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Wentworth Institute of Technology

Adjunct Instructor - Data Analysis & Visualization

Higher Education FacultyPosted on Nov 12
Part-timeBoston, MA, USA$1.5k

About the Job

Requirements

  • A qualifying graduate degree in a social science or humanities field is required, such as psychology, sociology, political science, anthropology, communications, history, economics, or another social science or humanities field; applicants whose degrees are exclusively in computer science or other technical fields will not be considered.
  • The instructor must make foundational concepts accessible to students with no prior experience in the relevant disciplines, data analysis, or data visualization, and should not assume prior programming or advanced quantitative background.
  • The instructor must advance general education learning objectives in critical information literacy, critical thinking, communication, and ethics.
  • The instructor must include Microsoft Excel as an instructional tool and provide students introductory experience organizing and examining data and creating clear visual representations.
  • The instructor must be comfortable guiding beginner students with diverse needs through a manageable semester-long inquiry-based project, with structured practice and feedback at each stage.
  • If the course is offered online, the instructor must demonstrate the ability to deliver high-quality, engaging synchronous online instruction with interaction, structure, feedback, and student support comparable to an in-person course.
  • For online teaching, the instructor must be comfortable teaching software-based skills remotely, including demonstrating tools such as Microsoft Excel, guiding hands-on exercises, troubleshooting common difficulties, and supporting students with varied levels of technical confidence.
  • The instructor should be prepared to teach effectively in an AI-enhanced teaching and learning environment, promoting thoughtful and responsible use of generative AI and emerging tools while maintaining data integrity, critical thinking, ethical information use, appropriate attribution, and independent student learning.
  • The instructor should be prepared to help students critically evaluate AI-generated analyses, visualizations, interpretations, and claims.

Responsibilities

  • Teach a data analysis and visualization course during Summer 2027.
  • Guide students through a semester-long social inquiry project, from defining a meaningful question and considering appropriate evidence through analysis, interpretation, visualization, and communication to an intended audience.
  • Teach data collection and survey design; data scraping, blending, and cleaning; and data analysis and visualization using software applications.
  • Help students evaluate data sources, ask meaningful questions, recognize bias and misleading representations, consider ethical responsibilities, interpret findings in context, and communicate conclusions clearly.
  • Use Excel and, where appropriate, other accessible tools to support inquiry, interpretation, and communication.
  • Provide structured practice and feedback as students identify and evaluate evidence, conduct accessible analysis, interpret findings, create visualizations, and communicate conclusions to an intended audience.

Desired Qualifications

  • Experience using data and visualization in social science or humanities inquiry, including survey design, evaluating and interpreting evidence, recognizing bias and limitations in data, and communicating data-informed findings clearly and ethically.
  • Familiarity with accessible data analysis and visualization tools that support social science and humanities inquiry.
  • Experience with a scaffolded approach to social inquiry and data literacy, including helping beginners formulate questions, evaluate data sources, consider sampling and bias, analyze and interpret evidence, create effective visualizations, and communicate findings clearly and ethically.
  • Experience as a social scientist using data for inquiry, critical thinking, and communication and making quantitative and visual concepts approachable for beginners.
  • Ability to help students critically examine how data are produced, selected, interpreted, visualized, and communicated, including issues of bias, ethics, validity, and potentially misleading representations.

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