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Master Propio in

Information Technology Management

Teaching structure

[1]​Clinical Practices: 1 ECTS for conducting or supervising clinical practices is equivalent to between 20 and 22.5 hours, indicate hours in the adjacent cell: 20.

MODULE
CREDITS ECTS
HS. OF 
TEACHING ACTIVITIES
MODALITY

* Total number of ECTS credits: 60

* Total hs. of teaching activities: 232

Program schedule

Module 1: Strategic Management / Management of technological innovation

- The nature of strategic direction
- External analysis of the company

- Internal analysis of the company

- Formulation of the strategies
- Evaluation and selection of strategies
- Implementation of the strategies
- Strategic direction of technology and innovation
- The innovation process 

- Strategies for exploiting technological potential

- Open innovation

Specific competences of Module 1:

  • To know how to apply the strategic management model of the company.

  • To apply external strategic analysis tools to detect opportunities and threats.

  • To know how to carry out an internal strategic analysis to detect strengths and weaknesses of the organization.

  • To know how to formulate strategies at different levels: corporate, competitive and functional.

  • To know the problems and implications of the implementation of the strategy.

  • To distinguish between science, technology, innovation and R&D, their main characteristics and the different types of technology and innovation, as well as their impact on the economy and the company.

  • To know the model of Strategic Management of Technology and Innovation as an instrument for the formulation and implementation of technological strategies.

    To know the basic components of the national innovation systems and the main tools for the analysis of the technological environment and the technological potential of the company.

    To know what legal tools there are to protect creativity and innovations and how they work and what other strategies can be followed to take advantage of those property rights or fight against those of the competition.

  • To know how to identify the technological exploitation and obtaining strategies that companies can follow, what advantages and disadvantages they have and in what circumstances they are most appropriate.

Module 2: Digital marketing

- Off-line marketing and digital marketing
- SEM /SEO strategies

- Social media

- Web Analytics  / Inbound marketing
- Marketing automation
- Mobile marketing
- CRM management
- User Experience
- Google ads, Wordpress, Google Analytics

Specific competences of Module 2:

  • To apply the tools and strategies of Online Marketing to the business world.

  • To design and develop a Digital Marketing Plan.

  • To use digital tools to position the company.

Module 3: Cybersecurity

- Threats, cybercrime and security
- Computer forensics

- Reverse engineering processes and tools

- Concepts and tools on network management for cyberdefense
- Advanced malware types and features and persistent threats
- Management of vulnerabilities at the software, network and web level

Specific competences of Module 3:

  • To know the risks of computer vulnerabilities.

  • To know techniques for analyzing and preserving evidence on a computer device, particularly after an attack.

  • To manage the most common cyberdefense tools.

  • To manage techniques to detect malware and advanced persistent threats.

Module 4: Data Analytics and Visualization

- Data cleaning and data handling
- APIs and web scraping

- Git, SQL and Python

- The use of Python in the fundamentals of business intelligence
- Workflow of machine learning
- Basics of machine learning algorithms

Specific competences of Module 4:

  • To know, identify and select the appropriate information sources for analysis.

  • To know the techniques for the extraction of information, prepare and refine the information available for subsequent data analysis.

  • To perform data analysis with real data sets.

  • To visualize and analyze data with the appropriate techniques.

  • To build models to make predictions about new data.

Module 5: Business Transformation / Industry 4.0

- Digital Transformation vs. Business Transformation
- Innovative business models

- Consumer behaviour in digital markets

- The legal framework in digital markets
- Automation and Robotics 
- Human Machine Interaction
- Cyber-physical systems

- Additive Manufacturing

- Intelligent Materials Technology

- Advanced maintenance

- Process modelling, simulation and virtualization

Specific competences of Module 5:

  • To understand what digital transformation implies and how it impacts the business.

  • To know the consumer purchasing processes arising from the digital economy.

  • To know the legal context of digital markets and businesses.

  • To be able to design disruptive business models.

  • To know the technologies related to the productive transformation.

  • To know the possibilities of the application of artificial vision, programmable robots and collaborative robotics in industrial manufacturing.

Module 6: Big Data and Data Science

- Control of technology and databases, such as SQL or PL/SQL.
- Programming and control skills of programs like R

- Management of distributed storage systems

- Design of reporting systems for data visualization, especially in matters of business intelligence
- Hadoop tool control, such as Hive or Pig
- Ability in managing software tools in data structure systems
- Data handling language instructions, such as data wrangling, data munging or data tyding

- Lead scoring

- Models based on dynamic prices

Specific competences of Module 6:

  • To discover patterns of behavior in large volumes of data.

  • To apply Data Science to solve a real problem through the different steps of: identifying the information, designing the study, analyzing data and building the appropriate model, interpreting the results and issuing technical reports.

  • To identify the usefulness and potential of statistical techniques and data analysis acquired in the different areas of use and know how to apply them properly to draw relevant conclusions.

  • To manage the most important big data tools and software in this area of knowledge with both specific software and R.