Part-time (24 weeks)
Coming soon

In 24 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.

In 24 weeks, part-time, learn Data Science from Python to advanced Machine Learning at Le Wagon.
コースの詳細

Join a unique course

Our part-time data science course gives you the skills you need to launch your career in a data science team, in 24 weeks studying some weekday evenings and Saturdays. 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.

Our data science course curriculum

Our Data Science course is designed to make you learn step by step, from the basic data toolkit in Python to implementing Machine Learning model in a production environment.

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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 analysis 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.

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 choose 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 methods that combine other models to improve performance, such as Random Forest or Gradient Boosting.

Machine Learning Pipeline

Move from Jupyter Notebook to a code editor and learn how to set up 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 and Airflow, you'll be able to train your model at scale, package it and make it available to the world.

Managing Images and Text data

Get comfortable with 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.

E-commerce project

Time to solve a real-life problem: "as a data scientist working for a major e-commerce company, how can I find interesting recommendations to improve our website's performance?". You'll learn how to structure a Python repository with object-oriented programming in order to collaborate efficiently, how to survive the data preparation phase of a vast dataset, how to find and interpret meaningful statistical results quickly before making advanced predictions, and how to explain your results to a non-technical audience thanks to cost/benefits analysis. You'll be working in group of 3-4 to share your progress, present and compare your results."

Student Projects

After this first e-commerce project of one week, you'll spend the next two weeks on a group project working on an exciting data science problem you want to solve! 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 practice all the tools, techniques and methodologies covered in the Data Science Course and will make you realize how autonomous you have become.

Our part-time format

Learn to code in 24 weeks with a tailor-made program adapted to your busy schedule.

09:00午前 05:00午後
07:00午後 10:00午後
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木曜日
金曜日
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日曜日
宿題 予習のために講義動画を見る
通学 講師のサポートのもと、クライメートとペアを組んでコーディングの課題に挑戦
通学 講師のサポートのもと、クライメートとペアを組んでコーディングの課題に挑戦
宿題 予習のために講義動画を見る
通学 1日かけて課題やプロジェクトに取り組みます。講師と一緒に進めるライブコードセッションで学んだことを復習しましょう。
☀️
🏠宿題

予習のために講義動画を見る

🏫通学

講師のサポートのもと、クライメートとペアを組んでコーディングの課題に挑戦

A taste of our program

Live a unique learning experience every week.

オンキャンパスで行うペアプログラミング・セッション
キャンパスで行うペアプログラミングセッション

週に3日、学生と講師が集まってコーディングの課題に取り組みます。ソフトウェア開発者のように考え、問題を解決する方法を学びます。

オンライン講義とフラッシュカード
オンライン講義とフラッシュカード

Le Wagonのオンライン学習プラットフォームを使い、講義動画を見て、自分のペースで学びましょう。コアコンセプトを理解し、次のコーディングセッションの予習をします。毎日フラッシュカードで勉強し、知識を自分の物にしていきます。

採用とネットワーキングイベント
採用とネットワーキングイベント

毎週開催している起業家や採用パートナーとのイベントに参加しましょう。活気付くテック業界で人脈を広げることができます。

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 6881 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 38 campuses all over the world: wherever you go, you belong to the Le Wagon community!

ずっと使えるツールとコミュニティー

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 6881 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.

ウェブ開発コースの卒業生は有力企業に採用されています

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.

Getaround 6 名の卒業生を採用
Lou Welgryn Jean Anquetil
+4
ContentSquare 1 名の卒業生を採用
Jerome Vivier
Aircall 3 名の卒業生を採用
Rhea Akiki Thomas Deschamps Manou Febvret
Doctolib 9 名の卒業生を採用
Arthur Fulconis Renan Le Gall
+7
Google 4 名の卒業生を採用
Jeroen Rutten Adrien De Villoutreys
+2
Frichti 1 名の卒業生を採用
Tanguy Foujols

Find the right tuition plan for you

Find out if you are eligible to special offers in リオデジャネイロ.

Up to 5 installments without any interest

You can choose to pay the bootcamp in up to 5 instalments without any interest, directly with us.

Up to 24 instalments with Provi, our financial partner
Up to 24 instalments with Provi, our financial partner

Provi is a fintech startup that believes in the future of tech skilled students and the promising career our coding bootcamp can offer them. With Provi, you do not need to provide any credit history or guaranty, just being accepted in our recruitment process :-)

Our partnership with Provi offers 3 instalment options: - 12 x R$ 1681 (interests of 1,89% per m) - 18 x R$ 1199 (interests of 1,99% per m) and - 24 x R$ 974 (interests of 2,19% per m).

How does it work? Apply to Le Wagon + Fill the form 👉 provi.com.br/lewagon/

Any questions? Contact our bootcamp manager! 👉 milene@lewagon.com

More information about Provi
Women in Tech 🦸‍♀️️ Scholarship

In an effort to increase the number of women in technology-related fields, we’re committing to help any self-identifying young women to pursue their dreams of becoming tech entrepreneurs, junior developers, and project managers.

We offer scholarships of R$ 1,225 to any women aspiring to be a Le Wagon student.

Discounts and instalments options are non-cumulative

Early Bird 🐣 Scholarship

Get a 5% discount by completing your application 2 months before the start of bootcamp.

5% discount by applying 2 months before the bootcamp

Completing your application means completing the interview and the 10-hour challenge Discounts and instalments options are non-cumulative

Want to go further?