About the job
About the University of Niagara Falls Canada
Innovation Flows Here.
Officially opening its doors in 2024, the University of Niagara Falls Canada (UNF) is an innovative and digitally oriented institution that prepares graduates for leadership in the digital world. We believe in delivering programs that provide a direct pathway to meaningful careers in high-demand industries. UNF is part of the Global University Systems Canada group of institutions and Global University Systems, an international network of higher education institutions.
Our location
Our campus is located in historic downtown Niagara Falls. Beyond being Canada’s #1 travel destination, Niagara Falls is a small vibrant city that offers up a slice of metropolitan living at a different pace. From world-class dining and entertainment options to luxury shopping and cultural experiences. Our location offers the amenities of a large urban centre within a comfortable, liveable community.
About the faculty and department
Data analysis is one of the fastest emerging professions in Canada. The purpose of the Master of Data Analytics (MDA) program is to develop big data professionals who can grow in this high-demand career. Workplace problems frame and guide all learning in this program from real-world case studies, to Internships, to the capstone project, students build the specialty knowledge and technical competencies for a successful data analysis profession. The Master of Data Analytics (MDA) program provides the core training in the Data Science Lifecycle – from Problem Framing and Hypothesis Formulation to Data Exploration, Warehousing, Analysis, and Visualization.
The Master of Data Analytics program invites applications for the position sessional instructor for the following course:
Course Name: Data Warehousing and Visualization
Per course rate: $7,200 (paid in installments)
Academic Term: Winter 2025( January 13 – April 6, 2025)
Course Overview:
The course builds on the knowledge gained in the Principal of Analytics course (DA500) and Descriptive and Predictive Analytics. This course provides a comprehensive introduction to data management and visualization techniques using Microsoft Power BI. Throughout the course, students will gain hands-on experience in building and managing data warehouses, performing data analysis, and creating interactive visual reports. Key topics include data modeling, data transformation, and advanced visualization techniques to extract meaningful insights from complex datasets. By the end of the course, students will have developed the skills necessary to effectively manage data and present it in ways that support informed decision-making in various business contexts.
The Data Warehousing and Visualization course emphasizes practical data management and analytics using Microsoft Power BI. Key learning areas include:
- Building and optimizing data warehouses to support large-scale data management.
- Performing descriptive, predictive, and prescriptive data analysis for informed decision-making.
- Utilizing Data Analysis Expressions (DAX) for advanced calculations and data transformations.
- Creating dynamic and interactive reports and dashboards to visualize insights.
- Applying Power BI’s capabilities in real-world scenarios, such as healthcare, finance, and business analytics.
- Integrating data from multiple sources to develop comprehensive data models and streamline workflows.
Key Responsibilities:
- Lecturing & Teaching: Deliver engaging lectures and instructional content across multiple delivery modes (in-person, online, or hybrid) to create an inclusive, dynamic learning environment.
- Course Materials: Prepare, update, and maintain course materials, including assignments, quizzes, and exams. Ensure materials are accessible and relevant to students’ learning.
- Student Engagement: Offer regular office hours to meet with students, address inquiries, and provide academic support.
- Assessment & Grading: Design and grade assignments, tests, and exams in a timely and consistent manner. Provide meaningful feedback to students on their progress.
- Curriculum Development: Collaborate on curriculum development to ensure course content is aligned with industry standards and trends.
- Technology Integration: Use internal software to manage grades, track progress, and update students on course material.
Required Qualifications:
- A Doctorate/Ph.D. in Data Science, Computer Science, Statistics, Math, Economics, or a related field, with a focus on predictive analytics or data analytics.
- Teaching experience in predictive analytics, regression modeling, data mining, and machine learning at the undergraduate or graduate level.
- Strong familiarity with software and programming languages used in prescriptive analytics, with demonstrated proficiency in Excel and Python and/or SAS
- Industry experience in the application of predictive analytics in domains such as healthcare, consumer behavior analysis, credit risk assessment, etc., is a significant asset.
- Curriculum development experience for on-site, online, and hybrid delivery is preferred.
- Ability to mentor and advise students in both academic and professional development contexts.
Preferred Competencies:
- Student-Centric Approach: Ability to create an inclusive learning environment that promotes student success and engagement.
- Technical Expertise: Proficiency in predictive analytics methodologies, with hands-on experience in SAS, Excel, and data visualization tools.
- Effective Communication: Excellent presentation skills with the ability to explain complex topics in an accessible way to students from diverse backgrounds.
- Problem Solving: Creative problem-solving abilities, especially in integrating real-life case studies and practical examples into the curriculum.
- Professionalism: Strong interpersonal skills, diplomacy, and discretion in dealing with colleagues and students.
- Time Management: Ability to prioritize tasks and manage multiple responsibilities in a high-pressure environment.
Application process
Interested candidates are invited to submit an application, using ONE document that includes a cover letter, resume/curriculum vitae (CV) including evidence of teaching experience and scholarly accomplishments, and teaching portfolio (if applicable), as well as the names of three references. A single PDF document is preferred. Note: file maximum of 5MB per attached upload.
Alternatively, applications can be sent to jobs@unfc.ca.
Diversity, inclusion, and equity
UNF is strongly committed to equity, diversity, and inclusivity within its community and especially invites applications from all qualified candidates. Racialized persons / persons of color, women, Indigenous / Aboriginal People of North America, persons with disabilities, 2SLGBTQI+ persons, and others who may contribute to the further diversification of ideas are encouraged to apply.
We recognize that applicants may have had obligations outside of work that have negatively impacted their record of achievements (e.g., parental, elder care, and/or medical). You are not required to disclose these obligations in the hiring process. If you choose to do so, UNF will ensure that these obligations do not negatively impact the assessment of your qualifications for the position.
All qualified candidates are encouraged to apply; however, Canadian citizens and permanent residents will be given priority.
We will accommodate the needs of the applicants and the Ontario Human Rights Code and the Accessibility for Ontarians with Disabilities Act (AODA) throughout all stages of the selection process, please advise jobs@unfc.com to ensure your accessibility needs are accommodated through this process. Information received relating to accommodation measures will be addressed confidentially.
We appreciate all applications received; however, only candidates selected for an interview will be contacted.
We are situated on the traditional territory of the Haudenosaunee, Hatiwendaronk and Anishinaabe peoples. We also acknowledge the many other First Nations, Métis, and Inuit peoples who call this region home.
We commit to building relationships based on respect, reciprocity, and reconciliation as we work, learn, and gather on this land. Furthermore, we acknowledge that the Niagara region is situated on treaty land, and we stand with all Indigenous peoples, past and present, in promoting the wise stewardship of the lands on which we live.
Learn more about the University of Niagara Falls Canada at our website .