Program on Big Data and Security
Big Data and Artificial Intelligence applied to the security of the organization
and the protection of the company's different assets
Today's leaders need the knowledge and confidence to ensure that their organizations are prepared to capitalize on the opportunities offered by Big Data and Artificial Intelligence, through the direct and indirect impact on employees, customers and the business ecosystem by successfully incorporating it into their main strategic lines. This program is designed for executives and professionals of companies who wish to be trained to enter the world of Big Data and Artificial Intelligence as professionals capable of managing the complete process, capturing and storing information, applying all data mining in the search of patterns of interest to the company, with a focus on the area of necessary security, both to protect company assets and to know what company information has been exposed on the Internet.
Candidates will be able to acquire the knowledge and analytical skills necessary to lead the organization's approach to security through analysis and data-driven decision making. We will explore different use cases where candidates will see the full potential of Big Data and Artificial Intelligence applied in a highly complex environment, such as the security of the organization and the protection of the different assets of the company, which will guarantee the future sustainability of the business: brand and reputation, industrial properties, competitive advantages, etc.
In collaboration with:
The lectures are taught by expert teachers with extensive professional experience that combine knowledge, experience and pedagogical vocation. In this way, the program is taught from a real perspective, and from close and specialized knowledge of business practice.
The syllabus includes information presented, in terms of form and content, in a way that is useful for its practical application, that allows understanding basic and specific concepts in the field of big data and cybersecurity, up-to-date and with the latest knowledge.
As a complement to the material, there will be "live" conferences with specialists, creating a space to share information, experiences, opinions and doubts on various topics. Likewise, these will remain on the platform to allow their later viewing.
Objectives
The participants will have the following objectives:
- Demystify the use of Big Data and Artificial Intelligence-
- Learn how to obtain value from data in the discovery of behavior patterns applied to the field of security.
- Learn to implement innovation processes through Artificial Intelligence in a successful way in order to protect company assets.
Through this program, participants will be able to:
- Know the techniques of Big Data and Artificial Intelligence for the analysis and massive data processing.
- Know the main AI tools that leading companies use today (and in the future).
- Discover the techniques and tools for effective AI design in your company with a focus on security.
- Know the types of AI and how they can generate value through data.
- Know the parallel processing systems for large volumes of data.
- Identify opportunities based on Big Data and AI with the potential to transform the business.
- Know how Big Data and AI allow one to find patterns of behavior that help identify security risks to prevent both unauthorized access to company assets, as well as knowing what company information is exposed in the Deep/Dark web.
- Know how to create an intelligence unit within the company.
- Identify the organizational models that favor a culture based on data and streamline development processes with the latest methodologies.
- Know the impact of Big Data and AI on the business, employees and products in order to minimize security risks.
Competences
At the end of this program, the student will have the necessary competences to:
- Establish best practices to capitalize on opportunities and capture value through Big Data and Artificial Intelligence.
- Evaluate the risks (ethical, data protection...) associated with the implementation of Big Data and AI projects.
- Know how Big Data and AI allow one to find patterns of behavior that help identify security risks to prevent both unauthorized access to company assets, as well as knowing what company information is exposed in the Deep/Dark web.
- Establish the appropriate guidelines to create a Data Driven organization.
- Determine the data needs of your company in order to monitor and protect its critical assets.
Course program Each module is equivalent to 15 hours with online methodology.
Module 1. Big Data and Artificial Intelligence Techniques
Know and understand the different techniques for data analysis and massive data processing.
Design the joint strategy of statistical techniques and artificial intelligence for the development of descriptive and predictive systems applied to the reality of a data set.
Identify techniques aimed at statistical analysis, artificial intelligence and massive data processing.
Index
- Basic concepts in data analysis and artificial intelligence
- Techniques for Evaluation and Selection of Data Models
- Machine Learning techniques
- Neural Networks
- Web Analytics
- Text Mining Techniques and Methods in Natural Language Processing (NLP)
- Analysis of Social Networks
Presentations
- Conference 1: Big Data and Artificial Intelligence. Past, present and future in use cases.
Module 2. Massive Data Analysis Tools
Know and understand the different techniques for data analysis and massive data processing
Design the joint strategy of statistical techniques and artificial intelligence for the development of descriptive and predictive systems applied to the reality of a data set
Identify techniques aimed at statistical analysis, artificial intelligence and massive data processing.
Index
- R and Python environment in the context of Data Science
- Static and Statistical graphics
- Data processing in different formats and different sources
- Cleaning and Preparation of Data to manage truthful data that give rise to precise conclusions
- Exploratory studies
- Decision Trees
- Classification and Association Rules
Module 3. Parallel Processing Systems for Big Data
Know the applicable AI techniques for the parallel processing of large volumes of data.
KKnow how to manage large volumes of data in a distributed manner.
KUnderstand the operation and characteristics of common massive data processing techniques.
KIdentify some of the commercial and free software tools oriented to statistical analysis, AI and massive data processing.
Index
- Conventional and unconventional databases
- Cloud, Fog and Edge Computing for distributed data management
- Large data ingestion tools
- Types of Parallels
- Data processing in streaming and real time
- Parallel processing: Hadoop
- Parallel processing: Spark
Module 4. Transversal Aspects of Big Data and AI
Identify the legal aspects of application related to the capture, storage and use of user data.
Know how to provide privacy in Big Data and AI.
Know how to provide anonymization mechanisms in Big Data and AI.
Anticipate the ethical risks and benefits derived from the application of Artificial Intelligence techniques that may occur in a real situation.
Index
- Application of GDPR in the treatment of large volumes of data
- Privacy in Big Data and AI
- Cybersecurity in Big Data and AI
- Identification, and identification in large volumes of Data
- Data Ethics
Presentations
- Conference 2: How to create an intelligence unit within a company
- Conference 3: Discovering the world of IoT. Understanding Shodan as a ‘work tool’.
Module 5. Data Sources and Analysis of Behavioral Patterns Using Big Data and AI
Provide an introduction to the Web as a massive source of real data based on searches carried out by users that can be used both in decision-making and in the search for patterns of behavior.
Analyze the technologies underlying the various Web systems.
Develop opensource intelligence solutions, exploiting available data sources.
Know the SQuID methodology and its applicability in the development of projects with large volumes of data
Index
- Data Sources on the Web
- Web data acquisition
- Tools for extracting data from the Web
- Semantic Web
- OSINT: Open Source Intelligence
- SQuID methodology to tackle projects with large volumes of data
Presentations
- Conference 4: Identification of work dictionaries in the SQuID methodology applied to the world of insurance.
- Conference 5: MasterLead. Predicting behaviors in online shopping.
Professors
Luis Galindo
Academic Director and Professor
Mónica Álvarez
Professor
José Lominchán
Professor
Julián Fernández
Professor