What Is the Difference Between Data Analytics and Data Science?
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 in Data Analytics degree program to provide students with the skills needed to analyze raw data and draw conclusions about that information.
Stonehill’s program is based on a executive (low-residency) format. Each full-time cohort begins the program together in the fall. Classes occur over a full calendar year, with graduation taking place the following August. Students may also complete the program part time and take up to courses each semester.
Courses in the program are delivered using a hybrid model of in-person and online coursework. It combines online with in-person instruction that brings students together on campus about one weekend a month. Students also attend a three-day intensive residency at the start of each semester.
Courses for the Master's in Data Analytics
Stonehill’s Data Analytics Master’s program consists of eight courses and one major field project or capstone.
This course introduces the key concepts of data analytics and data science as applied to solving data-centered business problems in many industries. It 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:
- Intro to data-analytic thinking
- Application of data science solutions to business problems
- Very high-level data mining techniques
- Intro to the SAS software suite
- Achieving and sustaining competitive advantage with data analytics.
This is an intermediate statistics course that builds off of the prerequisite statistics course all students in the program must have. It introduces key statistical methods for applying data analytics as well as statistical thinking – starting with an interesting question and using data and software tools to form a reasonable conclusion. The course also covers statistical analysis of both categorical and quantitative data. Most analysis will be performed in SAS Studio, which further integrates SAS into the curriculum.
This is a hands-on data analytics course for structured data that covers the skills 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).
Topics include:
- Defining a SAS Enterprise Miner project and exploring 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 environments and with many types of structured data.
This is a practical survey course covering database and data warehouse fundamentals. The course emphasizes:
- SQL (simple and complex queries)
- Extract-Transformation-Load (ETL) process
- Relational versus non-relational databases (and why relational databases can be a problem for analysis)
- Exploration of different database systems (Oracle, Microsoft SQL Server, etc.)
- Data warehousing concepts
- Normalization/denormalization
- Cloud data warehousing
Course provides practical skills for database querying and provides a foundational knowledge of database concepts so that students can work better with the database administration staff.
A hands-on course, Visual and Digital Storytelling emphasizes the importance of data visualization in understanding data. Designed for those who have never used data visualization software before, this course will utilize either Tableau, Microsoft Power BI, or SAS Visual Analytics 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. They 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.
This course covers the impact of big data on business and what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. No previous programming experience is required and all code will be provided to students. Students learn software basics in Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, students will experience how one can perform predictive modeling and leverage graph analytics to model problems. By the end of the course students will be able to perform basic big data analysis on a large provided data set.
This is 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 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 is given to industry-specific data privacy laws (HIPAA, FERPA, PCI DSS, etc.).
This is a special topics course that explores current major trends in the data analytics landscape. Topics may include:
- Natural language processing
- Fraud prevention
- Social media analysis
- Role of analytics in financial management
- Artificial intelligence.
This is the final course in the program. It requires students to prepare and present a comprehensive data analysis project in collaboration with their own organization or a sponsor organization. Students choose a faculty advisor and an external advisor in their organization. The final deliverable is a major paper and presentation.