Margarita is a senior analyst with over 7 years experience across market research, analytics, and financial services. As a lead Tableau developer, her current role also focuses on advocating and empowering business users to use visual analytics, building up internal Tableau education programs and the nitty gritty details of server infrastructure. Passionate about people, data and the positive social impact the two combined can achieve, she’s also a current Board Member of Engineers Without Borders Australia and is registered on Tableau Service Corps.
- Time commitment
- 12 weeks (3 months) 100% online
- Next start date
- 20 Jan 2020
- Python programming, including common data analysis libraries (NumPy, Pandas, Matplotlib); SQL programming; Statistics (Descriptive and Inferential); Calculus; Linear Algebra; Experience wrangling and visualising data
100% online, mentor supported, workplace ready
IBM Watson’s Chief Architect, Paul Taylor, said it best: “AI and Data Science require new skills that are in high demand and ultimately impact all industries, organisations and modern smart systems.” He’s right, too. Data Science is evolving, and employers are putting a premium on machine learning experience. The McKinsey Global Institute estimates 200,000 data scientist job openings in 2019, with salary benchmarks at around $147,000. A working knowledge of AI isn’t a bonus anymore – it’s almost a necessity.
In this course, you’ll learn the fundamentals of machine learning from some of Australia’s leading industry experts, build basic algorithms, run pipelines and deploy your solutions to the cloud. Everything you need for a brave new world.
Machine Learning For Data Science was designed with cutting-edge content from Silicon Valley disruptor Udacity, then multiplied with the credibility of RMIT through localised mentors with the best in industry. It’s a technical, job-ready program. You’ll need to be comfortable with wrangling data in Python and SQL, and have some experience with inferential statistics and data visualization. (If you’re after an intro to Data Science, check out RMIT’s Programming For Data Science.
Supervised and unsupervised machine learning, neural networks, data pipelines, recommendation systems, data scaling, PCA, PyTorch.
Get RMIT certified
After completing an RMIT Future Skills course, you will earn an RMIT certificate and credential which can be validated, recognised and shared on social media platforms.
Types of machine learning
Support vector machines
Ensemble of learners
Training and tuning
Introduction to Deep Learning
Introduction to neural networks
Implementing gradient decent
Training neural networks
Deep learning with PyTorch
Hierarchical and density based clustering
Gaussian mixture models
Principal component analysis
Random projections and independent component analysis
Learn with Industry Experts
Get ready to meet some of the biggest names in the business
Kale Temple is a co-founder and practise director at Intellify where he leverages expertise in data science and machine learning to architect solutions that empower business performance and growth. He has consulted with a number of the world’s leading corporate and government organisations from over 5 years. Since 2014, he has co-founded and scaled two successful technology start-ups and as Data Scientist & Consultant at Agile BI, played a key role in building the business from the ground up into a world-leading Microsoft Power BI Partner. He holds a Bachelor of Liberal Arts and Sciences (Economics) and Masters of Economics (Economics & Econometrics) from the University of Sydney.