Data Analytics With AI

Data analytics plays a critical role in today’s data-driven world. Organizations rely on data to make informed decisions, and professionals with strong analytical and AI skills are in high demand across many industries.

This program is designed to help students develop practical skills in data analysis, visualization, and artificial intelligence. You will learn how to work with large datasets, uncover patterns, and transform raw data into meaningful insights that support business decision-making.

Students gain hands-on experience using modern tools and techniques, including predictive modelling and machine learning methods, to solve real-world problems.

Data analysts collect, process, and analyze data to identify trends and patterns. Their work helps organizations improve performance and make strategic decisions.

Typical responsibilities include:

• Collecting data from various sources such as databases and surveys

• Cleaning and organizing data for accuracy

• Identifying trends and patterns

• Creating reports, charts, and visualizations

• Presenting insights to stakeholders

• Diploma completed in approximately 19 months

• Internship included for real-world experience

• Career support after graduation

• Flexible learning schedule (online options available)

• Updated curriculum aligned with industry needs

• Multiple start dates throughout the year

• Financial assistance available for eligible students

Graduates may find opportunities in a variety of industries, including:

• IT and software companies

• Healthcare organizations

• Banks and financial institutions

• Retail and e-commerce

• Government and public sector

• Education and research

• Manufacturing and logistics

• Data Analyst

• Business Intelligence Analyst

• Data Scientist

• AI / Machine Learning Specialist

• Big Data Engineer

• Marketing Analyst

• Data management and data analysis

• Cybersecurity fundamentals in data environments

• Predictive analytics techniques

• Advanced machine learning concepts

• Defining business problems using data

• Setting key performance indicators (KPIs)

• Designing data analytics platforms and environments

• Data ingestion from multiple sources

• Data exploration, summarization, and visualization

• Identifying target and independent variables

• Building predictive models using machine learning

• Deploying models into real-world applications