Master in Data Strategy & Analytics
Madrid, Spain
DURATION
4 Months
LANGUAGES
Spanish
PACE
Full time, Part time
APPLICATION DEADLINE
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EARLIEST START DATE
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TUITION FEES
EUR 6,400 *
STUDY FORMAT
Blended
* * 50% SCHOLARSHIP for students residing in Latin America
Introduction
Extract data value from the first day
With the Master in Business Analytics you will learn from data pre-processing, probability and statistics, Data Scrapping, to the main Machine Learning algorithms. You will use tools such as Tensorflow, Numpy, Prophet, Spark, Pandas, Keras, etc. to be able to work with datasets, as well as Business Intelligence tools such as Qlikview and Tableau.
Career Opportunities
This is what your future is called
These are some of the most exciting career opportunities that will be within your reach after this program.
- Data Analyst
- Business Intelligence
- Business Analyst
- Data Manager
- Business Consultant
Curriculum
What you will learn in the Master in Business Analytics
Data storytelling Strategies to connect data analysis with business objectives, develop stories that connect with different types of audiences and methods of creatively presenting data. | Data Governance & Ethics We'll look at best practices for managing data, the full range of responsibilities that come with using data in automated decision making, including data security, privacy and transparency. |
Data Strategy & Analytics Data management to achieve analytical advantages and achieve our growth objectives. | BI Tools: Power BI, Qlikview, Tableau & Excel We will analyze data with an excellent visualization and presentation layer in an understandable, easy and intuitive format. |
Data Visualization How to display different types of data? What techniques to use? Use of matplotlib, bokeh and seaborn among others. | Data Analytics with Python Python as a framework for the Data Analytics specialist. Notebook development, use of pandas and numpy. Data processing from structured (CSV, REST, Logs) and unstructured (Web) sources. |
Data Science Fundamentals Introduction to fundamental data science concepts. Presentation of the general framework of reference. | Machine Learning & Deep Learning Classification problems. How to evaluate the results? How to build the datasets? Main algorithms (knn, decision trees, support vector machines, deep neural networks, xgboost). |
Data Pre-Processing How to properly pre-process the data? Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction. Preprocessing of data sources in text mode. | Final Project The topic may be proposed by the student or selected from a list provided by MIOTI. |
Databases & SQL Master the main databases and the SQL language, learn the latest techniques for storing, manipulating and extracting data recorded in relational databases. |
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Admissions
Program Tuition Fee
Scholarships and Funding
We have MIOTI scholarship plan.
We have scholarships available from the Universia Foundation.
We have scholarships available from the ONCE Foundation.
Bonusable by Fundae.
You can also split the payment without interest.