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A syllabus informed by industry

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

Unit 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, visualised, shared and manipulated.

Module 1 - Data collection
    Introduction to data pipelines
    Introduction to APIs
    HTTP requests
    Web scraping using requests and Selenium

Module 2 - Data formats & the Pandas Python library
    Important file formats
    Intro to Pandas
    More Pandas

Module 3 - Data cleaning & exploratory data analysis (EDA)
    Reasons for and approaches to data cleaning
    Descriptive statistics
    Data visualisation
    Handling missing data

Module 4 - SQL
    SQL basics
    SQL Join operations
    SQL Aggregations
    SQL Subqueries
    Creating databases and importing data
    Python SQLAlchemy

Module 5 - Extract, Transform, Load (ETL)
    ETL using Pandas, SQLAlchemy
    MapReduce

Module 6 - Intro to cloud
    Intro to Amazon Web Services (AWS)
    AWS RDS
    The AWS CLI & Boto3
   AWS S3

Unit 2. Data Science & Machine Learning

This unit covers a comprehensive overview of data science. We'll start by learning how to visualise and explore data using industry standard tools. Modelling data is a key part of data science, and a key tool for doing this is machine learning (ML).

Module 1 - Data Science Basics
    Statistical modelling
    Hypothesis testing
    Multi-collinearity
    Influential points

Module 2 - Introduction to machine learning
    Intro to ML & when (NOT) to use
    Scikit-learn
    Validation and testing
    Linear regression
    Logistic regression
    Multiclass classification
    Hyperparameters, grid search and K-fold cross validation
    Feature selection and feature engineering

Module 3 - Theory
    Evaluation metrics
    Bias & variance, underfitting and overfittting
    Regularisation
    Maximum Likelihood Estimation (MLE)

Module 4 - More supervised models
    K-nearest neighbours
    Decision trees
    Random forests
    Gradient boosting

Module 5 - Unsupervised models
    K-means clustering
    Dimensionality reduction using principal component analysis (PCA) & T-SNE
    DBSCAN

Unit 3. Deep Learning

In this unit, we introduce deep learning models, a class of machine learning models that are able to represent much more complex relationships in data. This means they can reach state of the art results on tasks that involve processing images, audio, video and other complex data.

Module 1 - PyTorch
    Automatic differentiation
    PyTorch Datasets and DataLoaders
    Making custom datasets

Module 2 - Neural networks
    Neural networks
    Dropout
    Batch Normalisation
    Optimisation for deep learning
    Convolutional Neural Networks (CNNs)
    ResNets

Module 3 - Practical tips
    Architecture tips, data augmentation & debugging tips
    Pre-trained models
    Transfer learning
    Hardware acceleration (GPUs & TPUs)

Module 4 - Advanced Architectures
    Attention
    Self-attention
    Transformers

Module 5 - Applications
    Churn modelling
    Content based recommendation systems
    Collaborative filtering
    Lead scoring

Unit 4. ML Engineering & MLOps

In the ML Engineering part of this unit, we'll focus on the engineering required to put models into the real world. Then in the MLOps part, we'll focus on the tools which are becoming industry standards for ensuring that your AI systems scale up, are fault tolerant, and are well documented.

Module 1 - Scaling experiments in the cloud
    EC2, AMIs & SSH
    Experiment tracking
    Ray Tune

Module 2 - Managed solutions
    AWS Sagemaker
    Architecting an end-to-end ML product on AWS

Module 3 - Intro to MLOps
    MLOps overview
    ETL, feature stores & data versioning

Module 4 - Custom solutions
    Docker
    Flask
    MLFlow
    TorchServe
    Prometheus
    Grafana

Module 5 - Fault-tolerant deployment & autoscaling
    CI/CD with GitHub actions
    Kubernetes
    AWS EKS
    Kubeflow
    AWS Cloudformation

Unit 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 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

21st June 2021

Part-time
18 weeks

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

19th July 2021

Part-time
18 weeks

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

23rd August 2021

Part-time
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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