A lot has changed over the last century, not least of which is the use of data. The advent of the internet, smartphones, social media and other technologies has led to data’s immense growth. 

Just how much data is out there? A lot. Over 90% of data has been created in the last two years. It’s estimated that 1.145 trillion MB are created each day. Put another way, each human creates 1.7MB of data per second. 

With that kind of growth, organizations everywhere are wondering how they can leverage their data to make better decisions and plans. The result? An overwhelming need for skilled workers who understand their way around data.

If you’d like to become one of those skilled workers and are researching master’s programs, you’ve no doubt come across the terms “data analytics” and “data science” and wonder if they refer to the same thing? The short answer is that while they are related, data analytics and data science refer to different things.

What is Data Analytics?

The term data analytics refers to the science of analyzing raw data in order to make conclusions about that information. An array of tools, techniques, mechanical processes and algorithms are used to analyze data in order to help an organization inform its strategy and optimize its performance.

Data analytics tools and methodologies are used in different industries and in different ways to do things such as budgeting and forecasting, risk management, marketing and sales, and product development.

What is Data Science?

Where data analytics is used to understand datasets and gain insights for optimizing performance, data science is used to build, clean and organize datasets. Data scientists build and use algorithms, statistical models, as well as custom analyses to collect and shape raw data into something that can be more easily understood.

Data scientists know how to use skills such as Machine Learning, Python, R, and Apache Spark to do their work.

The Master’s in Data Analytics at Stonehill

While there are some overlaps, data analytics and data science work with data in different ways. 

If you’re interested in becoming a data analyst, Stonehill offers a Master’s Degree in Data Analytics to provide students with the skills needed to analyze raw data and draw conclusions about that information. 

Courses in the program are delivered fully online or using a hybrid model of in-person and online course meetings. Courses meet over a seven week term.

Courses for the Master's in Data Analytics

Stonehill’s Data Analytics Master’s Degree Program consists of nine courses and one major field project or capstone.

Introduces the key concepts of data analytics and data science as applied to solving data-centered business problems in many industries. Emphasizes principles and methods covering the process from envisioning the problem to applying data science techniques to deploying the results to improve a business and help make decisions. Topics include an introduction to data-analytic thinking; application of data science solutions to business problems; achieving and sustaining competitive advantage with data science. Students will read and analyze data analytics case studies in various industries.

An intermediate statistics course focusing on techniques used in data analytics. Introduces key statistical methods for applying data analytics. Introduces statistical thinking – starting with a question and using data and software tools to form a reasonable conclusion. Covers statistical analysis of both categorical and quantitative data. Most analysis will be performed using SAS software. Topics include statistical distributions, probability density functions, model accuracy analysis, bootstrapping, and sampling techniques.

A hands-on data analytics course for structured data using SAS Enterprise Miner. Covers the skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). Course includes defining a SAS Enterprise Miner project and explore data graphically, modifying data for better analysis results, building, and understanding predictive models such as decision trees and regression models, comparing and explaining complex models, generating, and using score code, applying association and sequence discovery to transaction data. Upon completion, students will have a set of practical data analytics skills and know how to apply these skills in a variety of business environment and with many types of structured data.

Practical survey course covering database and data warehouse fundamentals. Emphasizes SQL (simple and complex queries), the Extract-Transformation-Load (ETL) process, relational versus non-relational databases (and why relational databases can be a problem for analysis), an exploration of different database systems (Oracle, Microsoft SQL Server, etc.), data warehousing concepts, normalization/de-normalization, and cloud data warehousing. Course provides practical skills for database querying and allows provides a foundational knowledge of database concepts so that students can work better with the database administration staff.

A hands-on course emphasizing the importance of data visualization in understanding data. Designed for those who have never used data visualization software before, this course will utilize Microsoft Power BI to prepare students to create reports and dashboards at all levels of an organization. Students will learn exploratory and explanatory data analysis and learn how to ask the right questions about what is needed in a visualization. Students will assess how data and design work together and learn which visualization to use in various situations. Students will learn how to balance the goals of their stakeholders with the needs of their end-users and be able to structure and organize a digital story for maximum impact.

A survey and case study course emphasizing the importance of data privacy and security. We need to share data in organizations, but the more we share it, the more it becomes necessary to protect it. By the end of the course, students will understand the legal, social, and ethical ramifications of data security and privacy as well as the concepts behind data guardianship and custodianship and data permissions. Special attention will be given to industry-specific data privacy laws (HIPAA, FERPA, PCI DSS, etc.).

A special topics course which will explore current major trends in the data analytics landscape. Topics may include natural language processing, fraud prevention, social media analysis, the role of analytics in financial management, artificial intelligence, or unstructured data analysis.

A hand-on data analytics course for structured data using the Python programming language. Covers the skills that are required to explore and prepare data prior to analysis, create several types of predictive models, and perform data clustering. It also covers skills that are required for model assessment and implementation. Models covered include decision trees, regressions, neural networks, K-means, market basket analysis, and others. Upon completion, students will have a set of practical data analytics skills and know how to apply these skills in a variety of business environments and with many types of structured data. 

This course provides students with an introduction to the SAS programming language. It is for students who want to learn how to write SAS programs to access, explore, prepare, and analyze data. The course will also cover some intermediate topics as time allows. Through a series of mini projects, students will gain a basic working knowledge of the SAS programming language. This course also counts toward the professional SAS certification.

This course will provide an in-depth look at Fundamental Data Analytics Concepts – the methods of analytics, analytics versus data science. We will also explore analytics – team roles, team success factors, defining strategy, organizational culture, innovation, engagement, service, learning, data centrism. Data Strategy, including scope and purpose, data collection, standardization, and cleansing, data architecture, virtualization, and integration, will be an integral part of this course. Students will explore data insights and analysis, organization change and patterns, effective change management, and SMART Goals. Students complete a semester-long idea paper.