Data Science & Artificial Intelligence Programme

The Data Science & AI Programme can be taken in either 24 weeks part time or 12 weeks full time and is designed for career-driven professionals to transition into the big data and AI industry. 

The programme includes pre-work, intensive practical training, industry award and a job outcomes programme in collaboration with industry partners. The course content has been tailored to give you the skills that are demanded by businesses in today’s rapidly evolving job market.

Remote learning is available for this programme. The interactive remote format enabled by video conferencing technology is a rich and connected experience featuring virtual break-out rooms, shared screens, digital whiteboards, peer to peer collaboration and instructor support.

Learn how to create predictive models by learning to wrangle, analyse, visualise, predict and find insight into data alongside the best data science trainers in Aotearoa New Zealand.

The Data Science & AI Programme is a structured training course designed to help you develop the practical skills you will need as a Data Scientist or Data Analyst. After completing your Data Science & AI Programme, you will receive an AUT Data Science & AI Certificate of Completion and Institute of Data Certificate of Achievement.

Hundreds of professionals choose the Institute of Data every year to prepare them for their future career after careful consideration of their desired career progression and to attain maximum return on investment for their education. Leading the future of the profession, the Institute of Data offers a collaborative and successful programme, driven to ensure you have the best start to your career.

What is Practical Data Science Training?

Practical data science training is an essential requirement to practice as a Data Scientist or Data Analyst in Aotearoa New Zealand. Learn from industry experts on how to apply machine learning and AI techniques for businesses and government organisations including business consulting and simulating commercial projects. This means you will be ready to work as a Data Scientist or Data Analyst.

Syllabus Breakdown:

•  Mathematics, Statistics and Programming for Data Science
• Course Introduction and Overview
• Programming Fundamentals for Data Science
• Maths & Statistics Fundamentals for Data Science
• Data Science Practice (ongoing)
•  Exploratory Data Analysis and Data Science Practices
• Data Cleaning and Quality Assessment
• Data Visualisation
• Statistical Testing
• Succeeding as a Data Scientist in Industry
• Mini Project 1 – Data Science Practice
• Critical Thinking Training
•  Databases and APIs
• Relational and Non-Relational Databases
• SQL
• Application Programming Interfaces (APIs)
• Data Science Practice (ongoing)
•  Machine Learning - Supervised Learning - Regression
• Introduction to Machine Learning
• Feature Modelling
• Evaluating Regression Models
• Overfitting and Regularisation
• Questioning Technique Training
•  Machine Learning - Supervised Learning - Classification
• Logistic Regression
• Evaluating Classification Models
• Support Vector Machines
• Bayesian Inference
• Data Science Practice (ongoing)
•  Machine Learning - Unsupervised Learning
• K-means Clustering
• Principal Components Analysis
• Research and Documentation Training
• Mini Project 2 – Data Science Practice
•  Decision Trees and Ensemble Methods
• Decision Trees
• Random Forests
• Bias and Variance
• Bagging
• Boosting
• Stacking
• Data Science Practice (ongoing)
•  Natural Language Processing (NLP)
• Web Scraping
• Text Pre-processing
• Sentiment Analysis
• Text Classification
• Presentation Skills Training
• Data Science Practice (ongoing)
•  Artificial Intelligence and Deep Learning
• Introduction to Artificial Intelligence
• Reinforcement Learning
• Deep Feedforward Neural Networks
• Recurrent Neural Networks
• Convolutional Neural Networks
• Mini Project 3 – Data Science Practice
•  Machine Learning Deployment and Cloud Computing
• Introduction to Cloud Computing
• Deployment of Machine Learning Models
• Cluster Computing
• Streaming Data
• Storytelling Training
• Capstone Project
•  Ongoing Modules
• Additional Topics: Determined by Students and Lead Trainer
• Job Outcomes Programme
• Consultation and Presentation training
• Data Science Practice
• Capstone Project