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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课程具体信息

最近营期
综述
课程
典型的一天行程
社群&工具
职业服务
招聘伙伴
资助选项

现在申请

即将开始的营期

10月 5, 2020－12月 4, 2020 课程语言为葡萄牙语 (R$17,500), 开放 - 现在申请

综述

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.

课程

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.

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.

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

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.

典型的一天行程

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

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。

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。

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。

社群&工具

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 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 7932 alumni working in data-related roles, but also entrepreneurs, developers and product managers all over the world.

Global presence

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

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.

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 7932 alumni community.

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.

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.

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.

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.

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资助选项

Find the right financing option for you

Find out if you are eligible for special offers in 圣保罗.

Up to 24 installments 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 warranty, just being accepted in our selection process :-)

Our partnership with Provi offers 3 installment options:
- 12x R$ 1,681 (monthly interest of 1.89%);
- 18x R$ 1,199 (monthly interest of 1.99%);
- 24x R$ 974 (monthly interest of 2.19%).

How does it work?
👉 Apply to Le Wagon on lew.ag/applyLWSP
👉 Fill the form: provi.com.br/le-wagon

More information about financing

Up to 3 installments without any interest

You can choose to pay the bootcamp in up to 3 installments without any interest, directly with us.

Co-founders

Enroll with a friend and if both are approved in the selection process, each of you receive 7% off.

Payment at once

We offer (not cumulatively with other discounts) a 5% discount if you pay at once.

Income Share Agreement - Only pay when (and if) you get a job!

Income Share Agreement is finally available in Brazil!

We've partnered with Provi once again, so everyone can become a coder!

How does it work?
You don't pay anything to start the bootcamp!
You only start paying back when (and if) you get a job with a salary bigger than R$ 3,000:
- You pay 15% if you make up to R$ 3,999;
- You pay 18% if you make more than R$ 4,000;
- You pay a maximum of 1.7x of the bootcamp price (R$29.750);
- You pay a maximum of 60 months.

That's it!

How can I enroll?
👉 Apply to Le Wagon on lew.ag/applyLWSP
👉 Fill the form: http://bit.ly/ISAprovi

As of today, ISA option is only available for Web Development Bootcamps, not Data Science yet.

More information about Income Share Agreement

Want to go further?

Join our 9-week 网页开发课程.

Join our 24-week 网页开发课程.

Join our 9-week Data science course.

Next 圣保罗 data science course full-time coding bootcamp starts on 10月 5, 2020