Data Science

��91��Ƭ��’s Data Science major focuses on extracting, analyzing, and visualizing valuable knowledge and insights from real data, such as financial, economic, social, geographical, health, and biomedical data. Data science uses advanced techniques and theories from many fields. This minor also provides related courses, including data mining, machine learning, and big data.

A Data Science major provides opportunities and guided experiences through which the student will be introduced to the knowledge and skills essential to pursue careers in industrial, governmental, and professional fields, and attend graduate school in various areas of computer science.

Getting Started

General Notes

A minimum of 123 credits are required for graduation.
Credits earned for COM 100 and MAT 100 do not count toward the 123 credits required for graduation; however, COM 100 students may petition for elective credit.
Where appropriate, courses required for the major can be used to satisfy General Education requirements. However, the credits earned for these courses are applied to either Gen Ed requirements or the major, not both.
Paths of Knowledge coursework may count towards major or minor requirements, but may not fulfill a second Mid-Level Liberal Arts Exploration requirement.
Students are expected to follow the catalog requirements for General Education, the major, and additional requirements.
A minor or second major within the areas listed under Paths of Knowledge automatically fulfills that area of the Gen Ed requirements.

Application Instructions

Through a rolling admission process, prospective undergraduate students — including first-year, transfer, and international students — may apply via the or . To learn more about how to complete your application, please visit our application page.

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. Alvernia’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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College of STEAM

Program Overview

Curriculum: General Education

All Alvernia students must successfully complete a minimum of 123 semester credits to receive a Bachelor of Arts or a Bachelor of Science degree. You’ll need to complete 54-56 liberal arts core credits and complete community service hours.

First Year Curriculum: Enduring Questions

SEARCH Seminar-Enduring Questions or Honor Search-Enduring Questions (3 credits: SRH 101 or HNR 160)
COM 101 Composition & Research: Must earn a C or higher (3 credits)
THE 105 Foundations of Theology (3 credits)
PHI 105 Introduction to Philosophy (3 credits)

Mid-Level Liberal Arts Exploration Credits

Exploring the Natural World (6-8 credits)
- Lab Science (Met in major)
- Math (Met in related area)
Individuals & Communities (6 credits)
- History or Political Science
- PSY 101, HIS, POS, SOC, SSC, or ECON 3 (Met in related area)
Culture & Language (9 credits)
- Communication (Not COM 100 or 101)
- World Language – 2 courses in sequence
Creative Expressions (6 credits)
- Literature (ENG)
- Art, Music, or Theatre (Met with THR 244)

Ethical Leaders and Followers Credits

Theology or Philosophy (200-400 level)
Theology or Philosophy (Ethics/Morality @ 200 level) (Met with PHI 208)

Curriculum: Paths of Knowledge

Paths of Knowledge may count towards minor or Related Requirements, but not Mid-Level Arts Exploration requirements. Choose one path.

PATH 1: Interdisciplinary Study (IS)

Three courses, at least two from Liberal Arts disciplines, not the major, at the 200-400 level from the interdisciplinary minors of Women & Gender Studies, Digital Media, Community & Environmental Sustainability, Community Engagement, Cultural Studies, Leadership Studies or Pre-Law.

PATH 2: Multi-disciplinary Study (MS)

Three courses, at least two from Liberal Arts disciplines, not the major, at the 200-400 level employing multiple disciplinary perspectives to explore the Enduring Questions in one of the following themes: Cultural & Global Studies, Imagination & Creativity, Peace & Conflict, Poverty & Wealth or Sustainability, Science & Technology.

PATH 3: In-depth Disciplinary Study (DS)

Three courses at the 200-400 level in one Liberal Arts discipline other than one’s major, guided by common ideas and methods of inquiry. Students choose from Art, Biology, Chemistry, Communication, Computer Science, Economics, English, History, Mathematics, Music, Philosophy, Political Science, Psychology, Sociology, Theology, Theatre or World Languages. This path may be used to give students a firm foundation in a discipline supporting their chosen major, or to pursue an interest in one of the Liberal Arts disciplines.

Curriculum: Major Requirements

Semester 1 (15 credits)

SRH 101: Search Sem-Enduring Questions or HNR 160: Honor Search-Enduring Questions
THE 105 or PHI 105
CS 115: Intro to Programming
CS 120: Intro to IT
MAT 131: Pre-calculus or Gen Ed if MAT 131 is not needed.
Complete 5 hours of Community Service

Semester 2 (16 credits)

CS 155: Object-Oriented Programming
THE 105 or PHI 105
Gen Ed
MAT 209: Probability & Statistics
MAT 230: Calculus I
Complete 5 hours of Community Service

Semester 3 (15 credits)

CS 210: Computer Organization & Assembly Language
DS 110: Introduction to Data Science
MAT 250: Discrete Mathematics
Gen Ed (Foreign Language suggested)
Gen Ed
Complete 5 hours of Community Service

Semester 4 (16 credits)

CS 220: Data Structures and Algorithms
DS 210: Data Visualization & Methods of Data Science
MAT 231: Calculus II
Gen Ed
Gen Ed (Foreign Language suggested)
Complete 5 hours of Community Service

Semester 5 (16 credits)

CS 310: Operating Systems
CS 320: Algorithm Design and Analysis
DS 310: Data Mining & Machine Learning
Gen Ed (Lab Course Recommended)
Gen Ed
Complete 5 hours of Community Service

Semester 6 (15 credits)

DS Elective
MAT 240: Linear Algebra
Gen Ed (MAT if MAT 131 was not needed)
Gen Ed
Gen Ed
Complete 5 hours of Community Service

Semester 7 (15 credits)

CS 400: Database Systems
DS 410: Advanced Methods of Data Science
SCI 406: Research I Or SCI 480: Internship
Gen Ed
Elective
Complete 5 hours of Community Service

Semester 8 (15 credits)

CS 425: Software Development
CS 420: Computer Science Seminar
SCI 407: Research II Or SCI 480: Internship
Gen Ed
Elective
Complete 5 hours of Community Service

Experiential Learning Opportunities

The Washington Center Experience

Alvernia students have an opportunity to earn college credit by spending a semester or summer in Washington, D.C. where they serve as interns in a congressional office, government agency, major corporation, newspaper or news network, or agencies devoted to legal affairs, international relations, or business and economics. Participants are customarily juniors or seniors who have achieved grade point averages of 2.5 or better and who have the endorsement of the appropriate academic area. In addition to their internship, students select one seminar dealing with the arts and humanities, communication, public policy, the legal system, business, and government. Interested students should see their academic advisor.

Accreditation Statements

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

Career Outlook

According to the U.S. Bureau of Labor Statistics, employment as a data
scientist is projected to grow 35 percent and the median salary is $103,500.
Data science majors develop expertise and capabilities to embark on
careers with Fortune 500 companies, government agencies, banks, or
various local, regional, and national organizations. Potential roles for data
science graduates include:
• Data Scientist
• Machine Learning Engineer
• Statistician
• Business Intelligence Analyst
• Predictive Modeler
• Data Engineer
• Data Architect
• Quantitative Analyst
• Research Scientist
• Big Data Analyst
• Analytics Consultant
• Data Mining Specialist
• Artificial Intelligence Specialist

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