Data Analytics

The data analytics associates degree program at Nashua Community College prepares students for further study in data analytics and related degrees.  The degree program combines a solid academic background in data analytics along with associated mathematics and computer science courses.

Why Study Here?

The data analytics degree program at Nashua Community College offers a unique opportunity to study data analytics at a two-year college and prepare for further study.  The program is fully transferable to the University of New Hampshire- Manchester for students to complete a bachelor’s degree in data analytics.

Career Options

Data analytics is a flexible career offering many job options using the skillset and can be used in different industries. Careers in data analytics frequently offer work from home and may accommodate nontraditional work schedules and needs of individual workers. Job titles of program graduates may include:

• Data analyst
• Business intelligence analyst
• Administrative/executive assistant
• Market research analyst
• Health data analyst
• Research assistant

The data analytics degree program is open admissions and has no prerequisites for students to begin coursework.  Students are required to have a computer meeting the technical specifications necessary to run analytical software. Most software packages used in the program are open source, provided by the college or available at nominal cost to students.  When possible, freely available open educational resources are utilized in coursework thus minimizing student cost.

Upon the completion of the degree in Data Analytics, graduates will be able to:Understand the data science life cycle and how it applies across a variety of industries.

1. Efficiently collect and organize data from a variety of sources.
2. Effectively apply statistical tools to identify patterns and relationships in large data sets.
3. Effectively use data mining techniques to make recommendations to address real world problems.
4. Effectively communicate patterns and relationships using a variety of data visualization techniques.