to them. Little do many realize how much information lives within their data, ready to be deciphered - ready to be acted upon. For instance:

  • How are Amazon and Netflix able to build such accurate recommendation engines based on past customer purchases?
  • How is Nate Silver able to predict election results before voting day so accurately?
  • How are health organizations able to track disease outbreaks and strategically protect against their spread?
  • How did Billy Beaneís use of analytics change the way baseball and subsequently all professional sports are played?
  • How can pharmaceutical providers customize medications based on individual health factors and medical history?
Most importantly, how can you help companies utilize these same data mining and predictive analyses to optimize organizational processes? That's exactly what Lewis University's master's in data science degree program will prepare you for. In fact, Lewis' supportive educational community and state-of-the-art Science Center offers the perfect preparation for success as a Data Scientist through an interdisciplinary curriculum in which you will:

  • Design and develop software and systems to store, manage, query, process and interpret big data
  • Devise algorithms to identify reasonable trends buried in large data sets
  • Determine how best to display data to communicate actionable recommendations
  • Develop an in-depth understanding of the mathematics and computing of data science techniques and technologies
  • Use real-time and historical data to solve real-world business problems
  • Learn how to evaluate and compare large-scale and cloud-based data storage solutions that meet specific performance, security, query, functional and cost requirements
  • Choose appropriate data mining and analysis techniques to solve specific problems
  • Conclude your graduate experience by researching and writing your thesis capstone, applying data mining and analytics to your specific industry
Why Lewis for Your Master's in Data Science?
  • Ranked as one of U.S. News & World Report's "Top Tier Colleges"
  • One of the most affordable Master's in Data Science programs in the region
  • Small interactive classes provide the ideal learning environment to learn with and from your peers
  • 8-week courses offered on-campus or online to accommodate the working professional
  • Great balance between theory and practical application (learn how the tools work and how to work the tools)
  • Flexible curriculum to fit your interests and current skillset
  • Brand new Science Center houses some of the most impressive computer science resources in the region
  • Leverage cutting-edge cloud computing resources and software suites as Matlab, Maple and SPSS
Customize your Master's in Data Science degree with one of the following concentrations to match your specific career interests:

Life Sciences
Designed for the analyst working in the healthcare industry, this curriculum will focus on real-world applications. You will use data mining to design customized pharmaceuticals, maximize patient outcomes, track the spread of disease, optimize ambulance routes and more.

Computer Science
This curriculum is best suited to students with an undergraduate degree in Computer Science. You will focus on real-world applications including designing large-scale storage systems and writing applications that manage, query and visualize large data sets on a variety of computing platforms.

Life after Lewis
Our graduates are able to bring their newfound skills to a wide range of successful career options, such as:

  • Data Scientist
  • Data Analyst
  • Data Strategist
  • Data Engineer
  • Predictive Modeler
  • Computer and Information Research Scientist
  • Market Research Analyst
  • Risk Manager
100% Online Program
This degree program is offered within a flexible 100% online format - convenient for the working adult.

Data Science or Information Security?
Data Science tools have become an essential resource for cyber security threat identification, prioritization and prevention. Our two graduate fields are complementary pursuits and Data Scientists and Information Security Specialists at Lewis will work together very closely to create solutions to mine, interpret and protect an organizationís data.

Certificate Option
This 18-hour certificate program is an alternative for students looking to build their data science skills, but do not wish to pursue the full graduate degree. This certificate does not require the writing of a thesis. Students who start out pursuing the certificate option would be able to switch over to the degree program if they wish.

Minimum Admission Requirements:
  1. A baccalaureate degree from an accredited institution of higher education.
  2. A minimum undergraduate GPA of 3.0 on a 4.0 scale.
  3. Undergraduate mathematics coursework in Calculus.
  4. A completed application for graduate admission
  5. Professional resume
  6. Official transcripts from all educational institutions attended
  7. Two-page statement of purpose
  8. Two letters of recommendation
  9. International students are required to have a TOEFL test score greater than 550 (computer-based 213; Internet-based 79)
Degree Requirements
Total Credit Hours: 36

Data Science Core (24 hours)
13-510 Mathematics for Data Scientists (3)
13-511 Concepts of Statistics I (3)
13-512 Concepts of Statistics II (3)
70-510 Introduction to Data Mining and Analytics (3)
70-511 Statistical Programming (3)
70-525 Encryption and Authentication Systems (3)
70-530 Data Visualization (3)
70-540 Large-Scale Data Storage Systems (3)

Concentration in Data Mining and Analytics for Life Scientists (12 hours)
02-509 Introduction to Computational Biology (3)
02-510 Data Systems in the Life Sciences (3)
02-512 Research in Biotechnology (3)
02-590 Data Mining and Analytics Thesis for Life Scientists (3)

Concentration in Data Mining and Analytics for Computer Scientists (12 hours)
70-590 Data Mining and Analytics Project for Computer Scientists (3)
and choose three (3) of the following courses:
70-517 Pervasive Application Development (3)
70-550 Machine Learning (3)
70-552 Semantic Web (3)
70-555 Distributed Computing Systems (3)

Graduate Certificate in Data Analytics for the Life Sciences
(18 hours)

02-509 Introduction to Computational Biology (3)
02-510 Data Systems in the Life Sciences (3)
70-510 Introduction to Data Mining and Analytics (3)
70-511 Statistical Programming (3)
70-530 Data Visualization (3)
70-540 Large-Scale Data Storage Systems (3)

Course Descriptions

02-509 Introduction to Computational Biology (3)
This course will cover the computational techniques used to access, analyze, and interpret the biological information in common types of biological databases and the biological questions that can be addressed by such methods, applicable to the study of the context of genes within the same genome and across different genomes, the study of molecular sequence data for the purpose of inferring the function, interactions, evolution, and structure of biological molecules, and the study of annotation and ontology.

02-510 Data Systems in the Life Sciences (3)
This is a continuation of 02-509. Students will examine how bioinformatics, statistics and computation are being used to support the discovery of new biomedical knowledge and learn the basics of computational methods used to analyze molecular sequences and structures.
Prerequisite: 02-509

02-512 Research in Biotechnology (3)
Methods and sources for conducting research in biotechnology. A series of guest presentations will expose students to current trends in and applications of biotechnology. Students will conduct their own research based on these presentations. Use of primary sources, data collection techniques, and ethical conduct of research will be emphasized.
Prerequisite: 02-510

02-590 Data Mining and Analytics Thesis for Life Scientists (3)
The student will pursue a research project that makes a scholarly contribution to existing knowledge and practice in the field of data analytics as it is applied to the Life Sciences. The student will write a formal thesis that documents the conduct, results, and conclusions of his or her project. Upon successful completion of the thesis, the student will submit the paper for review by a thesis committee consisting of faculty in the Biology and Computer Science departments, along with possibly additional experts from industry. The student will make an oral defense of the work to the thesis committee. Prerequisite: 02-510 and a minimum of 24 hours earned in the MS-DMA program.
Prerequisite: 02-512

13-510 Mathematics for Data Scientists (3)
Differentiation and integration of functions; basic matrix operations; linearization; linear and nonlinear optimization techniques; clustering and similarity measures, introduction to probability and statistics, basic computational algorithms. Includes frequent illustration of concepts using mathematical computation tools.

13-511 Concepts of Statistics I (3)
Distribution of random variables, conditional probability and independence, distributions of functions of random variables, limiting distributions.
Prerequisite: 13-510

13-512 Concepts of Statistics II (3)
Point estimation, sufficient statistics, completeness, exponential family, maximum likelihood estimators, statistical hypotheses, beta tests, likelihood ratio tests, noncentral distributions.
Prerequisite: 13-511

70-510 Introduction to Data Mining and Analytics (3)
Overview of the field of data mining and analytics; large-scale file systems and Map-Reduce, measures of similarity, link analysis, frequent item sets, clustering, e-advertising as an application, recommendation systems.

70-511 Statistical Programming (3)
Programming structures and algorithms for large-scale statistical data processing and visualization. Students will use commonly available data analysis software packages to apply concepts and skills to large data sets and will also develop their own code using an object-oriented programming language.
Prerequisite: 13-511

70-517 Pervasive Application Development (3)
Development of web- and mobile-based front ends for large scale data systems, with a focus of portability, accessibility, and intuitiveness.
Prerequisite: 70-511

70-525 Encryption and Authentication (3)
(Double-numbered with 68-525) This course will present key cryptologic terms, concepts, and principles. Traditional cryptographic and cryptanalytic techniques are covered plus perspective on successes and failures in cryptologic history, including both single-key algorithms and double-key algorithms. Issues in network communications, network security, and security throughout the different layers of the OSI model for data communications will also be discussed in depth, as well as the use of cryptologic protocols to provide a variety of security services in a networked environment. Authentication, access control, non-repudiation, data integrity, and confidentiality issues will also be covered, plus key generation, control, distribution, and certification issues.
Prerequisite: 70-510

70-530 Data Visualization (3)
The theory and practice of visualizing large, complicated data sets to clarify areas of emphasis. Human factors best practices will be presented. Programming with advanced visualization frameworks and practices will be demonstrated and used in group programming projects.
Prerequisite: 70-511

70-540 Large-Scale Data Storage Systems (3)
The design and operation of large-scale, cloud-based systems for storing data. Topics include operating system virtualization, distributed network storage, distributed computing, cloud models (IAAS, PAAS, and SAAS), and techniques for securing cloud and virtual systems.
Prerequisite: 70-511

70-550 Machine Learning (3)
Algorithms for enabling artificial systems to learn from experience; supervised and unsupervised learning; clustering, reinforcement learning; control, Students will write programs that demonstrate machine learning techniques.
Prerequisite: 70-511


70-552 Semantic Web (3) Expressing relationships among items in a way that enables automated, distributed analysis in an application-independent way; text mining to derive meaning from semantic networks; algorithms for processing semantic networks; developing a web of things.
Prerequisite: 70-511

70-555 Distributed Computing Systems (3)
Architecture and programming of parallel processing systems; distributed data storage techniques; multithreading and multitasking; redundancy; load balancing and management; distributed system event logging; programming techniques for maximizing the importance of distributed systems.
Prerequisite: 70-511

70-590 Data Mining and Analytics Project for Computer Scientists (3)
The capstone experience for students pursuing the Computer Science concentration in Data Mining and Analytics. Students will develop a solution a real-world problem in data mining and analytics, document their work in a scholarly paper, and present their methodology and results to faculty and peers.
Prerequisite: A minimum of 24 hours earned in the MS-DMA program.

Market Demand

"With data analytics now one of the fastest growing fields in IT, it stands to reason that data scientists are in demand." - Fortune

VentureBeat lists 'Data Scientist' as the #2 fastest growing job in America, with 18.7% projected growth through 2020.

"The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data." - McKinsey Global Institute