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Advanced Data Analytics Internship Winter 2026
University of Niagara Falls Canada
This internship offers students in the Master of Data Analytics (MDA) program at the University of Niagara Falls, Canada, the opportunity to apply advanced analytics skills in a cross-functional, real-world setting. Whether in healthcare, finance, technology, retail, or the public sector, interns are trained to gather, analyze, model, and interpret data to uncover business insights and enable evidence-based decision-making. Organizations benefit from temporary but impactful support in building dashboards, forecasting, automating reporting, or improving internal analytics capacity. About the Learners: Enrolled in UNF’s Master of Data Analytics program, this program is built on industry needs and hands-on skill development Have completed foundational and intermediate coursework, with core competencies in dashboard(s), forecasting models. data reports, ETL scripts, and insight presentations Familiar with tools, platforms, and datasets such as Python, SQL, Tableau, Power BI, Excel, and R. Have completed a minimum of three program terms and mandatory pre-internship training modules Demonstrated strong dedication, professionalism, and readiness to contribute to industry-aligned projects Project Details: Intermediate/advanced level scope 350-480 hours, completed over 10-12 weeks between January 5, 2025, to March 22, 2025 1 project per learner Learners complete the project , in-person, hybrid, or remotely and work independently or with a company team. Interns may support your team with: Data wrangling and transformation from multiple sources (CSV, SQL databases, APIs) Creating interactive dashboards using Tableau, Power BI, or Excel for reporting purposes Building predictive or classification models for forecasting, risk scoring, or behavior prediction Performing exploratory data analysis (EDA) to identify trends, outliers, or process bottlenecks Automating business intelligence workflows using Python or R Presenting findings through concise reports, executive summaries, or stakeholder presentations Evaluating the quality and reliability of data and proposing cleaning or governance solutions Employers play a key role in guiding projects and providing feedback. This is a valuable opportunity to support emerging talent and receive meaningful contributions from skilled learners ready to make an impact. Interns will be assigned a supervisor within the host organization and will receive academic mentorship from a UNF Faculty Advisor. Regular check-ins will be coordinated with the Work-Integrated Learning (WIL) Manager to support the student's progress and development. Why Host a UNF MDA Intern? Gain short-term, high-impact analytics support Explore data-driven opportunities without full-time hiring Help shape the next generation of Canadian data professionals Enhance your team’s capability in applied analytics and dashboarding
Data visualization
Data analysis
Data modelling
Data science
Building Ethical Open-Source Software Solutions with Industry Collaboration
University of Toronto
This experience connects industry professionals with learners who have foundational knowledge in computer science, focusing on practical applications of programming, data analysis, and software development. Learners are equipped to tackle real-world challenges by applying their skills in coding, algorithm design, and problem-solving to projects that require innovative solutions. By collaborating with learners, companies can benefit from fresh perspectives and technical insights to enhance their projects. Please note we are ONLY looking for open-source projects (meaning no NDAs or confidentiality agreements).
Mobile app development
Information technology
Software development
Machine learning
Artificial intelligence
BUSA471 - Artificial Intelligence Ethics for Business
McGill University
In this project, students in groups of 4-6 assume the role of organizational consultants, providing advice on topics related to responsible AI and AI ethics such as: how to develop responsible AI policies, managing the ethical considerations related to an AI implementation, and addressing the impact of AI on the workforce. Students will work on a specific AI-related problem or topic of interest that you have articulated, providing you with their insights or suggestions. The projects give students an in-depth overview of the challenges involved in identifying and addressing the ethical implications of AI use in a business context as well as the implications for society.
Artificial intelligence
Operations
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Bioengineering Capstone: Real-World Design and Prototyping in Biomedical Engineering
Northeastern University
This capstone experience provides companies with a team of senior-level bioengineering learners who are trained to apply engineering design principles to solve complex real-world biomedical challenges. Throughout the course, learners will: Design, develop, and prototype medical devices, diagnostic tools, or bioengineering systems. Work under real-world fiscal, regulatory, and safety constraints to refine their designs. Utilize prototyping labs and wet lab spaces to construct and test solutions. Communicate findings effectively through written reports, oral presentations, and technical documentation . Consider ethical, environmental, and economic factors in engineering solutions. Learners will: Apply engineering and bioengineering principles to solve complex problems. Conduct background research , including journal articles and patents , to inform design choices. Develop a working prototype or simulation with documentation and testing data. Address safety, compliance, and regulatory considerations in the design process. Communicate results effectively through design history documentation, presentations, and final reports . Work in interdisciplinary team settings to complete projects on time and within scope.
Biotechnology
Spring 2026 Applied Sociological Research for Data-Driven Decision Making
The University of Texas at Dallas
The Advanced Sociological Research course at UT Dallas is designed to provide undergraduate students with hands-on experience in applied research methods and data analysis. Students in this course will engage in real-world projects that address complex societal issues, utilizing techniques such as surveys, interviews, case studies, and statistical analysis. Their work will support evidence-based decision-making for businesses, government agencies, and non-profit organizations. These learners are refining their professional skills in research design, data collection, data analysis, and effective communication of findings. The primary goal is to help students develop a deep understanding of how sociological research can be applied to solve pressing challenges in various sectors, including education, housing, public health, and marketing. We invite employers to collaborate with our students by offering relevant projects that allow learners to apply their academic knowledge and further develop their professional writing, analytical, and interpersonal skills. Employers will be expected to engage in regular communication, offer guidance and resources, and participate in final presentations to provide valuable feedback to the students. This collaboration will provide a fresh, data-driven perspective on real-world problems.
Data analysis
Workplace culture
Workplace health/wellness
Communications
Volunteer organizing
Business Analytics Practicum - Spring 2026
Montclair State University
Beginning in January 2026, student consultants from the Master of Science in Business Analytics program will work with your organization to identify and formulate a business problem that can be addressed using the analytical approaches that have been covered in this degree program. This course is designed to provide an experiential opportunity for the students to apply their Business Analytics skills in solving a real business problem. In this course, students will work on a collaborative group or individual project that addresses, ideally, a live business problem using the analytical techniques learned in the other courses comprising this major. Students will clearly articulate the business problem and the goals of their chosen analytical approach. They will have access to realistically big data and an opportunity to appreciate, through an application, the possibilities and limitations of these analytical techniques. Students will be placed in companies and expected to understand and communicate the business implications of their analysis to interested stakeholders. Companies are welcome to take on more than one student team.
Machine learning
Data visualization
Data analysis
Data modelling
Data science