Data Science course in London

London
Full-time (9 weeks)

In 9 intensive weeks, learn Data Science from Python to advanced Machine Learning, get all the skills to join a Data Science team and boost your career.

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In 9 intensive weeks, learn Data Science from Python to advanced Machine Learning at Le Wagon London.
Course details Apply now
Upcoming batches

Some of our upcoming courses in London can be followed remotely. After you complete your application, the admission manager will be in touch shortly to give you all the necessary information.

Jul 6, 2020 - Sep 4, 2020 remote available Course in English (£6,900), Last seats - Apply now
Overview

Join a unique course

Our full-time data science course gives you the skills you need to launch your career in a data science team in only 9 weeks. From Pandas to Deep Learning, you will finish the course knowing how to explore, clean and transform data into actionable insights and how to implement machine learning models from start to finish in a production environment, working in teams with the best-in-class tool belt.

Le Wagon's Data Science course gives you the data science skills you need to launch your career in any data-related role.
Curriculum

Our data science course curriculum

Our course is designed to make you learn Data Science step by step, starting with the basic data toolkit in Python and Mathematics to the complete implementation and deployment cycle of machine learning algorithms.

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Start the bootcamp prepared!

Our data science course is very intense. To save time and nail it from the beginning, our students must complete an online preparation work before starting the bootcamp. This work takes around 40 hours and covers the basics of Python, the pre-requisite language of the course, and some mathematical topics used every day by data scientists.

Python for Data Science

Learn programming in Python, how to work with Jupyter Notebook and to use powerful Python libraries like Pandas and NumPy to explore and analyze big data sets. Collect data from various sources, including CSV files, SQL queries on relational databases, Google Big Query, APIs and Web scraping.

Relational Database & SQL

Learn how to formulate a good question and how to answer it by building the right SQL query. This module will cover schema architecture and then dive deep into the advanced manipulation of SELECT to extract useful information from a stand-alone database or using a SQL client software like DBeaver.

Data Visualization

Make your data analyses more visual and understandable by including data visualizations in your Notebook. Learn how to plot your data frames using Python libraries such as matplotlib and seaborn and transform your data into actionable insights.

Statistics, Probability, Linear Algebra

Understand the underlying math behind all the libraries and models used in the bootcamp. Become comfortable with the basic concepts of statistics & probabilities (mean, variance, random variable, Bayes’s Theorem, etc.) and with matrix computation, at the core of numerical operations in libraries like Pandas and Numpy.

c pulse Statistical inferences

You'll learn how to structure a Python repository with object-oriented programming in order to clean your code and make it re-usable, how to survive the data preparation phase of a vast dataset, and how to find and interpret meaningful statistical results based on multivariate regression models

speaker Communication

Data analysts are meant to communicate their findings to non-technical audiences: You will learn how to create impact by explaining your technical insights and turn them into business decisions using cost/benefits analysis. You'll be able to share your progress, present and compare your results to your teammates.

Preprocessing and Supervised Learning

Learn how to explore, clean, and prepare your dataset through preprocessing techniques like vectorization. Get familiar with the classic models of supervised learning - linear and logistic regressions. Learn how to solve prediction and classification tasks with the Python library scikit-learn using learning algorithms like KNN (k-nearest neighbors).

Generalization and Overfitting

Implement training and testing phases to make sure your model can be generalised to unseen data and deployed in production with predictable accuracy. Learn how to prevent overfitting using regularization methods and how to chose the right loss function to improve your model's accuracy.

Performance Metrics

Evaluate your model's performance by defining what to optimise and the right error metrics in order to assess your business impact. Improve your model's performance with validation methods such as cross validation or hyperparameter tuning. Finally, discover a powerful supervised learning method called SVM (Support Vector Machines).

Unsupervised Learning & Advanced Methods

Move to unsupervised learning and implement methods like PCA for dimensionality reduction or clustering for discovering groups in a data set. Complete your toolbelt with ensemble method that combine other models to improve performance, such as Random Forest or Gradient Boosting.

Managing Images and Text data

Get comfortable into managing high-dimensional variables and transforming them into manageable input. Learn classic preprocessing techniques for images like normalization, standardization and whitening. Apply the right type of encodings to prepare your text data for different NLP tasks (Natural Language Processing).

Neural Networks

Understand the architecture of neural networks (neurons, layers, stacks) and their parameters (activation functions, loss function, optimizer). Become autonomous to build your own networks like Convolutional Neural Networks (for images), Recurrent Neural Networks (for time-series) and Natural Language Processing networks (for text).

Deep Learning with Keras

Discover a new library called keras, which is a developer-friendly wrapper over tensorflow, a Deep Learning library created by Google. We'll teach you the fundamental techniques to build your first deep learning model with Keras.

Computer Vision

Go further into computer vision with Deep Learning building networks for object detection and recognition. Implement advanced techniques like data augmentation to augment your training set by computing image perturbations (random crops, intensity changes etc) in order to improve your model's generalization.

Machine Learning Pipeline

Move from Jupyter Notebook to a code editor and learn how to setup a machine learning project in the right way in order to quickly and confidently iterate. Learn how to convert a machine learning model into a model with a robust and scalable pipeline with sklearn-pipeline using encoders and transformers.

Machine Learning workflow with MLflow

Building a machine learning model from start to finish requires a lot of data preparation, experimentation, iteration and tuning. We'll teach you how to do your feature engineering and hyperparameter tuning in order to build the best model. For this, we will leverage a library called MLflow.

Deploying to production with Google Cloud Platform

Finally, we'll show you how to deploy your code and model to production. Using Google Cloud AI Platform, you'll be able to train your model at scale, package it and make it available to the world. Cherry on top, you will use a Docker environment to deploy your own RESTful Flask API which could be plugged to any front-end interface.

Student Projects

You'll spend the last two weeks on a group project working on an exciting data science problem you want to solve! As a team, you'll learn how to collaborate efficiently on a real data science project through a common Python repository and the Git flow. You will use a mix of your own datasets (if you have any from your company / non-profit organisation) and open-data repositories (Government initiatives, Kaggle, etc.). It will be a great way to practise all the tools, techniques and methodologies covered in the Data Science Course and will make you realize how autonomous you have become.

Typical day

A typical day at Le Wagon

From morning lectures to evening talks, every day is action-packed.

Lectures
Lectures9:00AM - 10:30AM

Grab a coffee and start every morning with an engaging & interactive lecture, before putting what you’ve learnt into practice.

Challenges
Challenges10:30AM - 4:30PM

Pair up with your buddy for the day, and work on a series of programming challenges with the help of our teaching staff.

Yoga
Yoga4:30PM - 5:30PM

Learning to code is very intense, so it’s important to take a break and relax during our yoga classes.

Live code
Live code5:30PM - 7:00PM

Review the day’s challenges and get an overview of upcoming lessons during live code sessions.

Talks & Workshops
Talks & Workshops7:00PM - 8:30PM

Be inspired and get priceless advice from successful entrepreneurs invited for exclusive talks.

Community & tools

Network and learning platform

Our Data Science course is just the beginning of the journey. Once you graduate, you belong to a global tech community and have access to our online platform to keep learning and growing.

Slack icon Slack groups

Get tips and advice from professional data scientists & data analysts, access exclusive job and freelance opportunities from entrepreneurs & developers.

Online classroom

Access our online education platform at any time after the course: you will find all data science lectures, screencasts, challenges and flashcards.

Tech community

Benefit from our global community of 7466 alumni working in data-related roles, but also entrepreneurs, developers and product managers all over the world.

Icon tutorials Global presence

Our different courses are running in 39 campuses all over the world: wherever you go, you belong to the Le Wagon community!

Community and tools for life
Career Services

Find a data job in the best tech companies

Once the course ends, you benefit from our career services. We help you meet with the best recruiters and connect with relevant alumni.

microsoftwordCreated with Sketch. Career Playbook

Access a complete guide to kick-start your Data Science career after the course: boost your portfolio, find your dream job, leverage on our 7466 alumni community.

myspaceCreated with Sketch. Career Events

Attend our job fairs and networking events, meet with the best tech companies and receive offers by recruiters looking for talent in data-related roles.

buymeacoffeeCreated with Sketch. Alumni Coaching Sessions

Our data science course alumni love to share their experiences with fresh graduates: they explain how they found their job as Data Scientist, Data Analyst or Data Engineer.

wechatCreated with Sketch. Career Intros

Our local teams know their alumni and hiring partners, what they are up to and what they are looking for. They introduce you to the right people depending on your goal.

Our web development course alumni get hired by the best companies
Hiring Partners

Where our alumni work in data

The best companies partner with Le Wagon and hire our alumni as Data Scientist, Data Analyst or Data Engineer.

Frichti Hired 1 graduates
Doctolib Hired 9 graduates
+7
Google Hired 4 graduates
+2
Getaround Hired 6 graduates
+4
Aircall Hired 3 graduates
+1
ContentSquare Hired 1 graduates
Financing options

Find the right financing option for you

Find out if you are eligible for special offers in London.

Options For Paying Tuition.
Options For Paying Tuition.

There are two payment options available: you can choose to pay upfront, or you can pay your tuition in instalments*, where the deposit is paid to secure your seat on the course and then the remaining balance can be paid over 6 monthly instalments (there is a management fee associated with this option).

Web Development

Upfront: £6,500. A deposit of £2,500 is due upon signature of the contract, and the remaining balance of £4,000 is paid 1 week before the course start date.

Instalments: £7,000. A deposit of £2,500 is due upon signature of the contract, and the remaining balance is paid in 6 monthly instalments of £750 each, starting one week before the course start date.

Data Science

Upfront: £6,900. A deposit of £2,500 is due upon signature of the contract, and the remaining balance of £4,400 is paid 1 week before the course start date.

Instalments: £7,400. A deposit of £2,500 is due upon signature of the contract, and the remaining balance is paid in 6 monthly instalments of £816.70 each, starting one week before the course start date.

*To be eligible for the instalment plan you must: be over 18 years old; be a European Citizen; have a UK Bank Account; not have any convictions for financial crimes; successfully pass the credit check; and be successfully enrolled in the Training Programme.

Want to go further?

Next London data science course full-time coding bootcamp starts on Jul 6, 2020

Apply now