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 September 22, 2025, to December 14, 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

🔍 Goal (Generalized)

Apply data science and analytics to sector-specific problems (e.g., risk modeling, operational efficiency, customer insights, usage metrics, or public data transparency) to drive decisions and improve performance.

📌 Project Overview & Objective Statement

• Define the business or organizational challenge (e.g., fraud detection, patient wait times, customer behavior, SaaS churn, community insights).
• State the project goal and its impact (e.g., reduce risk, streamline operations, improve service delivery).
• Identify the project type (e.g., classification model, process optimization, dashboarding, segmentation).

🗂 Data Summary & Preprocessing

• Data Description: Source(s) of data (e.g., transaction logs, EHR, POS systems, usage logs, census data), timeframe, and key fields used.
• Cleaning & Preparation: Handling nulls, encoding, outliers, normalization, joins across tables.
• Preprocessing Notes: Sector-specific steps like:
• Finance: Feature engineering from transaction history
• Healthcare: Time calculations between visits
• Retail: Basket-level aggregations
• Technology: Sessionization or usage aggregation
• Public Data: Geocoding or demographic categorization

🧠 Analysis or Modeling Output

Depending on the project type:

• Predictive Model & Evaluation (e.g., credit risk, fraud detection, churn): Algorithm choice, accuracy, precision/recall, ROC-AUC.
• Segmentation/Classification Logic (e.g., retail customer groups): Clustering approach or rules used.
• Operational Optimization Output (e.g., clinic wait times): Queue modeling, bottleneck detection, throughput comparisons.
• KPI Dashboard or Time-Series Analysis (e.g., SaaS product usage): Interactive visuals and usage metrics over time.
• Open Data Visualization Tools (e.g., Tableau, Power BI, GIS): Maps or charts for public indicators.

📊 Insights Report

• Key Findings: Risk drivers, operational inefficiencies, customer clusters, high-usage patterns, or equity gaps.
• Visualizations: Supporting graphs, tables, and dashboards with filters or drill-downs.
• Trends & Anomalies: Unexpected spikes, lags, or deviations.

✅ Recommendations

• Finance: Credit policy adjustments or fraud alert rules.
• Healthcare: Scheduling improvements or resource reallocation.
• Retail: Tailored marketing or stock planning per segment.
• Technology: UX or feature adjustments to improve retention.
• Public/Non-Profit: Policy priorities or community engagement tactics.

📽 Stakeholder Presentation

• Clear, visual summary of the problem, approach, findings, and actionable recommendations.
• Tailored to non-technical audiences (e.g., business leaders, clinicians, nonprofit managers, city officials).

💬 Reflection or Learning Report

• Summary of methods, skills applied, and decisions made during the project.
• Challenges faced and how they were addressed.
• Personal or team reflections on real-world application and future improvements.