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Data Engineering

Data Science & Machine Learning

Deep Learning

ML Engineering & MLOps

Career Support

Overview

Curriculum

Course schedule

How to apply

Start dates

Instructors

Tuition plans

FAQs

Next cohort start 22nd Nov

Apply now

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.

What we need from you

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.

18

Weeks

200+

Hours of coding

6

Portfolio projects

A four-part curriculum to take you
from the basics to the state of the art

Software & Cloud Engineering Essentials

Apply industry best practices and write engineering-grade code to deploy a production ready project on the cloud.

Module 1:
The command line

Bash scripting
Navigation
Essential Commands and syntax
Finding help

Module 2:
Git and Github

Essential commands and syntax
Version control
Branching
Pull requests
Software collaboration best practices

Module 3:
Python Programming

The Python Environment
Debugging
Arithmetic Variable Assignment and Strings
Lists and Sets
Dictionaries, Tuples and Operators
Control Flow
Loops
Functions
Object Oriented Programming
Advanced Python
Error Handling

Module 4:
Data Formats and Processing Libraries

Numpy
JSON, CSV, XLSX and YAML
Intro to Pandas

Module 5:
APIs & Webscraping

Web protocols and requests
APIs
Webscraping with Selenium

Module 6:
Algorithms and Data Structures

Big O Notation
Sorting and Searching
Linked Lists
Stacks and Queues
Trees and Graphs
Dynamic Programming

Module 7:
Software Design and Testing

Principles of OOP Design
Inheritance, Polymorphism, Abstraction, Encapsulation
Class Decorators
Docstring and Typing
Testing
Project Strcucture
Code Review

Module 8:
SQL

PgAdmin4
CRUD
JOINs
Aggregations
CTEs
Psycopg2
SQLAlchemy

Module 9:
Containerisation with Docker

Creating Docker Containers
Docker Networking and Storage
Monitoring with Prometheus and Grafana

Module 10:
Essential Cloud Technology

The AWS CLI and Python SDK (boto3)
Virtual Compute with AWS EC2
Data Lake Storage on AWS S3
AWS RDS for Data Warehouse Storage

Module 11:
CI-CD

Github Actions

Specialisation 1. Data Engineering

Learn how to store, share, process various types of data at different scales.

Module 1:
Big Data Engineering Foundations

Introduction
Big data ecosystem overview
Batch vs real-time processing
Structured, unstructured and complex data
The data engineering lifecycle

Module 2:
Data Ingestion

Principles of data ingestion
Batch processing
Real-time data processing
Kafka
Flume

Module 3:
Data Management

Data Governance
Data Quality
Reference Data Management
Metadata Management
Challenges and Risks
Data Fabric
Data Quality and Cleaning
Data Enrichment
Big Data Privacy and Security

Module 4:
Data Wrangling and Transformation

ELT/ETL
SQL
NoSQL
Cassandra/MongoDB
Distributed Processing with Spark
Spark Streaming

Specialisation 2. Data Science

Learn to visualise, preprocess and model data with statistical tools and machine learning algorithms.

Module 1:
Data Cleaning and Exploratory Data Analysis

Data Visualisation
Multicollinearity
Influential points - Leverages and Outliers

Module 2:
Statistical testing

Significance testing
A/B Testing

Module 3:
Introduction to machine learning

Data for ML
Intro to models - Linear Regression
Validation and Testing
Gradient Based Optimisation
Bias and Variance
Hyperparameters, Grid Search and K-Fold Cross Validation

Module 4:
Classification

Binary Classification
Multiclass Classification
Multilabel Classification

Module 5:
Theory

Maximum Likelihood Estimation
Evaluation Metrics

Module 6:
Popular Supervised Models

K-Nearest Neighbours
Classification Trees
Support Vector Machines
Regression Trees

Module 7:
Ensembles

Ensembles
Random Forests and Bagging
Boosting and Adaboost
Gradient Boosting
XGBoost

Module 8:
Neural Networks

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

Specialisation 3. Machine Learning Engineering

Learn when and where machine learning models, including neural networks, are used within systems and how they are deployed.

Module 1:
Introduction to machine learning

Data for ML
Intro to models - Linear Regression
Validation and Testing
Gradient Based Optimisation
Bias and Variance
Hyperparameters, Grid Search and K-Fold Cross Validation

Module 2:
Classification

Binary Classification
Multiclass Classification
Multilabel Classification

Module 3:
PyTorch

Automatic differentiation
PyTorch Datasets and DataLoaders
Making custom datasets

Module 4:
Neural Networks

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

Module 5:
Practical

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

Module 6:
Applications

Churn modelling
Content based recommendation systems
Collaborative filtering
Lead scoring

Module 7:
Building APIs

Intro to FastAPI
Deploying FastAPI
Efficient FastAPI

Module 8:
Kubernetes (K8s)

KubeCTL
Workloads
Networking
Storage
StatefulSets
Deploying K8s

Module 9:
Kubeflow

Kubeflow Core
Kubeflow Serving
Kubeflow Pipelines

Module 10:
Apache Airflow

Scheduling with Airflow

Career support

Our curriculum is approved by
industry professionals

Federico Monti

Senior Machine Learning Engineer @ Twitter

Loïc Genest

Data Scientist @ Expedia

David Aponte

Machine Learning Engineer @ Benevolent AI

Daniel Clarke

Head of applied science @ Adarga

Aparna Dhinakaran

Founder @ Arize AI

Alexey Grigorev

Principal Data Scientist @ OLX Group

Tom Preece

Data Scientist @ Peak

Korri Jones

Senior Machine Learning Engineer @ Chick-fil-A

Jakub Czakon

Senior Data Scientist @ neptune.ai

Peter Bentley

Visiting professor @ Autodesk

Demetrios Brinkmann

Community Coordinator@ MLOps community

Tao Cao

Principal Machine Learning Engineer @ GSK

Vitalii Zhelezniak

Senior Research Scientist @ Babylon Health

Filipe Morais

Machine Learning Engineer @ Onfido

Mohamed Redha Sidoumou

Data Scientist @ AWS

Course schedule

Our course is part-time and runs from Monday - Thursday, 6:30 pm - 9:30 pm

6:30 - 7:30 pm

Lecture

Our expert instructors will walk you through new content and explain the underlying concepts

7:40 - 8:30 pm

Independent work

Work on challenges independently

8:40 - 9:30 pm

Group work

Work with your cohort in small groups

How to apply

Change your future in four easy steps

01

Application Form

Complete the application form in less than 10 minutes

02

Take our quiz

Find out if you are course-ready by taking a short assesment

03

Interview

Talk to our Admissions team to make sure this Course is right for you

04

Orientation

After the evaluation process, you will recieve a decision. Then get started!

Upcoming start dates

22nd November 2021

Part-time
18 weeks

Monday - Thursday
6.30PM - 9.30PM

Apply now

13th December 2021

Part-time
18 weeks

Monday - Thursday
6.30PM - 9.30PM

Apply now

24th January 2022

Part-time
18 weeks

Monday - Thursday
6.30PM - 9.30PM

Apply now

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.

Sam Cooper

Senior Lecturer,
Imperial College London

Felisia Loukou

Senior Data Scientist,
Adarga

Colin Kelly

NLP Lead,
Adarga

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
Ahmed Eldiwany

Ahmed has over 19 years of experience in senior roles at many companies in the field, including leading a team of over 20 as Principal Data Engineer at Emirates.

Harry Berg

Harry is a passionate teacher and hacker. Over the years, he has taught over 1000 students across the top universities in London including Imperial College, UCL and Kings.

Ivan Ying Xuan

Ivan got started working  with huge datasets in his Chemical Engineering PhD. During his time at AiCore, he's earned a reputation for being able to hack together complex demos in a matter of hours.

Ali Abdelaal

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

You could be a

data scientist.
machine learning engineer.
data analyst.
data engineer.
business analyst.
natural language processing engineer.
data analytics consultant.

Tuition plans that work for you

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 upfront

Pay the cost of the course before the date of your first class through a bank transfer.

AiCore Flagship

Pay the tuition cost in manageable monthly installments over 24 months.

No interest

No deposit

Income contingent plan

Pay tuition cost in monthly installments as a percentage of your salary after you've landed a high paying job.

Still have questions?

You could be our next
success story

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