This internship provides students from the University of Niagara Falls Canada's Master of Data Analytics (MDA) program, with hands-on experience in marketing analytics. The intern will support the marketing or data team in analyzing customer behavior, campaign effectiveness, and digital performance metrics to drive strategic decisions. The internship is ideal for organizations looking to enhance their data-driven marketing capabilities.

About the Learners:

• Enrolled in UNF’s Master of Data Analytics program, specializing in Marketing Analytics, 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 per learner, completed over 10-12 weeks
• 1 project per learner
• Learners complete the project in person or remotely and work independently or with a company team.

Key Responsibilities

• Analyze digital marketing performance metrics (e.g., website traffic, conversion rates, click-through rates) using tools like Google Analytics and Tableau.
• Support customer segmentation and lifetime value analysis using CRM data.
• Build dashboards and visualizations for internal reporting on campaign KPIs.
• Develop predictive models (e.g., churn prediction, response modeling) to inform marketing strategy.
• Assist in A/B testing setup and analysis for email, web, or ad campaigns.
• Clean, transform, and prepare marketing data for analysis using Python or SQL.
• Summarize and present actionable insights to stakeholders.

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 an MDA Intern?

MDA interns bring immediate value to your organization by:

• Supporting data-driven decision-making
• Enhancing analytics capability within your marketing team
• Contributing to special projects without long-term hiring commitments
• Building a potential talent pipeline for future recruitment

🔍 Goal (Generalized):

Analyze marketing, customer, or engagement data using data science tools to generate actionable insights, build data products (e.g., models or dashboards), and communicate results to stakeholders.

Project Overview & Objective Statement

• Brief summary of the business problem, dataset context, and project goal.
• Description of the selected project type (e.g., churn prediction, campaign analysis, etc.).

Data Summary & Preprocessing

• Data Description: Sources, timeframe, and key features used.
• Cleaning & Preparation: Handling of missing data, formatting, filtering, and transformations.
• Preprocessing Notes: Specific to project type (e.g., tokenization for sentiment, feature engineering for churn).

Analysis or Modeling Output

Depending on the project:

• Model Code & Evaluation (e.g., churn prediction): Model logic, algorithm choice, and performance metrics.
• Segmentation or Classification Logic (e.g., engagement tiers): Criteria, clustering, or rule-based segmentation.
• Dashboard Design & Output (e.g., funnel performance): Interactive visuals with drill-downs by channel/time.
• Sentiment Analysis Pipeline: NLP code and output (e.g., polarity scores, topic clusters).

Insights Report

• Key Findings: Top predictors, engagement trends, sentiment drivers, or conversion bottlenecks.
• Visualizations: Charts, graphs, or dashboards showing key results.
• Trend & Anomaly Highlights: Noteworthy patterns or outliers.

Recommendations

• Business or Marketing Actions: Based on findings—targeting strategies, A/B tests, messaging adjustments, retention tactics, etc.

Stakeholder Presentation

• Slide deck summarizing project process, visuals, insights, and recommendations.
• Tailored for a non-technical audience (e.g., marketing or business stakeholders).

Reflection or Learning Report

• Summary of skills used, technical challenges faced, decisions made, and what could be improved or expanded.
• Brief reflection on team collaboration and real-world application potential.