M.S. in Data Analytics

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Translate chaotic data sets into competitive advantage with a master’s in data analytics

With on-going expansion of modern technology including smart devices, social networking, online shopping and more, the amount of data captured is growing exponentially. While the collection and storage of these large amounts of data has become easier and inexpensive, the demand for individuals with the skills capable of managing, analyzing and mining this information is surging. The M.S. in Data Analytics equips you with the statistical skills necessary to conduct descriptive and predictive analytics; the programming skills to create and apply computer algorithms; the tools and platforms to visualize and mine big data, and the communication skills necessary to effectively convey findings and provide strategies and best practices within your organization.

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Finish in 19 Months

Earn your degree and prepare for advancement faster.

100% Online Coursework

Balance earning your degree with other work-life commitments.

SAS Badge

Distinguish yourself as a top-notch talent with a SAS Tier 2 Academic Specialization in Data Analytics.

Hands-On Learning

Build your skills using industry-leading tools and programming and modeling techniques.

No GMAT or GRE

Bypass expensive, time-consuming tests with a 2.75+ GPA.

Learn from the Best

Benefit from the experience of your instructors – seasoned, in-field practitioners.

M.S. in Data Analytics Degree Overview

Earn a degree aligned with industry demand

Organizations from all sectors of the economy are seeking individuals with programming, analytics and communication skills for the purpose of sorting immense data sets to provide a competitive edge or a predictive tool that will help differentiate themselves in crowded markets. Demand for data analytics professionals are also expected to continue to grow by 30%, a rate that’s faster than average.* In addition, a survey of job postings nationwide shows that data analytics jobs take longer to fill compared to other positions, indicating that demand is high and supply is low for qualified individuals to fill these positions.

Franklin’s M.S. in Data Analytics enables you to take advantage of the rising demand for analytics skills and effectively prepares you for the analytics-related jobs of today and tomorrow. The robust curriculum includes coursework in statistics, programming, data management, data visualization, data mining, machine learning and advanced analytics.

Build technical skills with hands-on exposure to industry-standard tools

As a student in the M.S. in Data Analytics, you will be exposed to cutting-edge technology and techniques to address the most common challenges within the discipline. You’ll explore big data technologies including Hadoop, MapReduce, Data Warehouse, SQL, No SQL and In-memory Databases.

You’ll use Python and R to learn data processing algorithms, while other hands-on projects include exercises and assignments in Tableau, SAS Enterprise Miner, SAS Text Analytics, and SAS Visual Analytics. Our partnership with SAS and its heavy integration into the coursework helps you build proficiency that translates into a competitive advantage in the job market. As a graduate of the M.S. in Data Analytics program, you will also receive a SAS Tier 2 Academic Specialization in Data Analytics along with your master’s degree to recognize your achievement with SAS technologies.

Earn a high-quality, comprehensive master’s in data analytics 100% online

At Franklin, you’ll get the convenience and flexibility of a quality online education, expert instructors who have relevant and real-world experience, and strong student support from dedicated faculty, tutors and advisors.

As you progress through the program, you’ll learn to manage, visualize and analyze complex data sets; apply a number of analytics methods to solve business problems and effectively communicate your results through a combination of interactive and relevant coursework. The capstone project gives you an opportunity to integrate and synthesize the skills and knowledge you gained throughout the program.

*Source information provided by Economic Modeling Specialists International (EMSI)

Data Analytics Courses & Curriculum

32 Semester Hours

Course Sequence (32 hours)

MATH 601 - Introduction to Analytics (4)

This course provides an introductory overview of methods, concepts and current practices in the growing field of Data Analytics. Topics to be covered include data collection, analysis and visualization as well as statistical inference methods for informed decision-making. Students will explore these topics with current statistical software. Some emphasis will also be given to ethical principles of data analytics.

COMP 630 - Issues in Database Management (4)

This course focuses on the fundamental design considerations in designing a database. Specific topics include performance analysis of design alternatives, system configuration and the administration of a popular database system. The course also offers an in-depth analysis of the algorithms and machine organizations of database systems.

DATA 605 - Data Visualization & Reporting (4)

This course focuses on collecting, preparing, and analyzing data to create visualizations, dashboards, and stories that can be used to communicate critical business insights. Students will learn how to structure and streamline data analysis projects and highlight their implications efficiently using the most popular visualization tools used by businesses today.

DATA 610 - Big Data Analytics (4)

This course examines the issues in management and analytical analysis of massive datasets, and unstructured data, including data warehousing from an enterprise perspective. Students will learn the concepts and techniques for managing the design, development, security and maintenance of enterprise information.

DATA 611 - Applied Machine Learning (4)

This course explores two main areas of machine learning: supervised and unsupervised. Topics include linear and logistic regression, probabilistic inference, Support Vector Machines, Artificial Neural Networks, clustering, and dimensionality reduction, and programming.

DATA 612 - Data Mining (4)

This course explores data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods.

DATA 621 - Advanced Analytics (4)

This course examines the data analysis process with the emphasis of quantitative and qualitative findings from data. Students will develop skills in data analytics methods and predictive analytics that will allow them to develop algorithmic methods and use them along with popular industry software for data-driven solutions.

DATA 695 - Capstone (4)

The purpose of this capstone course in Data Analytics is to assess students' ability to synthesize and integrate the knowledge and skills they have developed throughout their coursework. The course provides students with the opportunity to demonstrate competency on the key domains of data analytics through a comprehensive project including problem scoping, data preparation and analysis, and a model development.

Microcredentials Align with Job Essentials

In today’s dynamic work environments, adaptive professionals thrive. A microcredential - either as a stand-alone course or integrated into your degree program - is a short, skill-specific recognition that enables you to demonstrate your competency in a distinct area. Like Franklin’s degree programs, microcredentials are aligned with market and industry demand to ensure what you learn can be put to use right away. Microcredentials are easily shared via digital badges and can be stacked to create a unique portfolio of in-demand skills.

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Tier 2 SAS Academic Specialization in Data Analytics

Earn a badge that designates you as a skilled analytics professional

As part of your Franklin master’s degree, you’ll also earn a SAS Tier 2 Academic Specialization in Data Analytics. According to EMSI labor market analytics, nearly 220,000 job postings listed SAS as a desired skill in 2019. This digital badge, issued jointly by SAS and Franklin University, recognizes your ability to leverage SAS analytic tools for data preparation, advanced analytics and visualization. The badge is awarded after your successful completion of three master’s-level courses: MATH 601 (Introduction to Analytics), DATA 605 (Data Visualization and Reporting) and DATA 610 (Big Data Analytics). The knowledge gained through these courses provides you with valuable insight that is sought by SAS customers at more than 65,000 sites in more than 135 countries, including 90 of the top 100 Fortune 500 companies.

Master's in Data Analytics Program Details

Tuition Costs

2020 - 2021 Tuition	Cost Per Credit
Standard tuition per credit hour	$670
Current service members	$536
Graduate Prerequisite Courses (500 level)	$398

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Program Learning Outcomes

A learning outcome map functions as a roadmap to help guide students' progress through their program of study. It shows where each outcome is Introduced (I), Reinforced (R), and Assessed (A) within the program's curriculum.
	1	2	3	4	5
MATH 601	I	I	I	I
COMP 630	R		I		I
DATA 605	A		A	I
DATA 610	R		R	R	R
DATA 611	R	R
DATA 612	R	R
DATA 621		R	R
DATA 695	A	A	A	A	A

Degree Requirements

To be awarded a graduate degree, students must:

Successfully complete all required curriculum courses.
Maintain a minimum cumulative grade point average (GPA) of 3.00.
Complete the residency requirement:
1. Master of Science – Data Analytics students must earn at least 20 of the 32 required credits at Franklin University – in any modality (on-site, hybrid or online).
Complete the payment of all requisite tuition and fees;
Not to be under disciplinary dismissal due to academic dishonesty or violation of Student Code of Conduct.

Transfer credit awarded based on experiential learning shall not count toward the residence requirement at Franklin University.

Admission Requirements

The admission process reflects Franklin University’s efforts to clearly identify the performance standards that can help predict student success in graduate level study. The selection criterion for Franklin’s graduate programs, as determined by faculty, emphasizes academic ability, contributory work experience, and personal qualities and characteristics.

Requirements for admission include having earned a bachelor's degree from a regionally accredited institution. The bachelor's degree final cumulative GPA must be at least a 2.75 on a 4.0 scale, otherwise a graduate entrance exam may be required. The candidate’s work history, references, and other personal qualities and characteristics will be considered as well if the GPA requirement is not met.

English Language Testing & TOEFL IELTS

Prospective students must demonstrate English Language Proficiency. The requirement is met through any of the following:

The applicant is a citizen of a country where English is the official language.*
The applicant has received a bachelor’s degree (or higher) from an institution located in an English-speaking country in which the courses were taught in English.*
The applicant has earned appropriate scores on language proficiency exams taken within the last two years, as listed in the Academic Catalog.

*For more information about English proficiency, including a list of English-speaking nations, please visit our International Students section.

Program Advisory Board

The University employs a team approach to planning, developing and maintaining its academic curriculum. An essential element of this process – and a key to the institution’s quality assurance practices – is the Program Advisory Board (and the associated Alumni Advisory Board). A diverse array of business and industry leaders make up these discipline-specific boards that provide guidance on theory-to-practice ideas, global business perspectives, and emerging topics in the field. Each academic year, Program Advisory Boards meet with Division Chairs and faculty for lively and engaged conversations, thus bringing members’ substantial professional experience and expertise into the classroom. In addition, some Division Chairs elect to engage Program Advisory Board members in the assessment of academic program outcomes.

Data Analytics Program Advisory Board
Name	Organization	Title
Angela Stabler	SAS Institute	Analytical Training Consultant
Erin J. Hamm	Nationwide Insurance	Specialist, Data and Analytics
Gregor MacDonald	Nationwide Insurance	Executive Director, Mortgage CCAR and Retail Capital Modeling
Karl T. Leckrone	Scotts MiraceGro	Data Scientist
Rehgan Avon	Open Data Group	Solutions Architect
Paul Hendricks	NVIDIA	Senior Solutions Architect, Machine Learning and Deep Learning
David M. Sebert	Nationwide Insurance	Director, Business Process Optimization
Christopher Wetherill	State Auto Insurance	Lead Data Scientist

Career Opportunities

Data Scientist

Data scientists collect data and prepare it for analysis by building statistical and predictive models.

Information Security Analyst

Information security analysts plan and implement security features to protect an organization’s computer networks and systems.

Business Intelligence Analyst

Business intelligence analysts use big and small data to predict trends that enable companies to increase profits and efficiency.

Data Warehousing Administrator

Data warehouse administrators manage the daily activities of a data warehouse including archiving, tracking data changes, migration and monitoring data systems.

Operations Research Analyst

Operations research specialists analyze data and advise organizations on how to best manage resources without sacrificing quality.

Employment Outlook

Data Analytics Knowledge & Skillsets

Gain in-demand skills sought by employers with curriculum that teaches you:

Data Analysis

Interpret data and recommend appropriate course of action to improve effectiveness
Analyze information to produce analytically driven inferences and insights to generate actionable recommendations
Interpret results of analysis; present recommendations, including alternatives and implications
Conduct benchmarking, trend analysis and other input to drive continuous improvement and elevate performance
Analyze requirements, procedures and problems to automate processing or to improve existing business systems

Database Design & Development

Design, model, and build data structures and data mapping routines to support data and reporting requirements
Administer and monitor systems in the purview of information security
Update, review and maintain operational database systems
Examine the techniques and issues involved in securing databases
Predict new trends in database management
Investigate techniques for measuring the effectiveness of database design
Experiment with techniques for the optimization of database queries

Business & Management

Develop technical solutions to business problems and measure outcomes
Analyze and evaluate management systems for improvement
Analyze financial statements for trends and performance against expectations
Analyze business conditions, including pricing, cost and budget consequent to engage in strategic planning and decision
Analyze the external business environment to determine strengths, weaknesses, opportunities and threats
Write technically proficient quantitative surveys, across a wide array of topic areas and with varying degrees of complexity

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Data Analytics Frequently Asked Questions

What is an M.S. in Data Analytics degree?

The M.S. in Data Analytics is focused on the science of data – coding, modeling and analytic tools – and data operations including advanced analysis. Data analytics professionals use mathematical and statistical methods and techniques along with programming to design and build data models.

What can you do with M.S. in Data Analytics degree?

The M.S. Data Analytics equips you with the statistical skills necessary to conduct descriptive and predictive analytics; the programming skills to create and apply computer algorithms; the tools and platforms to visualize and mine big data, and the communication skills necessary to effectively convey findings and provide strategies and best practices within your organization.

Why get an M.S. in Data Analytics degree?

What makes Franklin’s M.S. in Data Analytics degree unique?

Coursework for Franklin’s 19-month accelerated M.S. in Data Analytics program can be completed 100% online. Through our partnership with SAS, a global leader in analytics, you’ll also earn a SAS Tier 2 Academic Specialization in Data Analytics, which will complement your master’s degree and underscore your analytic knowledge and abilities.

How long does it take to get M.S. in Data Analytics degree?

Franklin's M.S. in Data Analytics can be completed in 19 months, 100% online.

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Franklin University Founded in 1902, Franklin is an accredited nonprofit university offering flexible college degrees online and at locations in Ohio and the Midwest.

Franklin University
201 S Grant Ave.
Columbus, OH 43215

Local: 614.797.4700
Toll Free: 1.877.341.6300
admissions@franklin.edu

Franklin University is accredited by the Higher Learning Commission (1.800.621.7440) and authorized by the Ohio Department of Higher Education.

This institution is authorized by the Indiana Board for Proprietary Education, 101 W. Ohio St., Suite 670, Indianapolis, IN 46204-1984.

Franklin University is committed to being an inclusive community free from all forms of discrimination and harassment.

Translate chaotic data sets into competitive advantage with a master’s in data analytics

Take the next step toward your degree!

Finish in 19 Months

100% Online Coursework

SAS Badge

Hands-On Learning

No GMAT or GRE

Learn from the Best

Earn a degree aligned with industry demand

Build technical skills with hands-on exposure to industry-standard tools

Earn a high-quality, comprehensive master’s in data analytics 100% online

Earn a badge that designates you as a skilled analytics professional

Know the Total Cost of Your Master’s from Day One

Recommended Application Deadline

Program Prerequisites

Entrance Exam

English Language Testing & TOEFL IELTS

Data Analytics Program Advisory Board

Free Master's Toolkit

Data Scientist

Information Security Analyst

Business Intelligence Analyst

Data Warehousing Administrator

Operations Research Analyst

From 2018-2029 jobs in Data Analytics are expected to increase by 34%

Source information provided by Economic Modeling Specialists International (EMSI).

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