Master of Science
in Applied Analytics

Admission Criteria and Eligibility Information:

Students are eligible for this degree regardless of their undergraduate major. Programming knowledge/experience is not required, but familiarity with statistics is beneficial. It’s a myth that you need to be an expert in math or programming in order to effectively analyze data. Programming is useful for doing data analysis, but not necessary. Nor is it sufficient. Using data to improve decision-making requires actionable insight. This means understanding where analytics can improve decisions, obtaining the appropriate data, understanding what it does/does not measure, shaping it into an appropriate form, building useful predictive or explanatory models, and communicating the insight to multiple audiences: customers, employees, owners, communities, and beyond. Actionable insight also requires using your existing knowledge to ask the tough questions needed to move your organization or business forward. The M.S. in Applied Analytics is designed to provide and sharpen this skill-set for working professionals.

Loras offers scheduling options for either a 1 or 2 year completion plan.

Please refer to the Course Listings/Schedule section of the Registrar’s Office webpage for the in-person weekend dates and times.

MAA-509 Big Data Ecosystem (Summer)
This course examines the data management process from access to data sources through implementation of scalable processes. Big data requires understanding database design, and increasingly involves parallel processing and cloud-based data storage and analysis. Data formats and database architectures are examined. Tools for extracting data from relational, structured, and non-structured databases are explored. Included are issues related to data security and governance. Students will also learn how to evaluate technologies used to implement scalable decision analytic systems. 3 credits.

MAA-515 Ethical and Social Responsibilities of Data Analysis (Summer)
This course consists of two parts. In the first, relatively brief part, we examine the ethical principles and theories that are relevant to resolving any moral issue. In the second part, we apply these principles and theories to the key moral issues in business (with emphasis, where appropriate and relevant, on questions dealing with data/information acquisition, analysis, and application) by studying, discussing, and debating them, principally through a case-study approach. The focus of our attention is on the three basic kinds of moral relationships in business: a) between the firm and the employee; b) between the firm and other economic agents (i.e., customers, competitors); and c) between the firm and various non-business groups (i.e., the environment). 3 credits.

MAA-520 Data Science (Fall)
Analytics is the process of taking data and turning it into new forms of value. The beginning of this process is often referred to as Data Science and the second stage of the process encompasses algorithms and visualization. As an introduction to data science, we proceed to cover practical data analytic skills including accessing and transferring data (ETL — extract, transform, load), applying analytical frameworks or patterns, applying methods from data mining and machine learning, and learning analysis methods for processing text. The course will also provide students an opportunity to do hands-on exercises with Big Data. The emphasis will be on practical usefulness and analytics patterns. 3 credits.

MAA-521: Database Programming (January Term)
This course explores the fundamental concepts of relational databases: how they are designed, accessed, protected, and managed.  The primary focus is on database programming – statements that retrieve, modify, summarize, analyze, and extract data. Storing of queries within the database for repeated use will be covered. 3 credits.

MAA-530 Programming for Analytic Methods (Spring)
Business Analytics is the process of transforming data into business value. The Data Science course explores a variety of analytical methods for building models (predictive or explanatory). This course will focus on programming languages used for accessing, analyzing, and implementing such models. While many software platforms are available to automate various parts of this process, programming languages are commonly used – primarily R and Python at present. This course exposes students to the use of these languages, focusing on their use for accessing and cleaning data sources and implementing models in a production environment. The subsequent course (Big Data Ecosystem) utilizes these languages for an understanding of the entire process of Business Analytics. While some students may develop a proficiency with coding in these programming languages, the purpose of the course is to provide sufficient exposure to the use of these languages for making business decisions regarding choices of software, human resources, and organizational structures necessary for developing Business Analytics efforts. 3 credits

MAA-540 Machine Intelligence (Spring)
Machine intelligence involves the set of technologies that permit computers to learn, including pattern recognition (text, image, and data), classification modeling, recommendation systems, natural language processing, and a variety of applications that increasingly are part of everyday life. Often referred to as “artificial intelligence,” this course goes further to explore techniques (such as recurrent neural networks and deep learning) which automate the ability of computers to recognize and identify patterns and learn from these. These techniques are scalable in ways that provide automatic implementation – ranging from web search algorithms to intelligent agents to self-driving cars. This course will provide hands-on experience building such systems, but with the focus on understanding the implications for business. Students will gain an appreciation for the scope of potential applications, the limits of machine intelligence, ethical aspects of their use, and disruptive tendencies of these technologies. 3 credits.

MAA-550 Data Visualization (Fall)
Data/Information visualization is widely used in a number of industries, including business, engineering, and media disciplines to help people analyze and understand what the data is telling us. The industry has grown exponentially over the last few years, and as a result there are more tools available to help us quickly and efficiently create compelling visualizations. This course provides an overview of the data/information visualization discipline. Using a hands-on approach, readings and lectures will cover various visualization principles and tools. Our labs will focus on practical introductions to tools and frameworks, with plenty of time to explore & utilize additional applications. We will discuss existing visualizations (e.g. what we find in various publications and government data sources) and critique their effectiveness in conveying information. All students are expected to participate in class discussion, complete lab assignments, and create & critique many data visualization examples throughout the session. 3 credits.

MAA-560 Marketing Analytics (Spring)
Marketing remains a branch of business as well as a social science, and is often characterized by the “4 Ps” of product, place, promotion, and price, and has been extended in many contexts to include people, packaging, and positioning. Each of these Ps is a candidate for improvement through the use of analytics. In Marketing Analytics, we consider the analytics of:

1. Pricing, Forecasting Sales
2. Understanding Customer Demand
3. Customer Value
4. Market Segmentation
5. Retailing
6. Advertising
7. Market Research Tools
8. Internet and Social Marketing.

Topics include, but are not limited to, Price Bundling, Willingness to Pay, Profile Conjoint Analysis, Discrete Choice Analysis, Value Templates, Clustering and Collaborative Filtering, Bass Diffusion Models, Market Basket Analysis, Pay-per-Click Advertising, Principal Components Analysis, Measuring Nodes and Links, Network Contagion, and Viral Marketing Models. 3 credits.

MAA-571 Risk Analysis (Fall)
An important part of business planning is identification, analysis, and management of risk. This spreadsheet-based course examines a variety of models geared to addressing business and social needs. Uncertainty is explicitly analyzed through the use of scenarios, simulation, and other techniques. Emphasis is placed on understanding and communicating the important uncertainties associated with any plan, and developing ways to incorporate these into business plans. 3 credits.

MAA-581 Capstone Seminar (Summer)
The goal of this course is to have students complete a data project (generally in groups) of a complex nature. This includes obtaining and cleaning relevant data, conducting appropriate analysis and communications of findings, and planning implementation of organizational processes that utilize the results of the project. Projects may come from students’ work environment, Center for Business Analytics sponsored projects, or other timely data projects that may arise at appropriate times. 3 credits.

Application and Admission

What are the academic requirements for admission into the Master of Science in Applied Analytics and Graduate Certificate in Applied Analytics programs?

A minimum cumulative GPA of at least 2.75 (or 2.9 in last 60 credits) of a completed bachelor’s degree.

How do I apply for admission?
Please see the Application Requirements and Checklist section of this webpage.

What is the application deadline?
Fall enrollment – all materials due by August 1.
Spring enrollment – please contact the Program Director to discuss class availability and your eligibility for a Spring start.

Applications submitted after the deadline will be considered on an individual basis.

Do either the Master of Science or Certificate program require any particular undergraduate degree?
Students are eligible for either program regardless of their undergraduate major. Programming knowledge/experience is not required, but familiarity with statistics is beneficial. It’s a myth that you need to be an expert in math or programming in order to effectively analyze data. Programming is useful for doing data analysis, but not necessary. Nor is it sufficient. Using data to improve decision-making requires actionable insight. This means understanding where analytics can improve decisions, obtaining the appropriate data, understanding what it does/does not measure, shaping it into an appropriate form, building useful predictive or explanatory models, and communicating the insight to multiple audiences: customers, employees, owners, communities, and beyond. Actionable insight also requires using your existing knowledge to ask the tough questions needed to move your organization or business forward. Loras’s graduate analytics programs are designed to provide and sharpen this skill-set for working professionals.

How long will it take to receive an admission decision regarding my application?
Immediately following receipt of all application materials, your file will be reviewed by the Program Director and you will be notified of an admission decision within one week.

How will I be notified of the admission decision?
You will be contacted by email once a decision has been made.

Can I take some courses as a non-degree student?
Individuals who would like to take graduate classes for professional development, continuing education credits, or personal enrichment may take up to 9 graduate credits as a non-degree special student. Special students may not enroll in practicum, clinical, internship courses or other courses without approval from the program director.

In order to enroll in the course(s), students must complete the standard online application (select “Graduate: professional education program” as the Student Type, and “Other Non-Degree Coursework” as the Academic Program), and email unofficial copies of transcripts from each institution attended to the Director of Admission for Graduate and Postbaccalaureate Programs. The transcripts will then be evaluated by the program director to ensure that any necessary pre-requisite course requirements have been met. Non-degree seeking students interested in courses in the Counseling program must meet minimum program admission requirements for the program. Please note that degree-seeking students have registration preference.

After the completion of 9 non-degree credits, a student must then apply for formal admission into the program in order to move forward and take additional courses.

Financial aid is not available to non-degree special students.

How can I receive more information about the Master of Science in Applied Analytics or Certificate in Applied Analytics program?
For more information about the program curriculum, please contact Dr. Rober Keller, Program Director, at robert.keller@loras.edu. For more information about the application and admission process, please contact Megan Henderson, Director of Admission for Graduate and Postbaccalaureate Programs, at: megan.henderson@loras.edu or 563.588.7140.

Curriculum and Class Format

How many credits are required?
A total of 15 credits are needed to meet the course requirements for the Graduate Certificate in Applied Analytics, and a total of 30 credits are required for the Master of Science in Applied Analytics degree.

What is the class format?
Our programs are delivered through a blend of online and classroom delivery, integrating the flexibility of online learning without losing the valuable personal interaction that a classroom provides. Students take either on or two 7-week courses at a time and each class is held just one night per week. The first six weeks are delivered online in a live virtual classroom. During the final week, students come together face-to-face for a full weekend on the beautiful Loras campus in Dubuque. On campus, students will hear from engaging speakers and participate in meaningful group work.

How long does it take to complete the programs?
The Certificate can be completed in 9 months (Sept.-May), and the Master of Science degree can be completed in either one year (Sept.–Sept.) or two years.

Is there a preferred entry point into the programs?
Both programs begin in the Fall semester. Students interested in a Spring start should contact the program director to discuss their eligibility and class availability.

Does either program accept transfer credits?
We do not accept transfer credits into the certificate program, but students may transfer in up to 9 credits into the Master of Science in Applied Analytics degree. Please consult the Graduate Bulletin for details.

What is the time commitment outside of class?
Time commitments vary depending on the course and individual’s specific skill set. As a general rule, students will spend 4 – 6 hours per week outside of classroom instruction on class-related work for each class.

Where are classes held?
The face-to-face weekend classes are held on the beautiful Loras College campus on the 3rd floor of Keane Hall.

When do classes meet?
Each online live class meets one or two evenings per week, typically from 6:30pm – 8:30pm. The face-to-face weekends typically meet on Saturday from 8am-5pm and on Sunday from 9am-5pm. For specific times and dates, see the course schedule prior to each academic semester.

Will there be an orientation prior to the first class?
Yes, students are required to attend the New Graduate Student Orientation prior to beginning coursework so they can become familiar with the faculty, campus, services and their peers.

Is Loras College accredited?
Yes, Loras College is accredited by the Higher Learning Commission of the North Central Association of Colleges and Schools.

If I’m interested in playing a sport at Loras while in graduate school, how many credits do I have to take?
Graduate students must be enrolled in a degree-seeking master’s program to be eligible to participate in NCAA athletics at Loras (please note that the Graduate Certificate in Applied Analytics does not qualify as a degree-seeking master’s program).

Student athletes enrolled in one of our degree-seeking master’s programs need to have full-time status (taking at least 6 credits per term) during the semester(s) they’re practicing or competing in the sport. Students also need to be making satisfactory progress towards their degree (as determined by Loras). If a student wants to participate in athletics while being enrolled less than full-time, they will need to work with their coaches to determine if they are eligible for a waiver.

The Master of Science in Applied Analytics program can be completed in one year while satisfying the required number of credits for full-time status.

Please note that while 6 credits per term is considered full-time for graduate students at Loras, students only need to take at least 3 credits per term in order to be eligible for federal financial aid.

Financial

What are current tuition rates for the Master of Science in Applied Analytics and the Graduate Certificate in Applied Analytics programs?
Visit the Graduate section of our Tuition & Fees page for detailed cost information.

Tuition and Fees are subject to change at any time.

Is financial aid available?
Federal aid is available for the Master of Science in Applied Analytics program, but not for the Graduate Certificate in Applied Analytics. Students who wish to obtain a Federal Direct Unsubsidized Loan must complete the current year FAFSA and take at least 3 credits per term. For more information, please contact our Financial Planning Office at 563-588-7136 or by email at financial.planning@loras.edu.

Master of Science in Applied Analytics Program Cost

Visit the Graduate section of our Tuition & Fees page for detailed cost information.

Employer Reimbursement

Students who wish to utilize tuition reimbursement from their employer must complete Loras’s Employer Tuition Reimbursement Form before the first day of the first class. Students will still be issued regular billing statements from Loras, but will be exempt from any service charges and will be exempt from having to make full payment until after their last class of the semester. Students or employers are welcome to make payments directly to Loras along the way if they would like, but it is not required. If the employer reimbursement criteria are not met and/or reimbursement is denied, the student must bring their account current immediately before any future registration will be permitted. The balance must be paid in full within 30 days of program completion and prior to Loras issuing the student a degree and/or final transcript.

Financial Aid

Degree-seeking graduate students (in a Master’s program) are eligible to receive $20,500 in Federal Direct Unsubsidized Loan funds for each academic year. Students wishing to obtain a loan must complete the current year FAFSA (Free Application for Federal Student Aid) at: www.fafsa.ed.gov (Loras’s school code is 001873), and be taking at least 3 credits per term. The loan will disburse directly to the school to cover the cost of the term. Loras’s academic year runs from Summer-Spring (ie: Summer 2021-Spring 2022).

Loras College does not offer institutional scholarships or federal grants for graduate and professional education students at this time.

Students who have outstanding loans from their undergraduate education may be able to defer payment on those loans while enrolled in a degree-seeking master’s program. Deferment options are generally available to students who are enrolled at least half-time in a graduate program (3 credits or more per semester). If deferment is a requirement for you to be able to afford to enroll in a graduate program, we advise you to connect with our Financial Planning Office or your loan service providers to make sure you know exactly how deferment applies to your previous loans.

Contact us if you have additional financial aid or billing questions:

Financial Planning Office
Financial.planning@loras.edu
563.588.7136

Carrie Jones, Director of Student Accounts
Carrie.jones@loras.edu
563.588.7232

Tuition and Fees are subject to change at any time.