Master of Professional Studies in Analytics – Portland

Program Overview

The Master of Professional Studies in Analytics provides students with the knowledge and competencies integral to the role of an analyst. These include statistics, mathematics, analytics systems technology, business intelligence, advanced analytics, business process/management, business analytics agility, communicating with data, and leadership/data governance, policy, and ethics. 

The program prepares students by applying the principles, tools, and methods of analytics to a project within a sponsoring organization. Students successfully assist with the extraction, development, delivery, translation, and implementation of data analysis for tactical and/or strategic decision-making in organizations.

All Roux Institute programs provide content relevant to the urgent and emerging needs of industry in Maine and the rapidly evolving regional, national, and global economy. Opportunities for experiential learning will be concentrated in Portland, the state of Maine, and the Northeast region. Students are encouraged to pursue co-ops and special virtual Experiential Network projects with the institute’s founding corporate partners, a group of leading employers in Maine.

  • Portland

    Location

  • Full-Time
    Part-Time

    Commitment

  • 12-18 months

    Duration of Program

Unique Features

  • Graduate with a portfolio of work samples that demonstrate your range and depth of skill.
  • Be part of a larger network of analytics professionals.
  • This is a STEM-designated graduate degree.
  • The program is delivered through a hybrid model of online and on-site learning.

Program Objectives

  • Build portfolios of real-world projects demonstrating competency with key technologies, visualization and communication techniques, and the ability to translate information into recommended actions.
  • Gain a core analytical skill set upon which to layer more specialized technical skill sets or industry-specific applications.
  • Develop a relationship to industry leaders and peers so that you may leverage your Northeastern education long after your formal education ends.
  • Choose from a host of flexible programming options—all of which share an industry-defined core curriculum and a credit-bearing experiential requirement.
  • Anticipate and contribute to the future direction of data analytics.

Scholarships and aid

$43.9K

Tuition

Estimated Total Tuition

This is an estimate based on the tuition rates for Academic Year 2020-2021 and does not include any fees or other expenses. Some courses and labs have tuition rates that may increase or decrease total tuition. Tuition and fees are subject to revision by the president and Board of Trustees at any time. For more detailed information, please visit Student Financial Services.

Generous scholarships

The Roux Institute is currently offering generous scholarships to meet the financial needs of all students through its Alfond Scholars Initiative. Each award is determined by an individual assessment. And Northeastern alumni receive a Double Husky Scholarship —a tuition discount of 25 percent.

Learn more about the Alfond Scholars Initiative

Corporate tuition benefits

Many employers subsidize education for their employees. Speak with yours about any tuition benefits your company may offer.

Special military scholarships

For military veterans and servicemembers, a limited number of donor-funded scholarships are available even after all other aid has been awarded to help with commuting costs, childcare, and other costs of living.

Learn more about military scholarships

Federal aid

You can apply for federal aid grants and loans through the Free Application for Federal Student Aid, or FAFSA.

Learn about the FAFSA

Admission Requirements

  • Online application
  • Statement of purpose (500–1000 words): Identify your educational goals and expectations of the program. Please be aware that Northeastern University's academic policy on plagiarism applies to your statement of purpose.
  • Professional resumé
  • Unofficial undergraduate transcripts; official transcripts required at the time of admission
  • Two letters of recommendation from individuals who have either academic or professional knowledge of your capabilities such as a faculty member, colleague, or mentor, preferably one from your current employer
  • English language proficiency proof. Students for whom English is not their primary language must submit one of the following:
    • Official associate or bachelor's degree transcript from an accredited college or university in the U.S., stating degree conferral and date
    • TOEFL, IELTS, PTE, Duolingo, or NU Global Exam scores

Admission Dates

Our admissions process operates on a rolling basis; however, we do recommend following the application guidelines below to ensure you can begin during your desired start term.

  • Domestic application complete (first half start: April 12, 2021) April 2, 2021
  • Domestic application complete (second half start: May 24, 2021) May 14, 2021
  • Domestic application complete (first half start: July 12, 2021) June 28, 2021
  • Domestic application complete (second half start: August 9, 2021) July 26, 2021
  • Domestic application complete (first half start: September 20, 2021) September 7, 2021
  • Domestic application complete (second half start: November 1, 2021) October 18, 2021
  • Domestic application complete (first half start: January 10, 2022) December 20, 2021
  • Domestic application complete (second half start: February 22, 2022) February 8, 2022
  • Domestic application complete (first half start: April 11, 2022) March 28, 2022
  • Domestic application complete (second half start: May 23, 2022) May 9, 2022
  • Domestic application complete (first half start: July 11, 2022) June 27, 2022
  • Domestic application complete (second half start: August 8, 2022) July 25, 2022

Program Curriculum

General Requirements

Master of Professional Studies in Analytics General Requirements

Courses and their associated credit hours are listed below.

Note: ITC 6000 is for students without prior educational or professional experience with data and database structures. Students who do not complete ITC 6000 must complete a third elective course to reach 45 quarter hours.

ALY 6000 - Introduction to Analytics3.00
Offers an overview of analytics concepts and practices across a diverse range of industries and organizational contexts. Provides a hands-on introduction to statistics, data management, and the R scripting language. Technical projects based on introductory statistics and the R language offer students an opportunity to understand and apply the theories, practices, and application of analytics to real-world problems. An initial exploration of data sets illustrates how fundamental data analysis can impact decision making at both the strategic and operational level. Students research case studies to examine careers and professional opportunities in both for-profit and nonprofit industry segments.
ALY 6010 - Probability Theory and Introductory Statistics3.00
Introduces statistics for business analytics from an analysis-of-data viewpoint. Topics include frequency distributions; measures of location; mean, median, mode; measures of dispersion, variance, graphic presentation; elementary probability; populations and samples; sampling distributions; and categorical data. Includes a preliminary introduction to regression and correlation. Uses statistical software (for data analysis during analytic project assignments) to provide a hands-on experience to observe how probability and statistics, scripting, and basic data management impact decision making at all levels within a corporation.
ALY 6015 - Intermediate Analytics3.00
Builds on the foundation provided in ALY 6000 and ALY 6010 by exploring at greater depth the tools of data correction and recoding, as well as those of statistics and R. Offers students an opportunity to learn to discern and validate meaningful and statistically significant patterns in data through sound applications of the scientific method. Emphasizes initial mastery of correlation and regression, ANOVA, GLM, and logistic regression. Introduces the more advanced techniques of multivariable regression and nonparametric statistics and sampling. The goal of this course is to offer students an opportunity to master the fundamental skills of data management, analysis, and communication, which are the core data analytical competencies required of today’s analytic professionals.
ALY 6050 - Introduction to Enterprise Analytics3.00
Introduces advanced specific analysis techniques—including forecasting, simulation, linear programming, regressive modeling, and optimization—as well as the Python programming language. The more advanced mathematical, statistical, and presentation functions within the R library packages are heavily utilized. Emphasizes enterprise data analytics, which is the extensive use of data, statistical, and quantitative analysis; exploratory and predictive models; and fact-based decision making to drive business strategies and actions. Course projects embrace marketing, retail, financial, and human resources analytics, as well as familiarize students with general industry practices. Emphasizes end-to-end analytic development skills, including data management, data engineering, analytics modeling, and strategy development. Offers students hands-on opportunities to apply quantitative techniques in strategic business decision making.
ALY 6070 - Communication and Visualization for Data Analytics3.00
Offers an interdisciplinary examination of design concepts and cognitive and communication theories that support effective practices for data visualization and communication. Considers the relationship between information and audience and studies effective techniques in the written, spoken, and visual communication of complex quantitative information. Project-based activities offer students opportunities to apply these techniques in a manner that makes data understandable, compelling, and actionable. Introduces R Shiny, Tableau and R in the lab sessions as the tool for data visualization.
ITC 6000 - Database Management Systems3.00
Covers the use and capabilities of modern database management systems with an emphasis on performance and reliability. After a brief review of conceptual data models and database design, the focus moves to the underlying technology—database engines, storage and indexing, memory use, the relational model, normalization/de-normalization, query processing, and SQL. Also discusses the need for and design of concurrency control, integrity, security, and recovery capabilities.

Experiential Learning Course

ALY 6080 - Integrated Experiential Learning3.00
Offers a practicum in the development and delivery of predictive data analysis for strategic decision making in organizations. Offers students an opportunity to apply the principles and tools of analytics to real-world problems in business organizations and to develop and present analytical insights and recommendations for successful implementation of their capstone project.

Experiential Capstone Course

ALY 6980 - Capstone3.00
Offers an advanced practicum in the development and delivery of predictive data analysis for strategic decision making in organizations. Students apply the principles and tools of analytics to a comprehensive real-world problem or project within a sponsoring organization. Expects students to present analytical insights and recommendations for successful implementation of their capstone project and their individual project proposal.

The remaining quarter hours of the program may be completed by a combination of completing a concentration and additional electives or selecting any courses listed in the concentrations and elective list.

Electives

ALY 6020 - Predictive Analytics3.00
Introduces the end-to-end, data-driven statistical and predictive modeling approach in R with applications and case studies. Includes all the data and modeling steps in a full modeling cycle, including data ETL process, exploratory data analysis, and data cleansing for outlier imputation and data normalization. Commonly applied modeling techniques such as k-nearest neighbors, GLM, random forest, neural networks, and Naive Bayes are heavily utilized and explained using advanced visualization techniques and simplified mathematical derivations to enhance understanding. Predictive analytic modeling steps such as model training, validation, and testing are widely utilized, as are R and Python for data processing, analysis, and modeling.
ALY 6030 - Data Warehousing and SQL3.00
Focuses on the management, mining, and interpretation of patterns in large databases. Offers students an opportunity to learn how organizations construct data warehouses from operational databases, about different data warehouse architectures, how to build a data warehouse, and how to structure databases for efficient data mining. Discusses relational databases and Structured Query Language (SQL) for the fundamentals in data modeling, database management, and SQL queries. Introduces other modern database systems such as NoSQL (non SQL) and column-based databases.
ALY 6040 - Data Mining Applications3.00
Introduces the theories and tools for intensive data analysis methods and data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Introduces data analysis software; student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.
ALY 6050 - Introduction to Enterprise Analytics3.00
Introduces advanced specific analysis techniques—including forecasting, simulation, linear programming, regressive modeling, and optimization—as well as the Python programming language. The more advanced mathematical, statistical, and presentation functions within the R library packages are heavily utilized. Emphasizes enterprise data analytics, which is the extensive use of data, statistical, and quantitative analysis; exploratory and predictive models; and fact-based decision making to drive business strategies and actions. Course projects embrace marketing, retail, financial, and human resources analytics, as well as familiarize students with general industry practices. Emphasizes end-to-end analytic development skills, including data management, data engineering, analytics modeling, and strategy development. Offers students hands-on opportunities to apply quantitative techniques in strategic business decision making.
ALY 6060 - Decision Support and Business Intelligence3.00
Introduces current and emerging business analytical concepts and information technologies to support decision making and business intelligence. Commercial decision support systems in various application areas are introduced and discussed using case studies, including CRM (customer relationship management) for customer management, web analytics applications, sales force management systems, etc. Introduces business intelligence technology and applications, such as OLAP (Online Analytical Processing), OBIEE (Oracle Business Intelligence Enterprise Edition), and IBM Cognos. Offers students an opportunity to gain hands-on experience using business intelligence tools, including Tableau or QlikView.
ALY 6110 - Data Management and Big Data3.00
Designed to provide the student with the core concepts of data collection and management. Topics include systems for collecting data and implications for practice; types of data (textual, quantitative, qualitative, etc.); and storing data with privacy and security issues in mind. Offers students an opportunity to obtain a high-level understanding of big data technologies for data accessibility, efficiency, and security of data management at scale, including big data storage and computing technologies and big data analytics applications. Students create a working system for data acquisition and management using publicly available data sets and evaluate traditional data warehouse platforms as well as cloud-based big data storage and computing technologies. Azure is also introduced and used in the lab sessions.
ALY 6120 - Leadership in Analytics3.00
Covers analytical leadership principles for the structure and dynamics of organizations, combining relevant research to offer students an opportunity to deepen their understanding of effective change in business analytical decision making.
ALY 6130 - Risk Management for Analytics3.00
Seeks to provide a conceptual overview of analytic risk management. Offers students an opportunity to evaluate and analyze financial, technical, and other business risk-assessment and risk-modeling techniques and tools.
ALY 6140 - Analytics Systems Technology0.00
Presents a selection of analytics systems technologies that are deployed in lab sessions throughout the analytics program. A multitude of analytics systems technologies are used for different purposes to describe data numerically and graphically, for data visualization, file systems (HFS) for a large data mart, applications of structured query language, and filtering and transforming to ingest the data through scripting languages. Some of the tools are taught in greater detail (e.g., Python, machine learning), whereas others are introduced more broadly.
ALY 6150 - Healthcare/Pharmaceutical Data and Applications0.00
Introduces a selection of healthcare/pharmaceutical data used for a variety of purposes, and its specific application in data-driven business decision making. Healthcare/Pharmaceutical data is collected as part of Medicare and Medicaid databases and as mandated by the PPACA (Patient and Affordable Care Act) and the PPSA (Physicians Payment Sunshine Act). Data is available in the form of medical records, social networks, outcomes databases, syndicated data reports, epidemiological data, demographic data, analyst information, R&D Pipeline Database, market data, and online journals and newsletters. Organizations, corporations, and companies use these varieties of data for a host of different reasons - to better profile and segment customers, to answer performance questions, and to identify and capture key opportunities.

Concentrations

Choose one from the following:

EAI 6000 - Fundamentals of Artificial Intelligence0.00
Introduces the fundamental problems, theories, and algorithms of the artificial intelligence field. Topics include heuristic search and game trees, knowledge representation using predicate calculus, automated deduction and its applications, problem solving and planning, and an introduction to machine learning. Required course work includes the creation of working programs that solve problems, reason logically, and/or improve their own performance using techniques presented in the course.
EAI 6010 - Applications of Artificial Intelligence0.00
Explores numerous industry applications of AI with emphasis on solving specific needs or problems. Topics include neural networks, natural language processing, and implications of cybersecurity. Artificial Intelligence is actively developing in applications across numerous fields and industries, including finance, healthcare, education, and transportation.
EAI 6020 - AI System Technologies0.00
Presents a selection of systems technologies utilized in AI, including data visualization; file systems for a large data mart; applications of structured query language; and filtering and transforming to ingest data, predictions, etc. Covers mathematics/statistics and computation, machine learning, and privacy requirements.
ALY 6040 - Data Mining Applications3.00
Introduces the theories and tools for intensive data analysis methods and data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Introduces data analysis software; student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.
ALY 6110 - Data Management and Big Data3.00
Designed to provide the student with the core concepts of data collection and management. Topics include systems for collecting data and implications for practice; types of data (textual, quantitative, qualitative, etc.); and storing data with privacy and security issues in mind. Offers students an opportunity to obtain a high-level understanding of big data technologies for data accessibility, efficiency, and security of data management at scale, including big data storage and computing technologies and big data analytics applications. Students create a working system for data acquisition and management using publicly available data sets and evaluate traditional data warehouse platforms as well as cloud-based big data storage and computing technologies. Azure is also introduced and used in the lab sessions.
ALY 6040 - Data Mining Applications3.00
Introduces the theories and tools for intensive data analysis methods and data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Introduces data analysis software; student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.
ALY 6060 - Decision Support and Business Intelligence3.00
Introduces current and emerging business analytical concepts and information technologies to support decision making and business intelligence. Commercial decision support systems in various application areas are introduced and discussed using case studies, including CRM (customer relationship management) for customer management, web analytics applications, sales force management systems, etc. Introduces business intelligence technology and applications, such as OLAP (Online Analytical Processing), OBIEE (Oracle Business Intelligence Enterprise Edition), and IBM Cognos. Offers students an opportunity to gain hands-on experience using business intelligence tools, including Tableau or QlikView.
PJM 6005 - Project Scope Management3.00
Offers insight into how projects are defined, evaluated, and ultimately translated into manageable project requirements and concrete deliverables. By learning how to identify stakeholder needs and convert those needs into viable, measurable project scope documentation, a project manager can successfully manage not only a project’s scope but also make informed recommendations when trade-offs between project scope, cost, and schedule become necessary.
ALY 6120 - Leadership in Analytics3.00
Covers analytical leadership principles for the structure and dynamics of organizations, combining relevant research to offer students an opportunity to deepen their understanding of effective change in business analytical decision making.
ALY 6130 - Risk Management for Analytics3.00
Seeks to provide a conceptual overview of analytic risk management. Offers students an opportunity to evaluate and analyze financial, technical, and other business risk-assessment and risk-modeling techniques and tools.
ALY 6040 - Data Mining Applications3.00
Introduces the theories and tools for intensive data analysis methods and data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Introduces data analysis software; student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.
ALY 6110 - Data Management and Big Data3.00
Designed to provide the student with the core concepts of data collection and management. Topics include systems for collecting data and implications for practice; types of data (textual, quantitative, qualitative, etc.); and storing data with privacy and security issues in mind. Offers students an opportunity to obtain a high-level understanding of big data technologies for data accessibility, efficiency, and security of data management at scale, including big data storage and computing technologies and big data analytics applications. Students create a working system for data acquisition and management using publicly available data sets and evaluate traditional data warehouse platforms as well as cloud-based big data storage and computing technologies. Azure is also introduced and used in the lab sessions.
ALY 6020 - Predictive Analytics3.00
Introduces the end-to-end, data-driven statistical and predictive modeling approach in R with applications and case studies. Includes all the data and modeling steps in a full modeling cycle, including data ETL process, exploratory data analysis, and data cleansing for outlier imputation and data normalization. Commonly applied modeling techniques such as k-nearest neighbors, GLM, random forest, neural networks, and Naive Bayes are heavily utilized and explained using advanced visualization techniques and simplified mathematical derivations to enhance understanding. Predictive analytic modeling steps such as model training, validation, and testing are widely utilized, as are R and Python for data processing, analysis, and modeling.
ALY 6140 - Analytics Systems Technology0.00
Presents a selection of analytics systems technologies that are deployed in lab sessions throughout the analytics program. A multitude of analytics systems technologies are used for different purposes to describe data numerically and graphically, for data visualization, file systems (HFS) for a large data mart, applications of structured query language, and filtering and transforming to ingest the data through scripting languages. Some of the tools are taught in greater detail (e.g., Python, machine learning), whereas others are introduced more broadly.
ALY 6030 - Data Warehousing and SQL3.00
Focuses on the management, mining, and interpretation of patterns in large databases. Offers students an opportunity to learn how organizations construct data warehouses from operational databases, about different data warehouse architectures, how to build a data warehouse, and how to structure databases for efficient data mining. Discusses relational databases and Structured Query Language (SQL) for the fundamentals in data modeling, database management, and SQL queries. Introduces other modern database systems such as NoSQL (non SQL) and column-based databases.

Experiential Learning

Learning integrated with professional experience is a hallmark of Northeastern and the Roux Institute. Students gain a clear understanding of real-world industry needs in Portland, the state of Maine, and the Northeast—and valued workplace skills like communication and teamwork—through assignments at companies and nonprofit organizations. Students can complete a six-week virtual project relevant to their studies through the university’s Experiential Network (XN) of employers, or even for their own company. Or they can apply for four- and six-month, full-time co-op positions. All opportunities enable students to build their resumés, expand their professional networks, and chart a path to in-demand careers.

Contact us to explore your options.

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