Master of Science in Data Analytics (Online)

Master of Science in Data Analytics

Alvernia Master of Science in Data Analytics aims to equip students with the essential skills in critical-thinking and technical proficiency for careers in data analytics. Students will be enabled to address intricate challenges and make data-informed decisions. By prioritizing ��ֱ��’s commitment to community engagement and ethical leadership, our graduates are prepared to utilize data-driven insights responsibly and ethically to create positive impacts within their organizations and society at large.

The Master of Science in Data Analytics program will provide a flexible, accessible and affordable program that equips graduates with advanced skills to effectively navigate the complexities of data analysis and make informed decisions that meet the demands of modern businesses and operations.

Estimated Program Length	2 years
Credit Hours	30
Course Length	7 weeks
Transfer Credits Accepted	Up to 6
Cost per Credit	$500

In This Section

College of STEAM

Getting Started

Application Instructions

Along with your completed application accompanied by $50 non-refundable application fee, you’ll need to provide a few supporting materials.

Complete your online application through the Graduate Programs application portal.
Order your official transcript(s). Request the college or university send transcript(s) to gradandadult@alvernia.edu.
Request two professional or academic letters of recommendation.
Submit a Personal Statement reflecting on your goals for the program.
Submit your Professional Resume.

Financing Your Education

There are options to assist in financing your education and making the tuition affordable. Visit our Student Financial Services page for a detailed overview of the process, including a link to complete the FAFSA application, as well as more information about tuition, the tuition payment plan and the tuition deferment program we offer. ��ֱ��’s Title IV code for the FAFSA is 003233. The Office of Student Financial Services is available to help with any questions you may have. Call 610-796-8201 or email sfs@alvernia.edu.

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Program Overview

Curriculum

Two Year Plan: Master of Science in Data Analytics

There are three entry points into the program:

Fall (August)
Spring (January)
Summer (May)

Two 7-week courses are completed each semester. Students complete one course at a time.

Required Core (12 credits)

DS 615: Practical Data Analysis using R
DS 620: Databases for Data Analytics
DS 623: Ethics of Data Analytics
DS 680: Data Analytics Capstone

Electives (18 credits)

DS 501: Statistical Methods of Data Analysis
DS 503: Fundamentals of Data Analytics
DS 505: Principles of Programming
DS 507: Essentials of Data Analytics for Business
DS 510: Data Manipulation
DS 613: Data Visualization using Tableau

Semester 1

DS 501: Statistical Methods of Data Analysis (3 credits)
DS 505: Principles of Programming (3 credits)

Semester 2

DS 503: Fundamentals of Data Analytics (3 credits)
DS 507: Essentials of Data Analytics for Business (3 credits)

Semester 3

DS 613: Data Visualization using Tableau (3 credits)
DS 510: Data Manipulation (3 credits)

Semester 4

DS 615: Practical Data Analysis using R (3 credits)
DS 620: Databases for Data Analytics (3 credits)

Semester 5

DS 623: Ethics of Data Analytics (3 credits)
DS 680: Data Analytics Capstone (3 credits)

Accreditation

Alvernia is an accredited institution and a member of the (MSCHE). MSCHE is an institutional accrediting agency recognized by the U.S. Secretary of Education and the Council for Higher Education Accreditation.

Career Outlook

Alvernia's M.S. in Data Analytics is tailored to prepare individuals for these sought-after careers:

Data Analyst
Data Scientist
Business Intelligence Analyst
Market Research Analyst
Operations Research Analyst
Financial Analyst

According to the U.S. Bureau of Labor Statistics, various roles such as financial analyst, with an average salary of $101,000, market research analyst with an average salary of $71,500, and positions in big data and machine learning, are in high demand. Businesses, both large and small, are actively seeking professionals versed in data analysis and entrepreneurship, capable of leveraging an interdisciplinary approach to unlock the potential of data as a predictive and strategic asset.