Introduction
Every company needs specialized professionals who combine analytical skills with strategic vision. For your objectives you have this Master in Data Science and Information SystemsWhat is the Master in Information Systems mention in Data ScienceFacing new professional challenges that allow us to transform the environment in which we live requires solid training. An innovative and quality training, such as the Master's Degree in Information Systems, Data Science mention from Universidad de Los Hemisferios-IMF Global University.
A fourth level program that provides professionals with the knowledge, skills and precise tools to handle, analyze and interpret large volumes of information necessary to achieve business objectives, specialized professionals who combine analytical capacity and strategic vision.
Together with the technical or statistical profiles, the design and content of this master's degree allows managers and other professionals in the organization to identify, capture, transform, analyze and interpret data and drive strategy, innovation and the value of their Business.
Professional outingsMultiple skills that will open doors for you
Graduates of the Master's degree will be able to assume functions and tasks related to data analysis, being able to develop different professional profiles, such as:Data scientist
Data analyst
Business Analyst
Business intelligence expertIn the case of those profiles with prior experience in team management and management, the program will train them in technical aspects for the performance of roles related to the management and direction of data-based projects. For example:Analytics Project Manager
Business Analytics Manager
Business Intelligence Manager
Chief Data OfficerKnowledgeThe Master in Information Systems, mention Data Science, offers the business manager or technical professional the possibility of:Extract, process and analyze all types of information sources applying data science techniques and the main tools currently used in companies.
Mastering the techniques of traditional business intelligence and expanding them with the new possibilities offered by big data and artificial intelligence.
Detect causes, patterns and trends using predictive analytics based on machine learning techniques.
Design experiments and A / B tests to test hypotheses and make decisions based on data.
Generate effective reports and dashboards.
Manage projects based on big data and data science maintaining an appropriate dialogue with all team profiles.
Prepare proposals as well as promote and lead initiatives based on advanced analytics in different business areas.
Understand, create and develop new business models based on the value of data.
Properly manage the governance of data in order to guarantee quality and correctly apply the different regulatory (RGPD) and ethical requirements.
Acquire vision and experience of the main application fields and use cases that are being addressed in various areas such as marketing and CRM, banking and finance, operations, internet of things (IoT), people analytics, etc.Advantages of the Online MethodologyThe 100% online methodology, allowing real-time interactions between teachers and students.
Through the Virtual Campus, the student accesses in a simple, friendly and intuitive way, all the resources and contents necessary to achieve the development of the necessary competencies and skills. Didactic resources that by design optimize time and thus allow an effective learning experience to be achieved.
Following the design and the didactic sequence, the student determines the workload and rhythm, being able at all times through the platform to request the guidance and support of the teachers and tutors. The model is completed with tutorials, classes and virtual practical experiences in real time, the student interacting with the teacher to develop or deepen practical and relevant aspects of the subject content.
The master's degree organizes the eleven subjects of which it is composed, in two ordinary academic periods of 18 weeks duration, so that the student has five weeks to achieve the learning objectives of each subject.
The tutorials, classes and virtual practical experiences in real time are given every two weeks, on Thursday afternoon, Friday afternoon and Saturday. (* The schedule will be adjusted to the teaching sequence, seeking to be compatible with the work activity).
ContentsData Scientist ToolsPython basics.
Libraries for data science: Numpy, Pandas, etc.
Data processing and visualization with Python.
Fundamentals of R.
R.
Data processing and visualization with R.Impact and value of big dataIntroduction to the big data world
Business intelligence vs. big data.
Big data technologies.
Impact on the organization.
The value of the data and applications by sectors.Data science Analysis, mining and visualization techniquesThe life cycle of the data.
Data quality.
Data preparation and pre-processing.
Analytical models.
Visualization tools and techniques.Business intelligence and visualizationIntroduction to business intelligence.
Database design.
SQL standard.
The Data Warehouse.
Extraction, transformation and loading (ETL) tools and processes.
Effective information display.Degree PlanDesign and implementation of projects with applied research and / or development components.
Design and writing of high-level professional articles.
Analysis of practical models for the development of the complex exam.Big data technology and cloud solutionsHadoop and its ecosystem.
Spark. Fundamentals and applications.
NoSQL databases.
Cloud platform.Statistics for data scientistsIntroduction to statistics.
Probability and sampling.
Inference.
Regression.
Design of experiments.Machine learningTools for machine learning.
Techniques and applications of supervised learning.
Techniques and applications of unsupervised learning.
Deep learning modalities and techniques.
Cloud solutions for machine learningArtificial intelligence for the companyIntroduction to artificial intelligence.
Techniques and applications for decision making.
Reinforcement learning and applications.
Techniques and applications of natural language processing (NLP).
Recommendation systems and applications.Big Data in the companyProject management standards.
Agile project management.
Regulatory and ethical aspects.
Data governanceProfessional deontologyHumanistic vision for technical management and professional ethics.
Ethics of the public service facing the risks of arbitrariness and abuse of power.
Ethical responsibility for environmental care and other global problems.
Scope of the professional's responsibility.Admission profileGiven the nature of the program, third-level graduates will enter.
Those professionals whose degrees belong to the broad field of Information and Communication Technologies (ICTs) in accordance with the nomenclature of professional titles and academic degrees access preferentially.
Other professionals who have a third-level degree in a different broad field, accrediting experience in the use and professional application of information and communication technologies focused on data and information management through databases can access the master's degree.
