Bootcamp in Data Science

Bootcamp in Data Science
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On-site

Live Streaming

400h

September 2023

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información
Bootcamp in Data Science

On-site

Live Streaming

400h

September 2023

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Become a Data Scientist in 4 months
Solicita información

Currently the market requires young talent specialized in Data Science, capable of analyzing and interpreting data to achieve business objectives. If your goal is to specialize or become a Data Scientist, this Bootcamp will train you in 4 months to achieve your goals.

In MIOTI you will specialize in Data Science ranging from Python programming to the latest models of deep neural networks and computer vision. In addition, you will work with real datasets, so you will be trained with case studies of companies that will allow you to start applying your knowledge from the first day of your hiring.

At the end of the Bootcamp, you will have 2 weeks of Employer Days, where you will be able to meet MIOTI partner companies that are looking for profiles like yours to find the «match» and you can join their teams.

Why
Data Science?
Nº1

Data Scientist is the No. 1 job demand on the largest specialized job portal and will remain in that position for years to come.

Source: Glassdoor.
+650%

According to LinkedIn, there has been a 650% increase in data science jobs since 2012.

Source: LinkedIn.
Módulo 1
What you will learn in the Data Science Bootcamp
400h
Introduction
Introduction
Introduction to MIOTI, introduction to the platforms to be used during the program and initiation into the course.
Python for Beginners
Python for Beginners
Introduction to programming and preparation for its application in Data Science.
Data Science fundamentals
Data Science fundamentals
Introduction to fundamental concepts of data science. Presentation of the general frame of reference.
Data Science with Python
Data Science with Python
Python as a framework for Data Science specialist. Notebook development, use of pandas, numpy, matplotlib. Data processing from structured (CSV, REST, SQL, Logs) and unstructured (Web, Spark, Cassandra) sources.
Data Strategy
Data Strategy
Introduction to data management to achieve analytical advantages and achieve our growth objectives.
Statistics for Data Science
Statistics for Data Science
Review of the fundamentals of statistics needed to master data science.
Data Visualization
Data Visualization
Tools for data visualization. Introduction to the most used techniques and libraries.
Data Storytelling
Data Storytelling
Strategies for connecting data analytics to business objectives, developing stories that connect with different types of audiences, and methods of creatively presenting data.
Data Pre-processing
Data Pre-processing
How to adequately preprocess the data? Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.
Big Data for Data Science
Big Data for Data Science
Fundamental concepts of Big Data solutions. Reference architectures and adoption models with the main current technologies, including real-time data ingestion, analysis and visualization processes.
Soft Skills
Soft Skills
Professional experts will give a master class on how to present projects and public speaking and negotiation skills.
Predictive Analytics
Predictive Analytics
Introduction to time series analysis, review of the best available algorithms. Development of use cases for anomaly detection and series prediction.
Data Science for Business
Data Science for Business
Practical applications of AI for business, Algorithm Driven Companies, Skills Transformations, Data Driven Companies.
Machine Learning
Machine Learning
Introduction to classification and clustering problems. Construction of data sets and evaluation of results.
Machine Learning II
Machine Learning II
Review of the main supervised Bayes, support vector, regression, and unsupervised learning algorithms and their application. regressions, and unsupervised and their application.
Deep Learning
Deep Learning
Introduction of fundamental concepts of deep neural networks. Theoretical-practical walkthrough, learning to use the most important tools and to implement solutions from scratch antagonistic for data management.
Computer Vision
Computer Vision
Introduction to fundamental concepts of Computer Vision techniques. Theoretical and practical tour of the main techniques.
Natural Language Preprocessing
Natural Language Preprocessing
Introduction to fundamental concepts of the mechanisms used for communication between people and machines by means of natural language. Knowledge of interactions and their application in the field of artificial intelligence.
Machine Learning III
Machine Learning III
Application of convolutional networks and deep recurrent models such as TensorFlow in practical applications with images. Implementation and design of neural models for solving modeling/classification problems and design of GANs (generative antagonistic models) for data management.
antagonistic models) for data management.
Final Project
Final Project
Development of a final project to consolidate the knowledge acquired during the program.
Módulo 1
What you will learn in the Data Science Bootcamp
400h
Introduction
Introduction
Introduction to MIOTI, introduction to the platforms to be used during the program and initiation into the course.
Python for Beginners
Python for Beginners
Introduction to programming and preparation for its application in Data Science.
Data Science fundamentals
Data Science fundamentals
Introduction to fundamental concepts of data science. Presentation of the general frame of reference.
Data Science with Python
Data Science with Python
Python as a framework for Data Science specialist. Notebook development, use of pandas, numpy, matplotlib. Data processing from structured (CSV, REST, SQL, Logs) and unstructured (Web, Spark, Cassandra) sources.
Data Strategy
Data Strategy
Introduction to data management to achieve analytical advantages and achieve our growth objectives.
Statistics for Data Science
Statistics for Data Science
Review of the fundamentals of statistics needed to master data science.
Data Visualization
Data Visualization
Tools for data visualization. Introduction to the most used techniques and libraries.
Data Storytelling
Data Storytelling
Strategies for connecting data analytics to business objectives, developing stories that connect with different types of audiences, and methods of creatively presenting data.
Data Pre-processing
Data Pre-processing
How to adequately preprocess the data? Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.
Big Data for Data Science
Big Data for Data Science
Fundamental concepts of Big Data solutions. Reference architectures and adoption models with the main current technologies, including real-time data ingestion, analysis and visualization processes.
Soft Skills
Soft Skills
Professional experts will give a master class on how to present projects and public speaking and negotiation skills.
Predictive Analytics
Predictive Analytics
Introduction to time series analysis, review of the best available algorithms. Development of use cases for anomaly detection and series prediction.
Data Science for Business
Data Science for Business
Practical applications of AI for business, Algorithm Driven Companies, Skills Transformations, Data Driven Companies.
Machine Learning
Machine Learning
Introduction to classification and clustering problems. Construction of data sets and evaluation of results.
Machine Learning II
Machine Learning II
Review of the main supervised Bayes, support vector, regression, and unsupervised learning algorithms and their application. regressions, and unsupervised and their application.
Deep Learning
Deep Learning
Introduction of fundamental concepts of deep neural networks. Theoretical-practical walkthrough, learning to use the most important tools and to implement solutions from scratch antagonistic for data management.
Computer Vision
Computer Vision
Introduction to fundamental concepts of Computer Vision techniques. Theoretical and practical tour of the main techniques.
Natural Language Preprocessing
Natural Language Preprocessing
Introduction to fundamental concepts of the mechanisms used for communication between people and machines by means of natural language. Knowledge of interactions and their application in the field of artificial intelligence.
Machine Learning III
Machine Learning III
Application of convolutional networks and deep recurrent models such as TensorFlow in practical applications with images. Implementation and design of neural models for solving modeling/classification problems and design of GANs (generative antagonistic models) for data management.
antagonistic models) for data management.
Final Project
Final Project
Development of a final project to consolidate the knowledge acquired during the program.
Solicita información
Solicita admisión
Andrés Escribano
IoT Global Business Director
Carlos Picazo
Co Founder, Strategy & Finance Leader
Manuel Lopez
Machine Learning Researcher
David Gordo
Co-Founder
Alberto Rodriguez
Presidente TheCUBE
Diego García
Co Founder, Technology & Development Leader
Ana Belén Rueda
Advanced Analytics & Data Science Proyect Director
Alvaro Montero
Senior Business Analytics Consultant
Crisanto De Los Santos
CEO
Learn from professionals
from top-tier companies
Andrés Escribano
IoT Global Business Director
Carlos Picazo
Co Founder, Strategy & Finance Leader
Manuel Lopez
Machine Learning Researcher
David Gordo
Co-Founder
Alberto Rodriguez
Presidente TheCUBE
Diego García
Co Founder, Technology & Development Leader
Ana Belén Rueda
Advanced Analytics & Data Science Proyect Director
Alvaro Montero
Senior Business Analytics Consultant
Crisanto De Los Santos
CEO
Learn from professionals
from top-tier companies
Andrés Escribano
IoT Global Business Director
Carlos Picazo
Co Founder, Strategy & Finance Leader
Manuel Lopez
Machine Learning Researcher
David Gordo
Co-Founder
Alberto Rodriguez
Presidente TheCUBE
Diego García
Co Founder, Technology & Development Leader
Ana Belén Rueda
Advanced Analytics & Data Science Proyect Director
Alvaro Montero
Senior Business Analytics Consultant
Crisanto De Los Santos
CEO
Next
Edition
Fecha inicio
September
2023
Horario
From mondays to fridays
09:00 - 14:00

Challenges schedule
15:00 - 18:30
Duración
4 months
400 hours
Plaza
25 people
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«I like the variety of subjects offered by the Master. They are all very different and go quite deep into the world of Data Science. You receive a very personal treatment and you can tell that the professors are professionals in the sector and that they love teaching, they are always ready to solve doubts and make sure everything is clear».

«I was struck by the theme of practicing a lot. Here you learn
and I think that’s fundamental to really understand something. I’d like to take the subjects of data pre-processing, which I think is fundamental for our work, as well as those of predictive analytics and Machine Learnig, which are the ones you can get the most out of in the working world.»

Fernanda Casallas
Data Scientist Intern at Mercedes-Benz
Master in Data Science & Analytics Alumni
Francesco Matteazzi
Data Scientist Intern at Vithas
Master in Data Science & Analytics Alumni
“
Price &
Financing
Bootcamp in Data Science
400h
7.500€
Consult our
Scolarship plan
We have available
Scolarships from Fundación Universia
We have available
Scolarships from Fundación ONCE
Bonus available by
Fundae
It is also possible to split the
payment interest-free
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You are 3 steps away from converting your career into Data Science

You are 3 steps away from converting your career into Data Science

Step 1

Send us your CV
Let us know your profile to confirm that this is the course you need

Step 2

Interview
It is the opportunity to get to know each other and clarify all the doubts you have

Step 3

Admission
Our admissions committee will assess your candidacy and motivation
Solicita información
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Some of the professional outputs that will be at your reach:
Data Scientist
Data Engineer
Data Analyst
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