Data Science course

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.
课程具体信息 现在申请

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.

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 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.

A typical day at Le Wagon

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

  • 早上9点 讲座
  • 上午10:30 挑战
  • 下午4:30 瑜伽
  • 晚上5:30 实战代码
  • 晚上7点 活动 晚上8:30
讲座
课程讲座9:00AM - 10:30AM

每天早上喝一杯咖啡,开始互动性极强的讲座,然后把所学的内容付诸实践。

挑战
代码挑战10:30AM - 4:30PM

和其他学员结对,接下来的一天里在导师 助教的帮助下,共同应对系列编程挑战。

瑜伽
瑜伽4:30PM - 5:30PM

学习编程强度很大,因此学会在瑜伽环节休息和放松是很重要的。

实战代码
代码实战5:30PM - 7:00PM

复习当天的挑战内容,并在代码实战环节提前了解接下来的课程。

讲座&工作坊
讲座和工作坊7:00PM - 8:30PM

在我们举办的讲座和工作坊中,从成功创业者的分享中获得启发和有价值的建议。

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 6864 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!

终身受用的社群和工具

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 6864 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.

我们网页开发课程毕业的校友都受雇于行业最佳科技公司

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 雇用了6名校友
Lou Welgryn Jean Anquetil
+4
ContentSquare 雇用了1名校友
Jerome Vivier
Aircall 雇用了3名校友
Rhea Akiki Thomas Deschamps Manou Febvret
Doctolib 雇用了9名校友
Arthur Fulconis Renan Le Gall
+7
Google 雇用了4名校友
Jeroen Rutten Adrien De Villoutreys
+2
Frichti 雇用了1名校友
Tanguy Foujols

想要加入排名第一的编程训练营吗?

Join our 9-week Data science course.

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