Official Master's Degree in Big Data Science
More and more companies and organisations require professionals with knowledge of Big Data and Data Science data. The Master's Degree Executive in Big Data Science is an officialdegree scroll , born as a response to this demand: it offers a technical and specific programme, and at the same time, very practical.
The academic staff comes from various prestigious companies and institutions, with extensive experience in these tools. Students are taught how to use these tools in real situations and how to apply them in various fields.
The Executive format, with face-to-face sessions on Friday afternoons and Saturday mornings, makes it easier to combine professional performance with attendance at class, and to promote networking among professionals and companies.
Professional Certification as a Scientist of data
The Professional Scientist Certification from data is a test that enables professionals in the science industry from data:
-
Validate your knowledge and skills on the discipline science of the data.
-
Differentiate yourself from other professionals through a prestigious professional certification.
The Certification has as mission statement to define a standard in the field of science of data through which organizations can identify professionals with mastery of the field of advanced analytics. In addition, the Certification reinforces the credibility and visibility of the data scientist profession.
It consists of two clearly differentiated parts that will be addressed sequentially:
Part I
Use case review and work experience
Part II
Examination
Areas of knowledge evaluated
The Certification measures performance in 5 specific areas:
-
Problem formulation in the framework of the science of data
-
Machine Learning
-
Statistics
-
Mathematics
-
Programming
Microcourses
The Institute of Data Science and Artificial Intelligence offers the following advanced training courses:
Data Management Tools (6 ECTS credit)
2 ECTS CREDIT
Data management with Python
-
Installation of the working environment
-
Reading data files in different formats
-
Data manipulation: numpy and pandas
-
Mathematical operations
-
Web Scraping
2 ECTS CREDIT
Data management with R
-
Installation of the working environment
-
Reading data files in different formats
-
Data manipulation
-
Presentation of results: Rmarkdown and Shiny
-
Web Scraping
2 ECTS CREDIT
Spreadsheets for data analysis
-
Formulas
-
Data cleansing
-
Pivot tables, macros
-
Dashboards
-
Web Scraping
Data Science Techniques (8 ECTS credit)
2 ECTS CREDIT
Statistics of data science
-
Basic concepts of probability and most commonly used statistical distributions
-
Parametric contrasts for two populations
-
Non-parametric contrasts for two populations
-
ANOVA and non-parametric tests
-
Simple linear and multiple logistic regression
2 ECTS CREDIT
Social network analysis
-
Data mining and descriptive statistics
-
Segment and community detection
-
Virality detection
-
Sentiment analysis
2 ECTS CREDIT
Data visualisation
-
Software in use: PowerBI, Google Data, Studio
-
Graph types and their use: heat maps, sector and bar charts
-
Control panels
2 ECTS CREDIT
Machine learning
-
Terminology
-
Supervised methods: Decision trees, Random Forest, Variable selection, Neural networks.
-
Unsupervised models: k-means, kNN, LDA, neural networks, association rules
The need for professionals from any discipline to enter the job market, including research, with knowledge and skills in handling data for value creation has become increasingly relevant. Therefore, this programme is offered to allow professionals from all degrees, even those coming from clearly related areas such as mathematics and statistics, to update and gain some Degree in the use of the main methodologies and tools available in the market to solve problems associated with data management and analysis.
This programme is framed in the rules and regulations of the University of Navarra's own teaching of 4 September 2017. By which these programmes are carried out in the online modality under the typology of Higher Education Course.
|
Data management with Python |
Data management with R |
Spreadsheets for data analysis |
|
2 ECTS CREDIT |
2 ECTS CREDIT |
2 ECTS CREDIT |
|
Installation of the working environment |
Installation of the working environment |
Formulas |
|
Reading data files in different formats |
Reading data files in different formats |
Data cleansing |
|
Data manipulation: numpy and pandas |
Data manipulation |
Pivot tables, macros |
|
Mathematical operations |
Presentation of results: Rmarkdown and Shiny |
Dashboards |
|
Web Scraping |
Web Scraping |
Web Scraping |
|
Statistics of data science |
Machine learning |
Visualisation |
Social network analysis |
|
2 ECTS CREDIT |
2 ECTS CREDIT |
2 ECTS CREDIT |
2 ECTS CREDIT |
|
Basic concepts of probability and most commonly used statistical distributions |
Terminology |
Software in use: PowerBI, Google Data, Studio |
Data mining and descriptive statistics |
|
Parametric contrasts for two populations |
Supervised methods: Decision Trees, Random Forest, Variable Selection, Neural Networks |
Graph types and their use: heat maps, sector and bar charts |
Segment and community detection |
|
Non-parametric contrasts for two populations |
Unsupervised models: k-means, kNN, LDA, neural networks, association rules |
Control panels |
Virality detection |
|
ANOVA and non-parametric tests |
|
|
Sentiment analysis |
|
Simple linear and multiple logistic regression |
|
|
|
The courses will be delivered synchronously online. Each course will have a duration of 3 weeks, with 2 hours of theoretical and practical classes followed by one hour of tutoring. A table with a breakdown of the study hours is attached at student.
|
Activity |
Mark |
Synchronous classes (h) |
Synchronous tutorials (h) |
Independent work (h) |
|
Session 1 |
15 |
2 |
|
4 |
|
Session 2 |
15 |
2 |
1 |
4 |
|
Session 3 |
15 |
2 |
1 |
4 |
|
Session 4 |
15 |
2 |
1 |
4 |
|
Session 5 |
15 |
2 |
1 |
4 |
|
Final work |
25 |
|
|
16 |
|
Total |
100 |
10 |
4 |
36 |
All explanation sessions will be conducted in the Zoom application and recorded and made available to the students.
As complementary material, scientific articles or popular science videos, both theoretical and applied, will be used.
The evaluation will be done after each session with a work of implementation of the knowledge acquired, each equivalent to 15% of the final grade . And a final paper equivalent to 25% of the final grade .
Undergraduate or Master's students interested in entering the area of Data Science with basic knowledge of computers and spreadsheets.
They must have a computer, preferably a Windows PC, with the ability to install R and Python software.
In Company Programmes
Programming of tailor-made courses, workshops and seminars for companies, research groups, services or centres looking for updating and specialisation on data analysis topics related to real needs.
It is also possible to design introductory or specialised software courses.
Examples of courses already delivered:
- Specific workshop on design and validation of surveys for people in a hospital quality unit.
- Course for a hospital department focusing on survival analysis and sizing sample.
- Introductory workshop on R software.
Other programmes
Artificial intelligence has the power to transform global businesses by offering new sources of advantage and innovative business models. At the same time, it unleashes the disruption that will determine which players will ultimately capture value. IESE Business School offers executives the focused programme"Artificial Intelligence for Executives" where they can discover the power of Artificial Intelligence as a catalyst for innovation, growth and competitive advantage.
