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.
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 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.
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.
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).
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.
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.
Dalle lezioni al mattino fino ai workshop serali, le nostre giornate sono pienissime.
Prendi un caffè e inizia ogni mattina con una lezione coinvolgente e interattiva, prima di mettere in pratica ciò che hai imparato.
Lavora insieme ai tuoi compagni ed esercitati con la programmazione con l'aiuto dei docenti.
Imparare a programmare è molto intenso, quindi è importante fare una pausa e rilassarsi durante le nostre lezioni di yoga.
Riguarda le challenges del giorno e scopri le prossime lezioni durante le sessioni di live code.
Lasciati ispirare e ricevi preziosi consigli dagli imprenditori di successo nei nostri incontri e workshop esclusivi.
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.
Ottieni consigli da data scientist & data analyst professionisti e opportunità di lavoro esclusive da imprenditori e sviluppatori.
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.
Beneficia della nostra community globale di 8044 ex studenti che lavorano nel campo del data science, o come imprenditori, sviluppatori e product manager in tutto il mondo.
I nostri corsi hanno luogo in 39 città diverse in tutto il mondo: ovunque tu vada puoi contare sulla community di Le Wagon!
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.
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 8044 studenti.
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.
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.
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.
Le migliori aziende collaborano con Le Wagon e assumono i nostri studenti come Data Scientist, Data Analyst o Data Engineer.
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Scopri se hai diritto alle offerte speciali a Munich.
Le Wagon’s 9 weeks and 24 weeks Web development course is now counted as certified vocational training in Germany. This means that if you are registered and unemployed in Germany, you are eligible to receive a Bildungsgutschein (an education voucher) from the Bundesagentur für Arbeit or Job Center and take part in our course for free.
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Eligibility: Full time students/ azubis who are currently pursuing or have received their degree within the last year.
We offer students and azubis a 500€ discount on our Full Time Web Development Bootcamp. We believe in making technology more open and available in order to foster creativity and develop the skills that will in turn support Munich’s ecosystem.
With the Income Share Agreement, you can focus on the bootcamp, while CHANCEN eG takes care of your tuition fees. In exchange, you commit to affording future generations with the same opportunity. Once you start working in a company or found your own startup, you start making income-based repayments to finance the training fees of future generations.
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