Data Analytics: Data Science Pathway

The data science pathway emphasizes a solid foundation in higher mathematics and computational skills.

Students learn to analyze complex data—both structured and unstructured—using advanced techniques in predictive modeling and programming languages like Python and SQL.

This pathway is ideal for students who plan to pursue advanced studies in data analytics and related fields such as statistics, machine learning, and artificial intelligence, at the baccalaureate level and beyond.

This pathway has an articulation agreement to transfer to Northeastern University’s Bachelor’s degree in Analytics program (Bachelor of Science in Analytics | Northeastern University) as well as to transfer to the University of New Hampshire – Manchester campus for completion of a bachelor’s degree in data science.

Career Pathways

While an associate degree alone may not directly lead to a data science position a graduate will be prepared for entry-level technical roles that support data science activities. Job titles may include:

• Data and reporting analyst
• Research assistant
• SQL/database developer
• Digital media analyst

The bachelor’s degree level is the standard entry point into data science roles and qualifies for entry and mid-level data science positions including those in emerging artificial intelligence applications. Job titles may include:

• AI solutions architect
• Robotics engineer
• Data scientist
• Machine learning engineer
• Project data manager
• Data analyst

A master’s degree or higher in data science or a related field typically allows professionals to move into more specialized or senior roles. With a master’s degree, you may take on more responsibility, leadership roles, and contribute to complex projects. Job titles at this level may include:

• Senior/chief data scientist
• Professor/Academic in Data Science/AI
• AI/machine learning researcher

Data Science Pathway

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

1. Identify data sources, types of data, and data structures, including structured and unstructured data.
2. Remediate raw data as appropriate before analysis including cleaning and restructuring data using software tools and programming skills.
3. Collect and combine data from multiple sources using database programming (SQL) and related skills.
4. Use analytical tools to identify patterns and relationships in data sets including time trends, cluster analysis, association analysis, classification, and statistical associations and relations.
5. Apply data analytics to address real-world problems and communicate results to stakeholders
6. Visually communicate patterns and relations in data applying best practices of data visualization.
7. Identify legal and ethical issues in analyzing data and adhere to ethical standards.

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.