How to apply
Next cohort start 20th SepApply now
Data science and machine learning are about understanding how data can be used to make key business decisions and automate processes. Given the huge amounts of data being collected in the digital age, companies across all fields want to utilise their data to inform their decisions and improve operational efficiency.
The demand for data scientists has tripled over the past 5 years.
The number of machine learning engineer positions on Indeed quadrupled between 2015 and 2018.
This course was created with the intention of helping meet that demand.
Most of our students have a STEM background and are required to have a basic understanding of linear algebra, statistics and coding. The 15 minute quiz you complete during the application process will assess this and will give you access to precourse material to fill in any gaps in knowledge you may have.
If you love solving problems across different fields using data and are looking to get hired doing this, you have come to the right place.
Hours of coding
Data collection, Data formats, Pandas Python library, Data cleaning, SQL, ETL, Distributed computing & Data versioning
Introduction to data pipelines
Introduction to APIs
Selenium for web scraping
Performing basic web automation actions
Referencing HTML elements using Xpath
CSV, JSON & Parquet file formats
Intro to Pandas
Reasons for and approaches to data cleaning
Handling missing data
Data Lakes and Warehouses
SQL Join operations
Intro to DataBricks
Distributed computing with PySpark
Exploratory data analysis, Intro to Machine Learning, Theory, Supervised Models, Ensembles, Unsupervised learning techniques
Intro to ML & when (NOT) to use
Validation and testing
Hyperparameters, grid search and K-fold cross validation
Feature selection and feature engineering
Bias & variance, underfitting and overfittting
Maximum Likelihood Estimation (MLE)
Dimensionality reduction using principal component analysis (PCA) & T-SNE
Neural Networks, Computer Vision Fundamentals, Acceleration, Pretrained models, Real-world Applications
PyTorch Datasets and DataLoaders
Making custom datasets
Optimisation for deep learning
Convolutional Neural Networks (CNNs)
Architecture tips, data augmentation & debugging tips
Hardware acceleration (GPUs & TPUs)
Content based recommendation systems
Deployment, Automated deployment, CI/CD, scaling, Monitoring & CT
EC2, AMIs & SSH
Architecting an end-to-end ML product on AWS
Orchestration & Airflow
CI/CD with GitHub actions
Our course is part-time and runs from Monday - Thursday, 6:30 pm - 9:30 pm
Our expert instructors will walk you through new content and explain the underlying concepts
Work on challenges independently
Work with your cohort in small groups
Change your future in four easy steps
Complete the application form in less than 10 minutes
Find out if you are course-ready by taking a short assesment
Talk to our Admissions team to make sure this Course is right for you
After the evaluation process, you will recieve a decision. Then get started!
We connect our students to world class AI industry mentors. They’ll lecture technical topics in class, answer questions and share informal career advice in scheduled office hours.
Dedicated support means that on top of the 12 hours in class per week, you’ll have scheduled group office hours weekly, support through Slack and 1-on-1 sessions available to book.
Don’t waste a second. Learn from the comfort of your own home. Reach instructors instantly. Be ready with just an internet connection and your laptop.
Your financial background should be no barrier to accessing a quality education. Our wide range of tuition plans are designed to give you ultimate flexibility regardless of your circumstances.
Pay the tuition cost in manageable monthly installments over 24 months.