Adjunct Instructor, Data Analysis & Visualization

About this position

Job Description Summer 2026 Teaching Opportunity: Data Analysis & Visualization The School of Sciences and Humanities at Wentworth Institute of Technology is seeking an instructor to teach a data analysis and visualization course during Summer 2026. Summer Semester: May 5 - August 11, 2026 Meeting Time: Evenings Preferred Modality: In person Also Considered: Online synchronous instruction We would particularly welcome instructors with experience in data analysis, data visualization, survey design, data cleaning, data scraping, and communicating data-driven insights. Experience with relevant industry-standard data analysis and visualization software is desirable. Interested instructors should be comfortable guiding students through a semester-long applied data project that moves from defining a research question and collecting data through analysis, visualization, and presentation. Course Description: Regardless of profession, being able to gather, analyze, and visualize high quality data to gain insight into an issue and successfully articulate your findings to key decision-makers makes you a more valuable asset. This course takes a holistic approach to data-driven decision-making by introducing the theory, practice, and ethics of effective project and audience definition: data collection and survey design; data scraping, blending and cleaning; data analysis and visualization using a variety of software applications. You will leave this course with improved abilities to collect, analyze, and visualize data - and in a way that has integrity and validity and that successfully engages your audience. Over the semester, each student will conduct a data analysis project from start to finish, including: Articulating the project audience and goals; developing and implementing a data collection and/or scraping process; combining, pivoting, cleaning, and analyzing the data; and producing visualizations that efficiently convey insights gleaned from the data while enabling the end-user to also interact with the data and make their own discoveries. Consequently, the principal outcome of this course for the diligent student will be the ability in the future to conduct similarly rigorous and successful data analysis projects on their own from start to finish, and to explain why a rigorous approach to every step in the data collection, analysis, and presentation (DCAP) process matters. More specifically... Student Learning Outcomes: At the completion of this course, the diligent student will be able to...Create data collection instruments and processes that facilitate analysis and minimize both sampling and non-sampling error; Articulate the risks, benefits, and ethical implications of scraping and cleaning data; Use industry-standard software to clean datasets; Use industry-standard software to combine, pivot, analyze, and visualize datasets; Design data visualizations and infographics for maximum communication efficiency and clarity; Identify data visualizations which are misleading or lack integrity due faulty analysis or deceptive visualization design Required Qualifications and Teaching Approach: We seek a social scientist or humanities scholar with a master's degree or doctorate (MS, MA, MEd, PhD, or EdD) in psychology, sociology, political science, anthropology, communications, history, economics, or another social science or humanities field. A qualifying graduate degree in a social science or humanities field is required. Applicants whose degrees are exclusively in computer science or other technical fields will not be considered. This is an introductory general education course for students with no prior experience in the relevant disciplines, data analysis, or data visualization. The instructor must be able to make foundational concepts accessible, introduce software tools step by step, and support students with varied academic backgrounds and levels of confidence. Instruction must advance general education learning objectives in critical information literacy, critical thinking, communication, and ethics. Students should learn to evaluate data sources, ask meaningful questions, recognize bias and misleading representations, consider ethical responsibilities, and communicate findings clearly. Because of employer feedback, Microsoft Excel should be among the instructional tools, helping students develop practical skills relevant to employer expectations. The instructor must be able to teach beginners to organize and clean data, use basic formulas, sort and filter datasets, create pivot tables, and produce clear charts. Other accessible analysis and visualization tools may supplement Excel. We welcome experience with a scaffolded approach to data analysis, data visualization, survey design, data cleaning, data scraping, and communicating findings, along with the ability to select tools and activities appropriate for beginners. The instructor should be comfortable guiding beginner students with diverse needs through a manageable, semester-long applied project, with structured practice and feedback at each stage: defining a question, collecting or locating data, conducting basic analysis, creating visualizations, and presenting findings. Expectations for Online Instruction: If the course is offered online, the instructor must have demonstrated ability to deliver high-quality, engaging synchronous online instruction. The online course should provide a level of interaction, structure, feedback, and student support comparable to an in-person course. The instructor should be comfortable teaching software-based skills remotely, including demonstrating tools such as Microsoft Excel, guiding students through hands-on exercises, troubleshooting common difficulties, and supporting students with varied levels of technical confidence. The instructor should also be prepared to teach effectively in an AI-enhanced teaching and learning environment. This includes helping students use gene

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