Data Analytics Certificate

Nashua Community College’s Data Analytics Certificate equips students with the skills needed to succeed in an AI-driven world.

Students learn to transform raw data into actionable insights through hands-on work with real datasets, mastering data collection, cleaning, analysis, visualization, and communication while applying AI responsibly.

In-Demand Skills You’ll Build

• AI-powered data analysis
• SQL and data querying
• Programming for analytics
• Machine learning and NLP fundamentals
• Data automation and workflow optimization
• Business and decision analytics
• Data visualization and storytelling
• Ethical and responsible AI use

This updated certificate blends classical analytics with modern AI-powered methods through coursework in programming, SQL, business analytics, data communication, machine learning, natural language processing, and business automation.

It can be earned as a stand-alone credential or stacked toward an associate degree, with pathways to bachelor’s-level study.

There are no prerequisites for admission, and students may begin in any term. All courses are offered online, with select on-campus options, allowing completion fully online or in a flexible hybrid format.

Note: Data Analytics webpages feature AI generated images

Upon the completion of the degree in Data Analytics, graduates will be able to:

1.  Differentiate structured, unstructured, and other data types, including their subtypes, origins, and storage formats.
2.  Use spreadsheet tools, SQL, programming environments, and AI copilots to automate data tasks, clean and transform datasets, and develop reproducible workflows.
3.  Apply appropriate analytical methods to interpret data, evaluate patterns and trends, and support informed decision-making in diverse contexts.
4.  Create and evaluate data presentations using visual, textual, and accessible formats to communicate insights clearly, ethically, and inclusively.
5.  Build, interpret, and assess AI-driven models for classification, forecasting, optimization, and natural language processing, ensuring accuracy, reliability, and responsible use.
6.  Integrate data from multiple sources—using SQL, APIs, and AI-supported tools—to support analytic processes.
7.  Assess ethical issues in data analytics, including bias, transparency, accountability, and the role of human oversight in AI-assisted analysis.