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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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).
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.
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.
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.
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.
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).
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).
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.
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.
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."
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.
From morning lectures to evening talks, every day is action-packed.
Grab a coffee and start every morning with an engaging & interactive lecture, before putting what you’ve learnt into practice.
Pair up with your buddy for the day, and work on a series of programming challenges with the help of our teaching staff.
Learning to code is very intense, so it’s important to take a break and relax during our yoga classes.
Review the day’s challenges and get an overview of upcoming lessons during live code sessions.
Be inspired and get priceless advice from successful entrepreneurs invited for exclusive talks.
Since day one, we’ve taken teaching seriously. Great teachers inspire us to connect to topics on a profound level. Experience as a developer alone doesn’t necessarily make one an effective teacher — that’s why we’re passionate about finding not only great engineers, but deeply committed, experienced teachers.
With a wealth of background in astrophysics and algorithms from Cambridge University, Dounan is a true geek. He consults for early tech startups and social enterprises - coaching management, rescuing development, and building high-caliber tech teams.
Jason has experience in large enterprises, startups, and consulting. He was previously a data scientist at Quora, where he worked on user growth and content quality. He is interested in NLP technologies and product development.
more about JasonOur 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.
Get tips and advice from professional data scientists & data analysts, access exclusive job and freelance opportunities from entrepreneurs & developers.
Access our online education platform at any time after the course: you will find all data science lectures, screencasts, challenges and flashcards.
Benefit from our global community of 6893 alumni working in data-related roles, but also entrepreneurs, developers and product managers all over the world.
Our different courses are running in 38 campuses all over the world: wherever you go, you belong to the Le Wagon community!
Once the course ends, you benefit from our career services. We help you meet with the best recruiters and connect with relevant alumni.
Access a complete guide to kick-start your Data Science career after the course: boost your portfolio, find your dream job, leverage on our 6893 alumni community.
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.
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.
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.
The best companies partner with Le Wagon and hire our alumni as Data Scientist, Data Analyst or Data Engineer.
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If you got here, it means that you have questions about Le Wagon: how to apply, who can join, what you’ll learn… Good! We have the answers.
Central authorities, led by Ministry of Industry and Information Technology (MIIT) identified AI as the next economic mojo. The country has chosen Big Data to change millions of lives. Applications in facial recognition, voice recognition, autonomous driving are already rolling out at scale.
In 2019, China’s government put out its plan to lead the world in AI by 2030.
As Eric Schmidt (CEO of Google) has explained, “it’s pretty simple. By 2020, they will have caught up. By 2025, they will be better than us. By 2030, they will dominate the industries of AI.”
This context makes China the ultimate laboratory for any aspiring data scientist to learn.
Recommended book: AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee, an Artificial Intelligence pioneer, expert on China and a venture capitalist.
Next Shanghai data science course full-time coding bootcamp starts on Apr 20, 2020
