Module 1
Máster in Data Science
& Big Data
& Big Data
Data Science Fundamentals
Data Science Fundamentals
Introduction to data science. Presentation of the general framework.
Statistics for Data Science
Statistics for Data Science
Review of the fundamentals of statistics necessary to master data science.
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, HSQL, Logs) and unstructured (Web, Spark, Cassandra) sources.
Data Pre-processing
Data Pre-processing
How to properly pre-process data? Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.
Data Visualization
Data Visualization
How to visualize different types of data, which techniques to use?
Advanced Data Processing
Advanced Data Processing
Advanced Data Processing Data sources / ETL.
Batch processing architectures, streaming.
Databases (structured and unstructured).
Batch processing architectures, streaming.
Databases (structured and unstructured).
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 I
Machine Learning I
Introduction to classification and clustering problems. How to evaluate the results? How to build the datasets? Review of the main algorithms and their application.
Big Data Fundamentals
Big Data Fundamentals
Overview of the fundamental concepts of Big Data solutions. Reference architectures and adoption models will be reviewed with the main current technologies, including real-time data ingestion, analysis and visualization processes.
Entrepreneurship I
Entrepreneurship I
Discussion and discovery of new business models based on data science.
Module 1
Máster in Data Science
& Big Data
& Big Data
Data Science Fundamentals
Data Science Fundamentals
Introduction to data science. Presentation of the general framework.
Statistics for Data Science
Statistics for Data Science
Review of the fundamentals of statistics necessary to master data science.
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, HSQL, Logs) and unstructured (Web, Spark, Cassandra) sources.
Data Pre-processing
Data Pre-processing
How to properly pre-process data? Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.
Data Visualization
Data Visualization
How to visualize different types of data, which techniques to use?
Advanced Data Processing
Advanced Data Processing
Advanced Data Processing Data sources / ETL.
Batch processing architectures, streaming.
Databases (structured and unstructured).
Batch processing architectures, streaming.
Databases (structured and unstructured).
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 I
Machine Learning I
Introduction to classification and clustering problems. How to evaluate the results? How to build the datasets? Review of the main algorithms and their application.
Big Data Fundamentals
Big Data Fundamentals
Overview of the fundamental concepts of Big Data solutions. Reference architectures and adoption models will be reviewed with the main current technologies, including real-time data ingestion, analysis and visualization processes.
Entrepreneurship I
Entrepreneurship I
Discussion and discovery of new business models based on data science.
Module 2
Máster in Deep Learning
Deep learning
Deep learning
Fundamental concepts of deep neural networks. The most important tools are used and solutions are implemented.
Computer vision
Computer vision
Fundamental concepts of computer vision techniques. A theoretical and practical tour of the main techniques of the main techniques from basic processing filters to pattern recognition techniques using convolutional neural networks.
Natural language processing
Natural language processing
Fundamental concepts of the mechanisms used for communication communication between people and machines by means of natural language.
Entrepreneurship II
Entrepreneurship II
Global perspective of the process of creation, financing and possible success of a startup. Tools for entrepreneurship projects.
Machine learning II
Machine learning II
Unsupervised learning. Clustering methods, principal component selection.
Reinforcement Learning
Reinforcement Learning
Know the ways to calculate averages and moving averages, Markov decision processes, dynamic programming, approximation methods.
Machine learning III
Machine learning III
Advanced Machine Learning Techniques. Review of the current state of the art and the future of machine learning.
Reto Kaggle
Reto Kaggle
You will choose and develop a challenge to measure yourself against the best professionals in the world and evaluate what you have learned.
Final Project
Final Project
Development of a final project to put into practice with a real case the knowledge acquired during the Master.
Module 2
Máster in Deep Learning
Deep learning
Deep learning
Fundamental concepts of deep neural networks. The most important tools are used and solutions are implemented.
Computer vision
Computer vision
Fundamental concepts of computer vision techniques. A theoretical and practical tour of the main techniques of the main techniques from basic processing filters to pattern recognition techniques using convolutional neural networks.
Natural language processing
Natural language processing
Fundamental concepts of the mechanisms used for communication communication between people and machines by means of natural language.
Entrepreneurship II
Entrepreneurship II
Global perspective of the process of creation, financing and possible success of a startup. Tools for entrepreneurship projects.
Machine learning II
Machine learning II
Unsupervised learning. Clustering methods, principal component selection.
Reinforcement Learning
Reinforcement Learning
Know the ways to calculate averages and moving averages, Markov decision processes, dynamic programming, approximation methods.
Machine learning III
Machine learning III
Advanced Machine Learning Techniques. Review of the current state of the art and the future of machine learning.
Reto Kaggle
Reto Kaggle
You will choose and develop a challenge to measure yourself against the best professionals in the world and evaluate what you have learned.
Final Project
Final Project
Development of a final project to put into practice with a real case the knowledge acquired during the Master.
Diego García
CEO
Carlos Picazo
Co Founder, Strategy & Finance Leader
Alberto Rodriguez
Presidente
Fabiola Pérez
CEO
Oscar Fernández
Software Engineer for Data Visualization Solutions
Ernesto Padilla
Data Science Consultant
Daniel Montilla
Head of AI
Jesús Gómez
Artificial Intelligence Analyst
Víctor Vaquero
Data Scientist
See all
Train with professors from
leading companies
leading companies
Train with professors from
leading companies
leading companies
Diego García
CEO
Carlos Picazo
Co Founder, Strategy & Finance Leader
Alberto Rodriguez
Presidente
Fabiola Pérez
CEO
Oscar Fernández
Software Engineer for Data Visualization Solutions
Ernesto Padilla
Data Science Consultant
Daniel Montilla
Head of AI
Jesús Gómez
Artificial Intelligence Analyst
Víctor Vaquero
Data Scientist
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
Jesús Hernando
Dr. Grupo Ingeniería de Software
Ruben Zazo
Research Team Leader
See all
The tools you will
you will master
you will master
The tools you will
you will master
you will master
Next edition
Start date
March
2024
2024
Schedule
Tuesdays
18:30 - 22:30
Thursdays
18:30 - 22:30
18:30 - 22:30
Thursdays
18:30 - 22:30
Duration
10 Months
370 hours
370 hours
Seats
25 People
