In 9 intensive weeks in Montreal, learn Data Science from Python to advanced Machine Learning, get all the skills to join a Data Science team and boost your career.
Some of our upcoming courses in Montreal can be followed remotely. After you complete your application, the admission manager will be in touch shortly to give you all the necessary information.
Our full-time Data Science in Montreal 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 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.
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.
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
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.
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 chose 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 method that combine other models to improve performance, such as Random Forest or Gradient Boosting.
Unveil the magic behind Deep Learning by understanding the architecture of neural networks (neurons, layers, stacks) and their parameters (activations, losses, optimizers). Become autonomous to build your own networks, especially to work with images, times and text, while learning the techniques and tricks that make Deep Learning work.
Go further into computer vision with Convolutional Neural Networks, architectures designed to take the most out of images. Improve your model generalization thanks to data augmentation techniques and implement advanced methods to benefit from state-of-the-art architectures thanks to Transfer learning methods.
Get comfortable into managing sequential data and text (sequence of words) by transforming them into appropriate inputs. Leverage the power of Recurrent Neural Networks to forecast future values and perform valuable Natural Language Processing.
Discover the Keras Deep Learning library which enables to prototype easily while having the flexibility to tune precisely your neural network. Moreover, Google Colab will greatly speed up the computational time thanks to dedicated GPUs.
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.
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, 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.
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.
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, which run at least once a week.
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.
A data science consultant with 10+ years in the field, Song helps companies build big data platforms and formulate their data strategies. With advanced degrees in economics and engineering, he's interested in financial and IoT data applications.
more about Song
Head of Research at Dataperformers Inc, Viral has a background in academics, research & applied engineering. A PhD candidate, he is working towards development and improvement of core AI and machine learning algorithms for computer vision.
more about Viral
A Data Scientist working at Local Logic, Guy holds a Bachelor of Science and a Master in Biomedical Engineering from Ben Gurion University & McGill University respectively, where his focus was signal processing & object tracking in video recordings.
more about Guy
Lucas is a battle-tested data nerd with over 10 years of practical experience in Data Science, acquired solving real-life problems using data at large companies such as Dell and National Bank of Canada, as well as his own business ventures.
more about LucasOur 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 10,620 alumni working in data-related roles, but also entrepreneurs, developers and product managers all over the world.
Our different courses are running in 43 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 10,620 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.
Hired 6 graduates
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Hired 9 graduates
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Le Wagon has 1956 reviews with an average grade of 4.98/5 which makes it the most acclaimed coding bootcamp worldwide on Switchup according to student reviews! Whilst these reviews make us proud, above all they make us happy. Behind these reviews are hundreds of people around the world who have had a life-changing experience joining our program, that has opened new doors for them. We feel blessed and honoured to have enabled them - and to keep enabling our students - to become autonomous in coding, upskill or change their career, or launch their own startups.
Also, these 1956 reviews are extremely important for us to make sure we’re always meeting the same level of excellence. Having such positive and enthusiastic reviews about our bootcamps is the ultimate proof that we provide the best tech education for all our students, in the 43 cities we run our coding bootcamps in.
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.
If you’ll be attending the 9-week full-time bootcamp, simply entering Canada as a visitor with a valid passport is usually enough. That said, some people may require a visa or an Electronic Travel Authorization. While we don’t formally assist students with acquiring visas, we’re happy to point you in the right direction. Here’s some useful information to help find the travel documents you need.
If you’re planning on attending the 24-week part-time program, make sure to check (and double check!) whether you’re authorized to stay in Canada for the program’s entirety.
We have a partnership with a financial institution (Caisse Desjardins des TI), that offers low-interest student loans and flexible payment options. This option is available for all students who are approved in the first steps of our selection process and are Canadian citizens or residents.
You can also withdraw from your RESPs or RRSPs (for use in the LLP) to pay for your tuition. Here is more info for using your RESP and to participate in the LLP.
4 ways of financing Le Wagon Montréal’s coding bootcamp
Further Readings: The reasons why to use RRSP to go back to school.
The cost of living might vary a lot according to your lifestyle. You can find here more information about the neighbourhood we are located at and also some links with apartments for rent - some of them are suggestions from our alumni.
We do support all our students in finding the right career path after the bootcamp, sharing them all the necessary resources to build the best LinkedIn profile, getting the best out of the alumni Slack groups, enjoy our exclusive perks, land their first job as developers, product managers or in a data team, launch their startup or start their freelancing career.
We organize four career weeks a year for our alumni that include inspiring talks, networking with alumni and job fairs. See one of our career week' s video
However, we consider that the ability of each student to find a position after the bootcamp is highly based on the student's motivation and work. Guaranteeing 100% employment at the end of the bootcamp would be dishonest in that regard. When choosing a bootcamp, what you should focus on is the quality of the curriculum and teachers. You should ask yourself about the skills you will have at the end and the product you will be able to build. This is what will really make you hireable.
Keep in mind that Le Wagon has an excellent network within the tech industry and a very good reputation with recruiters. Our students massively benefit from this. Moreover, we now have thousands of alumni worldwide. This internal network is your first support when looking for a job: our alumni work for the best tech companies out there and can recommend you for a position, and they'll also be available to support you during the search - by answering technical or non-technical questions.
We have hundreds of alumni who graduated from one of our batches in Montréal, most of them living in the city. After Le Wagon, the majority found a new job (either as a product manager or developer). A part of them are working as freelancers now and around 10% of them are entrepreneurs. It’s a rich, diverse and vibrant community <3
If you want to know more about them, you could also check some of our inspiring alumni stories or read the outcomes of our coding bootcamp
If you already have a work permit and are authorized to work in Canada, it will make the job search process faster. If it is not the case yet, find more information about different types of work visas. You could also check eligibility to immigration programs, such as Express Entry or Quebec Immigration Programs.
But again, as we mentioned before, to attend the full-time bootcamp you only need to enter to Canada as a visitor :)
Your tuition is tax deductible. We'll issue a T2202A for you to claim your tuition for the program on your tax return. For student living in Québec, we will also issue a receipt for your tax filing at Revenu Québec.
For local or national students: e-transfer (Interac), bank transfer, or cheque. For international students: Transferwise is a good option or World Remit.
For more questions & answers, you can visit our FAQ section! If you would like to discuss with Le Wagon team, don't hesitate to contact us.
Next Montreal Data Science course (full-time coding bootcamp) starts on Jul 5, 2021
