The Graduate Diploma in Data Analytics (Level 7) at NZSE is designed for graduates who want to move into data analytics, business intelligence, data engineering and machine-learning-related careers. The programme combines statistics, technology and business strategy and focuses strongly on practical application. Students work with tools and technologies including Python, R, SQL, machine learning, AI, big-data platforms and business intelligence.

Key benefits include:

• Practical, hands-on data analytics training.
• Development of skills in collecting, cleaning, transforming and interpreting data.
• Training in Python, SQL, machine learning, AI and big-data technologies.
• A real-world capstone project based on a business problem.
• Development of a job-ready portfolio.
• Industry-focused project experience.
• Live classes and learning from industry experts.
• 10 weeks of mentored work experience listed by NZSE.
• Pathways to graduate and postgraduate study at universities in New Zealand or overseas, as well as Te P?kenga and other PTEs.

Programme Structure and Subjects / Topics

The current programme page lists the following courses:

1. GDDA604 – Data Collection and Analysis
   Covers methods of collecting data from different sources and transforming, analysing and interpreting data for data-driven business decisions. Technologies include Python and SQL.
2. GDDA612 – Data Transformation and Management
   Focuses on collecting, cleaning and transforming data, importing data into data stores, querying and manipulating datasets, and exporting data for further use. Technologies include Python, SQL/NoSQL, BeautifulSoup, Scrapy and Selenium.
3. GDDA707 – Advanced Data Engineering
   Covers data modelling, relational and non-relational data models, extraction, transformation and loading (ETL), and data engineering using big-data platforms. Technologies include Python, SQL, ETL, Cassandra, Hive, MongoDB and the Apache Hadoop ecosystem, including HDFS, Yarn, Zookeeper, Kafka and Spark.
4. GDDA708 – Machine Learning and AI
   Covers supervised and unsupervised machine-learning algorithms and their application in AI. Students learn to evaluate and optimise algorithms for business decisions and AI applications. Technologies include Python, TensorFlow and Scikit-learn.
5. GDDA709 – Big Data Analytics
   Focuses on investigating, evaluating and implementing big-data technologies. Technologies include Hadoop, Yarn, HDFS, MapReduce, Hive, Apache Spark and Spark machine-learning libraries.
6. GDDA713 – Capstone Project
   Students apply critical thinking, creativity, problem-solving, communication and technical skills to solve a real-world problem. The project involves addressing a client brief and demonstrating the programme's graduate-profile outcomes. Technologies may include SQL, Python, AI and machine learning, big-data analytics and statistical models.
7. GDDA716 – Emerging Trends and Technologies in Data Analytics
   Covers emerging developments in data analytics, research methodologies, technology trends and their business applicability. Students also conduct SWOT analysis and communicate research findings to technical and non-technical audiences.

Disciplines / Specialisations

The programme does not currently advertise separate named majors or specialisations.

Its principal study areas are:

• Data analytics
• Data collection and analysis
• Data transformation and management
• Data engineering
• Big-data analytics
• Machine learning
• Artificial intelligence
• SQL and database technologies
• Statistical modelling
• Business intelligence
• Emerging data technologies
• Data-driven business decision-making

Programme Highlights

The current NZSE information highlights:

• NZQF Level 7 qualification.
• 120 credits.
• 1 academic year.
• Blended delivery.
• Auckland CBD campus.
• Live classes.
• Industry-focused learning.
• Job-ready portfolio development.
• Real-world capstone project.
• 10 weeks of mentored work experience.
• Exposure to current data and AI technologies.
• Training in Python, SQL, machine learning and big-data technologies.
• NZQA-certified programme.
• Alumni-network access.
• Pathways to higher-level study.

Career Outcomes

NZSE specifically lists the following potential career roles:

• Data Analyst
• Database Developer Administrator
• Social Media Data Analyst
• Marketing Data Analyst
• Business Intelligence Analyst
• Data Insights Analyst
• Research Analyst
• Data Warehouse Engineer
• Machine Learning Analyst
• Quantitative Analyst

Latest Updates / Special Requirements

The current 2026 NZSE information confirms the following programme details:

• Level 7 Graduate Diploma.
• 120 credits.
• 1 academic year.
• Blended delivery.
• Auckland CBD campus.
• February and July intakes.
• International IELTS requirement of 6.0 overall with no band below 5.5.
• Relevant degree-level qualification equivalent to at least 360 NZQA Level 7 credits.
• Current programme-page international fee of NZD $19,800.
• 10 weeks of mentored work experience.
• Capstone project.
• Pathways to higher-level study.

nternational Student Visa Requirements

International students intending to study this programme in New Zealand generally require a Fee Paying Student Visa unless another visa category applies. NZSE's programme page specifically requires international applicants to hold a valid study visa.

Immigration New Zealand currently requires applicants for a Fee Paying Student Visa to have:

• An offer of place from an approved education provider.
• Sufficient funds to pay tuition fees or evidence of an applicable scholarship.
• Sufficient funds for living expenses or an acceptable sponsor.
• Acceptable medical and travel insurance.
• Evidence satisfying applicable health, character and genuine-intention requirements.

For tertiary study, Immigration New Zealand currently requires evidence of:

• NZD $20,000 for each year if studying for 1 year or more; or
• NZD $1,667 per month if the course is shorter than 1 year.