Master of Data Analytics (Online)

The Master of Data Analytics Online (MDA) program at the University of Niagara Falls Canada, is designed to prepare graduates for high-demand careers in data analysis.

This 48-credit program can be completed in five 12-week terms, with this flexible online option. Online learning allows students to study at their own pace, from anywhere, while engaging with interactive content and a supportive virtual community. The curriculum emphasizes real-world applications, covering the data science lifecycle, with three specialized tracks to choose from: Marketing Analytics, Operations Analytics, and General Analytics. This format offers the flexibility to balance academic, professional, and personal commitments while gaining industry-relevant skills.

Graduates will be equipped to help organizations make data-driven decisions, driving innovation and increasing organizational value.

The MDA program has been meticulously crafted through extensive research and close collaboration with key industry partners. Through industry-relevant course material and applied learning, graduates acquire the knowledge and skills necessary for success in a data-driven world. This program not only opens doors to a diverse range of exciting career prospects but also positions graduates for opportunities that may not even exist yet.

Domestic students $24,000
International students $48,000

Admission Requirements

• Bachelor’s Degree – Completion of a four-year honours degree standard.
• Completed at least two undergraduate courses in statistics or quantitative methods.

What You’ll Learn

• Master foundational principles of data analytics, learning to interpret and leverage data to drive informed decisions in business and technology contexts.
• Gain hands-on experience with SQL databases, developing the skills to efficiently query, manage, and manipulate data for deeper insights.
• Learn Python programming tailored for data analysis, covering libraries like Pandas, NumPy, and Matplotlib to clean, analyze, and visualize complex datasets.
• Explore predictive analytics techniques, including statistical models and machine learning algorithms, to forecast trends and behaviors based on historical data.