Subjects
Master in Data Science & Analytics
Introducción
Introducción
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
Introducción a conceptos fundamentales de data science. Presentación del marco de referencia general.
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.
Statistics for Data Science
Statistics for Data Science
Review of the fundamentals of statistics necessary to master data science.
Data Pre-processing
Data Pre-processing
How to properly preprocess data? Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.
Data Visualization
Data Visualization
Tools for data visualization. Introduction to the most used techniques and libraries.
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.
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.
Entrepreneurship
Entrepreneurship
Understanding of the new business models based on Data Science that are emerging in the business and industrial sector, and the techniques to implement ideas based on this technology.
Deep Learning
Deep Learning
Introduction of fundamental concepts of deep neural networks. Practical theoretical 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 Pre-processing
Natural Language Pre-processing
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.
Entrepreneurship II
Entrepreneurship II
To deepen in the new business models based on data science that are emerging in the business and industrial sector, and the techniques to implement ideas based on this technology.
Kaggle Challenge
Kaggle Challenge
You will choose and develop a challenge to measure yourself against the best professionals in the world and thus evaluate what you have learned during the master.
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 problem solving, modeling/classification and design of GANs (generative antagonistic models) for data management.
Reinforcement Learning
Reinforcement Learning
Introduction to reinforcement learning concepts. To know the ways of calculating averages and moving averages, Markov decision processes, dynamic programming, time difference learning and approximation methods.
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 ingestion processes, analysis and visualization of data in real time.
New Technologies
New Technologies
Initiation to Blockchain, Industry 4.0, Internet of Things and Robotics.
Data Science for Business
Data Science for Business
Practical applications of AI for business, Algorithm Driven Companies, Skills Transformations, Data Driven Companies.
Soft Skills
Soft Skills
Professional experts will give a master class on project presentation, public speaking and negotiation skills.
Project Management
Project Management
To know the phases of development and implementation of projects, identify those elements to be taken into account to facilitate the execution, minimizing the foreseeable incidences that are found in this type of projects.
Final Project
Final Project
Development of a final project to consolidate the knowledge acquired during the program.
Subjects
Master in Data Science & Analytics
Introducción
Introducción
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
Introducción a conceptos fundamentales de data science. Presentación del marco de referencia general.
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.
Statistics for Data Science
Statistics for Data Science
Review of the fundamentals of statistics necessary to master data science.
Data Pre-processing
Data Pre-processing
How to properly preprocess data? Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.
Data Visualization
Data Visualization
Tools for data visualization. Introduction to the most used techniques and libraries.
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.
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.
Entrepreneurship
Entrepreneurship
Understanding of the new business models based on Data Science that are emerging in the business and industrial sector, and the techniques to implement ideas based on this technology.
Deep Learning
Deep Learning
Introduction of fundamental concepts of deep neural networks. Practical theoretical 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 Pre-processing
Natural Language Pre-processing
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.
Entrepreneurship II
Entrepreneurship II
To deepen in the new business models based on data science that are emerging in the business and industrial sector, and the techniques to implement ideas based on this technology.
Kaggle Challenge
Kaggle Challenge
You will choose and develop a challenge to measure yourself against the best professionals in the world and thus evaluate what you have learned during the master.
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 problem solving, modeling/classification and design of GANs (generative antagonistic models) for data management.
Reinforcement Learning
Reinforcement Learning
Introduction to reinforcement learning concepts. To know the ways of calculating averages and moving averages, Markov decision processes, dynamic programming, time difference learning and approximation methods.
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 ingestion processes, analysis and visualization of data in real time.
New Technologies
New Technologies
Initiation to Blockchain, Industry 4.0, Internet of Things and Robotics.
Data Science for Business
Data Science for Business
Practical applications of AI for business, Algorithm Driven Companies, Skills Transformations, Data Driven Companies.
Soft Skills
Soft Skills
Professional experts will give a master class on project presentation, public speaking and negotiation skills.
Project Management
Project Management
To know the phases of development and implementation of projects, identify those elements to be taken into account to facilitate the execution, minimizing the foreseeable incidences that are found in this type of projects.
Final Project
Final Project
Development of a final project to consolidate the knowledge acquired during the program.
Paid internships included
With the Master in Data Science & Analytics you will put into practice what you have learned in class in companies such as P&G, Mercedes-Benz, Pelayo, Securitas Direct, Ferrovial, Exolum, Janssen or Unlimiteck, among others. You will have the opportunity to learn first-hand about real Data Science projects while you make your training profitable.
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Some of the companies
where you can do your internship
where you can do your internship
Some of the companies
where you can do your internship
where you can do your internship
Iván Pinar
Director of Operations
Diego García
CEO
Carlos Picazo
Co Founder, Strategy & Finance Leader
Alberto Rodriguez
Presidente
Fabiola Pérez
CEO
Angel Fombellida
Data Engineer
Oscar Fernández
Software Engineer for Data Visualization Solutions
David López
IoT/XR Solutions & Innovation Project Manager
Víctor Vaquero
Data Scientist
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Learn with professors from
leading companies
leading companies
Learn with professors from
leading companies
leading companies
Iván Pinar
Director of Operations
Diego García
CEO
Carlos Picazo
Co Founder, Strategy & Finance Leader
Alberto Rodriguez
Presidente
Fabiola Pérez
CEO
Angel Fombellida
Data Engineer
Oscar Fernández
Software Engineer for Data Visualization Solutions
David López
IoT/XR Solutions & Innovation Project Manager
Víctor Vaquero
Data Scientist
Victor Gallego
CEO
Sergio De Miguel
Executive Coach
Alvaro Montero
Head of Data
Andrés Haddad
CEO
Oscar Picazo
IT Specialist - Freelance
Crisanto De Los Santos
CEO
Manuel Lopez
Senior Deep Learning Scientist
David Gordo
Co-Founder
Andrés Escribano
IoT Global Business Director
Jesús Hernando
Dr. Grupo Ingeniería de Software
Ruben Zazo
Research Team Leader
Next
Edition
Edition
Start date
October
2023
2023
Schedule
Mondays
16:30 – 20:30
Wednesdays
16:30 – 20:30
Fridays
16:30 – 20:30
16:30 – 20:30
Wednesdays
16:30 – 20:30
Fridays
16:30 – 20:30
Duration
8 Months
400 Lective Hours
720 Internship Hours
400 Lective Hours
720 Internship Hours
Seats
25 people
