Full-time (9 settimane)

In 9 settimane intensive, impara il Data Science da Python a Machine Learning avanzato, e ottieni tutte le competenze necessarie per unirti a un team di data science e dare una svolta alla tua carriera.

In 9 settimane intensive, impara il Data Science da Python fino al Machine Learning avanzato a Le Wagon.
Dettagli del corso

Iscriviti a un corso unico

Il nostro corso di data science full-time ti fornisce le competenze necessarie ad avviare una carriera in un team di data science in sole 9 settimane. Da Pandas al Deep Learning, finirai il corso sapendo come fare data cleaning, esplorare e trasformare dati in informazioni utili e implementare modelli di machine learning dall'inizio alla fine in un product environment, lavorando in team con i migliori strumenti a disposizione.

Il corso di Data Science di Le Wagon ti fornisce le competenze di data science necessarie per dare inizio a una carriera in qualsiasi ruolo del settore.

Il curriculum del nostro corso di data science

Il nostro corso di web development è strutturato in modo da permetterti di imparare a programmare passo per passo, dai data toolkit di base su Python e Matematica fino al completo ciclo di implementazione e lancio di algoritmi machine learning.

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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 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.

c pulse 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

speaker 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.

Una giornata tipica a Le Wagon

Dalle lezioni al mattino fino ai workshop serali, le nostre giornate sono pienissime.

  • 9:00 del mattino Lezioni
  • 10:30 del mattino Challenge
  • 4:30pm Yoga
  • 5:30pm Live-code
  • 19:00 Eventi 8:30pm
Lezioni9:00AM - 10:30AM

Prendi un caffè e inizia ogni mattina con una lezione coinvolgente e interattiva, prima di mettere in pratica ciò che hai imparato.

Le sfide
Sfide10:30AM - 4:30PM

Lavora insieme ai tuoi compagni ed esercitati con la programmazione con l'aiuto dei docenti.

Yoga4:30PM - 5:30PM

Imparare a programmare è molto intenso, quindi è importante fare una pausa e rilassarsi durante le nostre lezioni di yoga.

Live code
Live code5:30PM - 7:00PM

Riguarda le challenges del giorno e scopri le prossime lezioni durante le sessioni di live code.

Colloqui & Seminari
Talks & Workshops7:00PM - 8:30PM

Lasciati ispirare e ricevi preziosi consigli dagli imprenditori di successo nei nostri incontri e workshop esclusivi.

Piattaforma di networking e apprendimento

Il nostro corso di Data Science è solo l'inizio del tuo cammino. Una volta diplomato, farai parte di una community tech globale e avrai accesso alla nostra piattaforma online per continuare a imparare e a crescere.

Slack icon Gruppi su Slack

Ottieni consigli da data scientist & data analyst professionisti e opportunità di lavoro esclusive da imprenditori e sviluppatori.

Classe online

Ottieni accesso alla nostra piattaforma di apprendimento online quando vuoi dopo il corso: troverai tutte le lezioni di data science, gli screencast, le challenge e le flashcard.

Community tech

Beneficia della nostra community globale di 8042 ex studenti che lavorano nel campo del data science, o come imprenditori, sviluppatori e product manager in tutto il mondo.

Icon tutorials Presenza globale

I nostri corsi hanno luogo in 39 città diverse in tutto il mondo: ovunque tu vada puoi contare sulla community di Le Wagon!

Comunità e strumenti per la vita

Trova un lavoro nel campo del data science nelle migliori aziende tech

Una volta finito il corso, potrai beneficiare dei nostri servizi per la carriera. Ti metteremo in contatto con i migliori recruiter e gli ex studenti che lavorano nel settore.

microsoftwordCreated with Sketch. Career Playbook

Ottieni accesso a una guida completa su come dare il via alla tua carriera Data Science dopo il corso: migliora il tuo portfolio, trova il lavoro dei tuoi sogni, fai affidamento sulla nostra community di 8042 studenti.

myspaceCreated with Sketch. Career Events

Partecipa ai nostri eventi di networking e fiere del lavoro, incontra le migliori aziende tech e ricevi offerte di lavoro da recruiter in cerca di talenti in ruoli relativi al data science.

buymeacoffeeCreated with Sketch. Sessioni di Coaching

I nostri studenti del corso di data science amano condividere la loro esperienza dopo essersi diplomati: spiegano come hanno trovato lavoro come Data Scientist, Data Analyst o Data Engineer.

wechatCreated with Sketch. Career Intro

I nostri team locali conoscono i loro studenti e partner d'assunzione, cosa stanno facendo e cosa cercano. Ti presentano le persone adatte per raggiungere il tuo obiettivo.

I nostri studenti del corso di web development vengono assunti dalle migliori aziende.

Dove lavorano i nostri studenti nel campo del data science

Le migliori aziende collaborano con Le Wagon e assumono i nostri studenti come Data Scientist, Data Analyst o Data Engineer.

Frichti Ha assunto 1 studenti
Doctolib Ha assunto 9 studenti
Google Ha assunto 4 studenti
Getaround Ha assunto 6 studenti
Aircall Ha assunto 3 studenti
ContentSquare Ha assunto 1 studenti

Trova il piano di studi che fa per te

Scopri se hai diritto alle offerte speciali a Madrid.

Study first, pay later
Study first, pay later

Pay your tuition once you land a job

With the Income Share Agreement, you can focus on the bootcamp, while Student Finance takes care of your tuition fees.

1. Pay-for-success model: Student Finance takes care of your tuition cost until you are making at least €16,000 a year.

2. Monthly payments based on what you make: Once you are employed, you'll start to make monthly payments based on a percentage of your income, until you've reached the cap or the payment schedule ends.

Who's eligible? Legal Residents in Spain with European Passports

*Limited seats per batch

Learn more about this option

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