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A syllabus informed by industry

Learn what companies really need, and what universities don't teach you
4 chapters to take you from the basics to the state of the art

Part 1. Data Engineering (click to expand)

As data plays a larger part in every organisation, especially digital ones, it becomes more and more necessary to understand where data comes from, how it's stored, and how it's manipulated.

Module 1 - Data collection.
    Introduction to data pipelines
    Introduction to APIs
    HTTP requests
    Selenium for web scraping
    Performing basic web automation actions
    Referencing HTML elements using Xpath

Module 2 - Data formats & the Pandas Python library.
    CSV, JSON, Parquet file formats
    Intro to Pandas

Module 3 - Data cleaning.
    Reasons for and approaches to data cleaning
    Handling missing data

Module 4 - Intro to Cloud.
    Data Lakes and Warehouses
    AWS S3
    AWS DynamoDB

Module 5 - SQL.
    SQL basics
    SQL Join operations
    SQL Aggregations
    SQL Subqueries

Module 6 - ETL, Distributed Computing & Data Versioning
    Intro to DataBricks
    Distributed computing with PySpark
    Data Versioning

Part 2. Data Science

This chapter is a comprehensive introduction to data science. We'll start by learning how to explore your data and visualise it, through introducing the industry stardard tools for doing so.

Modelling data is a key part of data science, and a key tool for doing this is machine learning (ML). There will be a deep focus on being practical - actually building every algorithm you learn and knowing how and when to apply it. We’ll begin with an introduction to the basic machine learning problems and the different types of ML. In successive sessions, you'll learn the theory behind a particular algorithm before implementing it and using it on a real dataset.

In this unit, we'll also introduce deep learning - a class of models for representing more complex relationships withing data, especially unstructured data like images.

Module 1 - Exploratory data analysis
    Descriptive statistics
    Data visualisation  

Module 2 - Introduction to machine learning
    What is ML?
    When (NOT) to use ML?
    Linear regression
    Validation and testing
    Hyperparameters, grid search and K-fold cross validation

Module 3 - Theory
    Evaluation metrics
    Fitting Polynomials and the curse of dimensionality
    Bias & variance, underfitting and overfittting
    Maximum Likelihood Estimation (MLE)

Module 4 - Supervised models
    Multiclass classification
    Decision trees

Module 5 - Ensembles
    Random forests

Module 6 - Unsupervised learning techniques
    K-means clustering
    PCA and t-SNE

Module 7 - PyTorch
    Automatic differentiation
    PyTorch Datasets and DataLoaders
    PyTorch Lightning
    Making custom datasets

Module 8 - Deep learning
    Neural networks
    Batch Normalisation
    Convolutional Neural Networks (CNNs)
    Optimisation for deep learning

Part 3. ML Engineering

In this chapter, we'll focus on the engineering required to put models into the real world.

Module 1 - Acceleration
    GPUs for PyTorch
    AWS EC2
    Cloud training

Module 2 - Running experiments
    AWS Sagemaker for training
    Ray Tune

Module 3 - Pretrained models
    Pretrained models
    Transfer learning

Module 4 - Building cloud solutions
    AWS Sagemaker for deployment
    AWS Lambda
    AWS API Gateway
    Demo building an end-to-end cloud solution

Part 4. MLOps

As the AI systems of more companies mature, there becomes a need for an emerging role that crosses ML engineering with DevOps - ML Operations Engineer, or MLOps Engineer for short. This role is responsible for building, deploying and maintaining ML infrastructure that other teams work with.

Module 1 - Deployment
   Testing using PyTest
    Building an API

Module 2 - Automated deployment, CI/CD and scaling
    AWS CloudFormation
    GitHub Actions for CI/Cd

Module 3 - Monitoring & continuous training (CT)
   AWS Sagemaker for monitoring
    Continuous training

Part 5. Career support

"By the time your graduate, you'll know everything a data scientist or machine learning engineer will need to have an impact in the workplace."
Evgeny Dyshlyuk - Research Scientist, Imperial College London

Is this course right for you?

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 are love solving problems across different fields using data and are looking to get hired doing this, you have come to the right place.

Upcoming cohort start dates

17th May 2021

18 weeks

Monday - Thursday
6:30PM to 9:30PM

21st June 2021

18 weeks

Monday - Thursday
9:30AM to 12:30PM

19th July 2021

18 weeks

Monday - Thursday
6:30PM to 9:30PM

Throughout the programme, you will build a portfolio of projects that will showcase your practical skills. But we know there's more to getting hired than technical knowledge.

At the start of the programme you will have a consultation with one of our coaches to help figure out the optimal career for you. As you progress in your learning, our career coaches will help you polish your CV, hold mock interviews, audit your LinkedIn and keep you accountable in your job search process.

You will be recommended to exclusive roles with our hiring partners and be matched with an industry mentor to make sure you are ready for the workplace.

Our goal is for you to feel 100% confident going into any hiring process.

Our industry mentors will make sure you're ready for the workplace

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.

Meet some of our latest industry mentors
Felisia Loukou
Senior Data Scientist, Adarga
Colin Kelly
NLP Lead, Adarga
Sam Cooper
Senior Lecturer, Imperial College

Easily accessible through evening or morning classes

You'll have classes Monday - Thursday from 18:30-21:30 or 9:30-12:30

18 weeks

8 portfolio projects

200+ hours of programming experience

Dedicated instructors

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.

Remote learning, done right

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.

Our instructors have diverse expertise

Daniel Sanz Becerril

Daniel started out as an analyst over 10 years ago now. He quickly transitioned into data science, taking on various roles and honing his skills for 4 years.

He has since grown a passion for teaching, and has been delivering data science education for the past 3 years.

Nihir Vedd

Nihir is a talented NLP specialist, starting his journey many years ago with his bachelors in Computer Science and masters in Data Processing.

He is currently pursuing his PhD in Natural Language Processing at Imperial College London, in
Dr Lucia Specia's Multimodal AI lab.

Szymon Maszke

Szymon is our very own AI prodigy. He is a voracious coder and contributor to the coding community.

Over 408,000 people have read his answers to Python and machine learning questions on Stack Overflow (the Quora of coding). This puts him in the top 0.4% of contributors in the world!

He has written many open source libraries, which over 1000 people have starred on Github.

Ali Abdeelal

Having contracted in machine learning for over 10 companies across 4 years, Ali knows all about how machine learning is deployed at a wide variety of companies.

He is passionate about education, having taught over 1200 students over the years.

You could be a
  • data scientist.
  • machine learning engineer.
  • data analyst.
  • data engineer.
  • business analyst.
  • natural language processing engineer.
  • data analytics consultant.

Still have questions?

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