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Master of Science in Data Analytics | On Campus
Master of Science in Data Analytics
With the growth of the digital age businesses and organizations have had access to unprecedented amounts of data. In order to turn this data into a competitive advantage businesses must develop effective methods to analyze and interpret vast amounts of information. Part business management and part data science, Data Analytics is the key to developing methods that unlock the predictive potential of data.
Data Analytics plays an integral role in determining an organization’s overall strategic direction, and demand for this crucial set of skills is growing globally. The McKinsey Global Institute expects that demand for business people with deep analytical skills could outstrip current projections of supply by 50% to 60%. Graduated students can expect to play a greater role in decision making and strategy setting for their current or future organizations, adding significant value: studies show that businesses that understand how to interpret data outperform competitors by up to 20%. New England College’s MS in Data Analytics will provide students with frameworks for critically looking at data, interpreting and visualizing data, and applying that knowledge in real-world applications that will shape how 21st century business challenges are addressed.
Who Should Enroll
The MS in Data Analytics degree is useful for:
- Anyone with a bachelor’s degree, with or without experience, wishing to prepare for the emerging global opportunities in data analytics
- Individuals wishing to move into a role in corporate strategy
- Individuals with a background in statistics who wish to leverage those skills in business management
- 7 start times throughout the year
- 10 classes/40 credits or 10 classes + Internship for 44 credits
- Learn how to effectively leverage data for strategic decision making
- Learn methods to evaluate data from acquisition, cleansing, warehousing and final analyses
- Understand ways to leverage internet presence to harvest big data
- Understand methodologies and tools for collecting data and designing databases
- Proficiency in interpreting collected data using a variety of statistical tools
- Execute real-time analytical methods on living datasets to quickly respond to a customers wants and needs
Internship Track and Curricular Practical Training (CPT/Internships)
The Internship Track allows for the student to overlay the academic and theoretical study of Data Analytics with practical experience in the field. Students have the means to apply academic concepts to solve real world business problems. The Internship is designed for students to better understand the work expectations and individual organizational culture. Students and their Internship organization must be approved by the MS Data Analytics Program Director prior to the start of the internship, and take an Internship course or courses. Internship courses have variable credit and are MG 5901 through MG5904. Internship credits that are earned are applied in addition to the 40 credits that are required for the MS-DA Program, for a maximum of 44 credits.
Students with F-1 visa status who wish to take part in the Internship Track should work closely with their International Student Advisors regarding the use of Curricular Practical Training (CPT). CPT authorization can only be issued after an internship agreement is completed, and this authorization is an important step to be sure that all off-campus employment is authorized and well-documented. Please contact your International Student Advisor with any questions.
Curriculum (40 credits/44 Credits with internships)
- CT 5610 Database Design
- CT 7610 Database Management
- CT 5320 Data Mining for the Intelligent Business
- MG 5500 Data Driven Decision Making
- MG 6340 Applied Business Statistics I
- MG 6940 Applied Business Statistics II
- MG (course number pending) Statistical Modeling for Managers
- CT 5230 Cloud Computing Concepts
- MG (course number pending) Digital and Internet Marketing Analytics
- MG (course number pending) Capstone
- Data Analytics Internship
CT 5610 Database Design
This course introduces database design and creation. Emphasis is on data dictionaries, normalization, data integrity, data modeling, and creation of simple tables, queries, reports, and forms. Students should be able to design and implement normalized database structures by creating database tables, queries, reports, and forms. Students will use MS Access and MS SQL Server and the SQL programming language. They will also work with Visio to create database diagrams.
CT 7610 Database Management
This course is a continuation of Database Design, a course that focused on the design and implementation of relational databases. In this follow-up course, students learn how to manage databases and how to use those databases to solve business problems. The course studies the concurrency issues that can arise when multiple users are attempting to update the same database structure. Advanced SQL techniques such as Triggers, Functions and Stored Procedures are reviewed. Students then use these techniques to perform database maintenance, backup and recovery. Students will learn about Big Data, Data Warehousing and options available for Cloud processing. Finally, the course will review the processes and procedures required to maintain database access in an online environment.
CT 5320 Data Mining for the Intelligent Business
Business Intelligence depends on the quality of processes and structures for data storage, retrieval, and analysis. In this course, students will study the theory of operational database design and implementation, including concepts of normalization, database queries, database application development, text analytics and big data harvesting . The course will then extend to include the concepts of data mining from the perspective of the web environment, with a particular focus on the quality of data. Students will be encouraged to find the patterns in the data and to prepare reports and presentations describing the implications of their findings.
MG 5500 Data Driven Decision Making
This course introduces students to key methods used to extract information from large datasets and apply that data to business problems. Students will learn how to identify quality data using key concepts like classification, data reduction, and model comparison, and interpret that data using tools like decision trees and logistic regressions. In addition, data preparation and visualization techniques are addressed to provide students with skills in visual representation methods.
MG 6340 Applied Business Statistics I
This course presents fundamentals of probability and provides an overview of the statistical tools and methodologies in the context of business strategy and project management. Topics include probabilistic decision making, hypothesis testing, statistical quality control, and regression analysis. This is a case-based course which allows students to apply their knowledge to specific problems. * requires statistical software package
MG 6940 Applied Business Statistics II
Picking up where Applied Business Statistics ends, this course will further explore statistical tools used in strategic decision making like conjoint analysis and multi-dimensional scaling, stochastic control and noise theory. Using “real-world” problems, students will develop frameworks for cause-and-effect logic that supports predictive analysis for developing suitable hypotheses. Emphasis is placed not just on gathering data but also on the interpretation of data and the limitations of each tool. * pre-requisite MG 6340 Applied Business Statistics I
MG (course number pending) Statistical Modeling for Managers
This course trains students to apply statistical tools towards building robust predicative models. This is a hands-on course that focuses on using Excel to build optimization and simulation models with a variety of applications from finance to marketing. Students will become familiar with methods such as Parametric and non-parametric statistical tests, Generalized Linear Modeling techniques, Data-reduction techniques, Recursive and non-recursive models and Neural networks.
CT 5230 Cloud Computing Concepts
This course provides the basic skills required to analyze, design, and implement cloud-based solutions in a multitude of organizational structures. It focuses on the integration of scalable, reliable platforms, utilizing such fundamental concepts as: private vs. public clouds, migration, virtualization, debugging, development and performance metrics, and disaster recovery. Additional tools and topics, such as the use of Amazon Web Servers, are also explored.
MG (course number pending) Digital and Internet Marketing Analytics
This class focuses on how to leverage strategic marketing concepts and tools to grow brand value in a digital environment. Students will learn the fundamentals of web crawling, search engine optimization, social media marketing, and digital marketing analytics in order to process and analyze data and use it to create effective marketing campaigns.
MG (course number pending) Capstone Project
This capstone experience requires students to integrate principles, theories, and methods learned in courses required through their program. Students creatively analyze, synthesize, and evaluate learned knowledge in the project having a professional focus and communicate the results of the project effectively at a professional level.