Master of Science
in Applied Analytics

Personalized Attention.
Professional Growth.
Community Impact.

How do you apply the latest methods and technologies to help your company gather, store, report, and analyze important data?

Data-informed decision-making has permeated virtually all aspects of life, from health care to social services, business, politics, manufacturing, retailing, real estate, finance, insurance, and sports. The Master of Science in Applied Analytics and Graduate Certificate in Applied Analytics are designed to give working professionals the technical skills and management tools needed to make data-informed decisions.

Students will gain insights into discovering meaningful patterns in data and how predictive modeling can be used to improve decision-making. Loras College's Graduate Analytics programs will position your employees to make better decisions today and facilitate their increasing use of analytics in the future.

FLEXIBLE & CONVENIENT
Both the Master of Science in Applied Analytics and the Graduate Certificate in Applied Analytics 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.

Depending on the program, students take either one or two courses at a time (comprised of a 14-week fully online course and/or a 7-week hybrid course). Each class is held just one night per week, allowing students to continue working while in school.

In the 7-week hybrid courses, the first six weeks are delivered online in a live virtual classroom, and in the last 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.

Applied Analytics Degree Comparison chart.pdf

ARE YOU READY?
Now is the time to invest, whether you’re looking to build your individual knowledge and skill-set or manage a business/organization and aim to motivate and inspire your current workforce through tuition reimbursement. You don’t have to be a large company to analyze, utilize, and benefit from big data. Our program is designed to immediately improve an organization’s use of data, and establish a baseline for future evolution.

Straight Talk from a Duhawk

Companies of all sizes are leveraging the power of intelligence from data, adding new value to their organizations. The applied analytics and machine learning offerings at Loras continue to raise the bar each year. Loras offers exceptional course content from professors with relevant industry experience. The cohort format enables strong networking relationships, while single course focus and less than one-year timeline are ideal for working professionals.

Joni Wallace, Associate Director of Artificial Intelligence, Collins Aerospace

Straight Talk from a Duhawk

"Offerings at Loras continue to raise the bar each year. Single course focus and less than one-year timeline are ideal for working professionals."

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Straight Talk From an Employer

The utilization of data analytics helps us monitor the vital signs of the organization, to ensure it is operating healthy. In addition, the internal transparency and visibility of the performance of the organization helps with problem-solving, creates higher engagement levels among the employees, and has increased retention levels of our employees.

Brad Pinchuk, President and CEO,
Hirschbach

Straight Talk From an Employer

"Has increased retention levels of our employees"

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Application Requirements & Checklist
Course Requirements & Schedule

FALL 2020
Risk Analysis (MAA 571)
Wednesday Nights online, Aug 26 – Dec 9
Data Visualization* (MAA 550)
Tuesday Nights online + 1 in-person weekend, Aug 22 – Oct 6
Data Science* (MAA 520)
Tuesday Nights online + 1 in-person weekend, Oct 13 – Dec 6

JANUARY TERM 2021
Database Programming (MAA 521)
Tuesday & Wednesday Nights in January online + 1 in-person weekend

SPRING 2021
Programming for Analytics Methods (MAA 530)
Wednesday Nights online, Feb 3 – May 12
Machine Intelligence* (MAA 540)
Tuesday Nights online + 1 in-person weekend, Feb 2 – March 21
Marketing Analytics* (MAA 560)
Tuesday Nights online + 1 in-person weekend, March 23 – May 16

SUMMER 2021
Capstone Seminar (MAA 581)
Wednesday Nights online May 19 – Aug 18
Big Data Ecosystem (MAA 509)
Tuesday Nights online + 1 in-person weekend, May 18 – July 11
Ethical/Social Responsibility of Data Analysis (MAA 515)
Tuesday Nights online + 1 in-person weekend, July 13 – Aug 29

*Indicates the 4 courses required for the Graduate Certificate in Applied Analytics. The 5th course is an elective of your choosing. Please note that depending on which elective is chosen, the Certificate completion timeline may be longer than 9 months.

Course Descriptions

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.

Frequently Asked Questions

Application and Admission FAQ

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.

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 James Padilla, J.D., Francis J. Noonan School of Business Dean, at: james.padilla@loras.edu or 563.588.7405. 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 FAQ

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 one year (Sept.–Sept.).

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.

Financial FAQ

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.

Tuition & Fees

Program Cost

Employer Reimbursement

  • The Employer Reimbursement Form must be completed.
  • Students are responsible for providing the course information, final grades, and billing information to their employer to obtain the reimbursement.
  • If the employer reimbursement criteria is not met and/or reimbursement is denied, the student must bring their account current before any future registration will be permitted.

Federal Loan Options (for Master of Science program only)

  • Students who wish to obtain a Federal Direct Unsubsidized Loan must complete the current year FAFSA and take at least 3 credits per term.
  • The loan will disburse directly to the school to cover the cost of the term.
  • Graduate students are eligible for up to $20,500 in loan funds each academic year.
  • The Loras College academic year runs from Summer-Spring (i.e. Summer 2020-Spring 2021).

Feel free to contact us if you have Billing Questions

Tuition and Fees are subject to change at any time.

MS in Applied Analytics Faculty

James Padilla, J.D.
Francis J. Noonan School of Business Dean
563.588.7405 | james.padilla@loras.edu
Full Profile

Dale Lehman, Ph.D.
Center for Business Analytics Director
Professor of Business
563.588.7725 | Dale.Lehman@loras.edu
Full Profile

Robert Keller, Ph.D.
Division of Mathematics, Engineering, & Computer Science Chair
Professor of Mathematics
563.588.7015 | Robert.Keller@loras.edu
Full Profile

Roman Ciapalo, Ph.D.
Professor of Philosophy
563.588.7434 | Roman.Ciapalo@loras.edu
Full Profile

Shikhar Acharya
Assistant Professor of Business Analytics
563.588.7784 | shikhar.acharya@loras.edu
Full Profile

Questions? Let’s get in touch.

Loras College Graduate Admissions
Megan Henderson

megan.henderson@loras.edu
563.588.7140

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