Master of Data Analytics Internship
Duration
10 to 12 weeks
Minimum 350 hours
Format
In-person, Remote, Hybrid
Intern role
Data analytics consultant
Compensation
Paid or unpaid
What is the MDA internship?
As part of the Master of Data Analytics program requirements, students have the option of completing a professional internship during the fourth term at a company offering data analytics-related practice in their field of study. The work period must cover a minimum of 10 weeks of full-time work, approximately 350 hours.
MDA interns are equipped with a robust blend of technical and analytical skills tailored to meet the demands of today’s data-driven industries. Over 10–12 weeks, they can support your organization in unlocking insights, solving data problems, and enabling evidence-based decision-making.
Skills our students bring
Our students gain hands-on experience with leading tools and platforms, including Python, R, SPSS, SQL, Tableau, and Power BI, enabling them to manage, analyze, and visualize complex datasets effectively.
They are trained in statistical modeling, machine learning, and data storytelling, with a strong emphasis on turning raw data into actionable insights.
Whether it’s building predictive models, performing advanced data queries, or creating dynamic dashboards, our graduates are prepared to drive data-informed decision-making across a wide range of sectors.
Summary of skills based on coursework
Analytical methodology and problem-solving
- Deep understanding of the data science lifecycle: from problem definition to delivering insights
- Skilled in framing business problems as hypotheses and developing data-driven solutions.
- Experience with the complete analytics cycle: data collection, analysis, visualization, and interpretation.
Advanced technical proficiency
- Expertise in industry-standard tools: Python, SQL, Excel, Power BI, Tableau, and SAS.
- Hands-on experience in data wrangling: data collection, transformation, cleaning, and preparation.
- Proficiency in handling data quality issues: missing values, outliers, unbalanced data, and normalization.
Statistical thinking
- Solid grounding in descriptive, predictive, and prescriptive analytics.
- Applied understanding of sampling, probability theory, distributions, and inferential statistics.
- Able to perform statistical analysis and modeling using Excel and Python.
- Familiar with Linear Algebra principles applied in machine learning contexts.
Real-world application across domains
- Experienced in solving domain-specific problems in marketing, finance, manufacturing, supply chain, public, and non-profit sectors.
- Skilled in creating, validating, deploying, and monitoring models in operational environments.
- Ability to test, compare, and communicate results of multiple modeling approaches in project settings.
Communication and collaboration
- Strong communication skills to translate technical insights into business impact.
- Experience presenting to stakeholders and collaborating in cross-cultural, high-performance teams.
- Capable of mentoring and contributing to peer and professional development.
Software development practices
- Ability to manage the full lifecycle of analytics projects using software engineering principles.
- Skilled in project planning, execution, testing, and model deployment in real-world environments.
Data ethics and responsible AI
- Deep awareness of data privacy, confidentiality, and ethical use of data.
- Understands bias mitigation and the importance of fairness in analytics and AI systems.
- Commitment to responsible and socially conscious data practices.
Digital mindset and adaptability
- Mindset geared toward continuous learning and navigating digital transformation.
- Understanding of the big data ecosystem and its implications in modern organizations
Marketing analytics expertise
- Practical experience using CRM tools (e.g., Salesforce, Tableau) to derive and communicate marketing insights.
- Ability to analyze customer data, deploy marketing models, and influence business decisions.
Operations analytics skills
- Proficient in using ERP analytics tools (e.g., SAP) to analyze operations data.
- Capable of identifying cost reduction and efficiency improvement opportunities through data insights.
Grading structure
The course is graded on a Pass/No Pass basis. To pass, students must demonstrate satisfactory performance in the following areas:
- Initiative and ownership in completing assigned tasks
- Responsiveness and professionalism in workplace conduct
- Technical competence in applying data analytics tools and methods
- Application of academic learning to real-world business challenges
- Contribution to daily operations and/or problem-solving within the organization
- Efficient and effective use of work time
- Consistent attendance and presence at the job site
- Overall quality of performance, as evaluated by the employer and instructor
Internship work term timelines
Note: Employers who post jobs early will have access to a larger candidate pool.
Intern hiring process
Prepare the job description
There are three options for posting a Data Analytics Internship Position:
1. Propose a project using the UNF MDA Internship Proposal Form (request a form from the Work-Integrated Learning Manager)
2. Create a job posting
3. Submit your project proposal through Riipen
Finding the right hire starts with a well-written job description. Your description will affect the quality and level of students who apply to your position. The Work-Integrated Learning Manager can guide you on the optimal content and format for your posting. Contact us to get started.
Prepare the job description
There are three options for posting a Data Analytics Internship Position:
1. Propose a project using the UNF MDA Internship Proposal Form (request a form from the Work-Integrated Learning Manager)
2. Create a job posting
3. Submit your project proposal through Riipen
Finding the right hire starts with a well-written job description. Your description will affect the quality and level of students who apply to your position. The Work-Integrated Learning Manager can guide you on the optimal content and format for your posting. Contact us to get started.
Post an internship position
We ask that you carefully review the Master of Data Analytics program website and the above details to ensure the position aligns with student learning outcomes.
New employers
Create an account on our Riipen portal to self-post a position. We will contact you if we have any questions and notify you when the position is approved.
Returning employers
Sign in to your account and self-post your position on the Riipen portal.
Frequently asked questions
What are the expected learning outcomes for students during this internship?
Students should gain knowledge of contemporary data analytics practices, and can use their acquired techniques, skills, and modern analytical tools necessary for data analysis.
What benefits are there for students?
They gain exposure to professional and ethical responsibilities of working data professionals, get to work on multi-disciplinary teams, and have the opportunity to understand the impact of data solutions in a wider context.
How is a student’s progress monitored during the work term?
As part of the course requirements, students are expected to submit a weekly report of their hours and a summary of their activities. Employers will need to sign off on this report.
Can employers speak with the university directly during the work term?
During the internship, employers are welcome to reach out to the Internship Instructor at any time to discuss the student’s progress.
Will there be a final evaluation?
Yes, employers are asked to complete a formal evaluation of the student at the end of the work term. The student will provide the necessary evaluation forms at the appropriate times.
Do internships have to be paid?
While compensation is not required, it is encouraged when feasible.
Get in touch
If you are interested in hiring an intern from the Master of Data Analytics program, please reach out to the Work-Integrated Learning Manager by emailing: workintegratedlearning@unfc.ca.
We will contact you to discuss next steps.
