Curso Gratuito Official Master´s Degree in Big Data and Business Analytics + 60 Credits ECTS

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Curso 100% Bonificable si eres trabajador contratado en el régimen general y envías la documentación de matrícula (en el caso de ser estudiante, desempleado, autónomo, funcionario o jubilado puedes realizar este curso de forma parcialmente subvencionada)

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Para qué te prepara:

Thanks to this Official Master's Degree in Big Data and Business Analytics you will be able to learn the fundamental aspects of Big Data technology, what tools it uses such as Hadoop, Weka or Pentaho, how to extract business analytics thanks to Power BI, Tableau or Qlikview, being able to program algorithms using Python or R to draw conclusions and even extract web data using Google Analytics.

A quién va dirigido:

This Official Master's Degree in Big Data and Business Analytics is designed for multidisciplinary profiles since it can be applied to a multitude of fields and sectors with the aim of improving your professional career and with what tools you can carry out these large-volume processing analyzes data and information visualization.

Titulación:

- Official Master's Degree in Big Data and Business Analytics issued by the e-Campus University accredited with 60 University ECTS. Passing it will give the right to obtain the corresponding Official Master's Degree, which can qualify for carrying out the Doctoral Thesis and obtaining the Doctor's title.

Objetivos:

- Learn the principles of Big Data and the development of the phases of a Big Data project. - Discover what tools Big Data uses to analyze data such as Hadoop, Weka or Pentaho. - Exploit data and visualize results through the Data Science technique. - Program data analysis algorithms with the Python and R programming languages. - Apply the analyzes carried out to create data visualizations thanks to PowerBI, Tableau or Qlikview. - Know how to use web analytics for Big Data and apply them through Google Analytics.

Salidas Laborales:

Big Data and Business Intelligence are booming technologies and highly demanded by all companies. Therefore, thanks to this Official Master's Degree in Big Data and Business Analytics you will be able to work as a Big Data systems consultant/auditor, data analyst, Python programmer for data analysis, data visualization expert or Web analyst.

Resumen:

To achieve business success, it is currently key to use technologies such as Big Data, Business Intelligence or Data Science to analyze information that allows good conclusions to be drawn. Thanks to this Official Master's Degree in Big Data and Business Analytics you will be able to learn the fundamental aspects of Big Data technology, what tools it uses such as Hadoop, Weka or Pentaho, how to extract business analytics thanks to Power BI, Tableau or Qlikview, being able to program algorithms using Python or R to draw conclusions and even extract web data using Google Analytics. You will have a team of professionals specialized in the field. In addition, thanks to the guaranteed internships, you will be able to access a labor market in full expansion.

Metodología:

With our online learning methodology, the student begins his journey with us through a virtual campus designed exclusively to develop the training itinerary with the aim of improving his professional profile. The teletraining hours carried out in the Virtual Campus are complemented by the student's autonomous work, communication with the teacher, complementary activities and readings and research work. The Final Master's Project is carried out after completing the theoretical-practical content on Campus, which will be graded with a score between 0-6 points. Finally, they will have to take an official exam in person in Spanish for each of the master's subjects, which can be done at the Madrid or Bogotá offices or at any of the Chamber of Commerce offices with which the University has an agreement to conducting face-to-face assessments. These exams are currently being carried out online, exceptionally due to the Covid situation.

Temario:

  1. MODULE 1. BIG DATA INTRODUCTION
  2. DIDACTIC UNIT 1. INTRODUCTION TO BIG DATA
  3. DIDACTIC UNIT 2. DATA SOURCES
  4. DIDACTIC UNIT 3. OPEN DATA
  5. DIDACTIC UNIT 4. PHASES OF A BIG DATA PROJECT
  6. DIDACTIC UNIT 5. BUSINESS INTELLIGENCE AND THE INFORMATION SOCIETY
  7. DIDACTIC UNIT 6. MAIN BUSINESS INTELLIGENCE PRODUCTS
  8. DIDACTIC UNIT 7. BIG DATA AND MARKETING
  9. DIDACTIC UNIT 8. FROM BIG DATA TO LINKED OPEN DATA
  10. DIDACTIC UNIT 9. INTERNET OF THINGS
  11. MODULE 2. TECHNOLOGIES APPLIED TO BUSINESS INTELLIGENCE
  12. DIDACTIC UNIT 1. DATA MINING AND MACHINE LEARNING
  13. DIDACTIC UNIT 2. DATAMART. DEPARTMENTAL DATABASE CONCEPT
  14. DIDACTIC UNIT 3. DATAWAREHOUSE OR CORPORATE DATA STORE
  15. DIDACTIC UNIT 4. BUSINESS INTELLIGENCE AND ANALYTICAL TOOLS
  16. DIDACTIC UNIT 5. POWERBI TOOL
  17. DIDACTIC UNIT 6. TABLEAU TOOL
  18. DIDACTIC UNIT 7. QLIKVIEW TOOL
  19. MODULE 3. TOOLS FOR THE EXPLOITATION AND ANALYSIS OF BIG DATA
  20. DIDACTIC UNIT 1. NOSQL DATABASES AND SCALABLE STORAGE
  21. DIDACTIC UNIT 2. INTRODUCTION TO A NOSQL DATABASE SYSTEM. MONGODB
  22. DIDACTIC UNIT 3. DISTRIBUTED DATA PROCESSING WITH HADOOP
  23. DIDACTIC UNIT 4. WEKA AND DATA MINING
  24. DIDACTIC UNIT 5. PENTAHO AN OPEN SOURCE SOLUTION FOR BUSINESS INTELLIGENCE
  25. MODULE 4. INTRODUCTION TO STATISTICAL PROGRAMMING
  26. DIDACTIC UNIT 1. PYTHON AND DATA ANALYSIS
  27. DIDACTIC UNIT 2. R AS A TOOL FOR BIG DATA
  28. MODULE 5. DATA SCIENCE
  29. DIDACTIC UNIT 1. INTRODUCTION TO DATA SCIENCE
  30. DIDACTIC UNIT 2. RELATIONAL DATABASES
  31. DIDACTIC UNIT 3. PRE-PROCESSING & DATA PROCESSING
  32. DIDACTIC UNIT 4. DATA ANALYSIS
  33. MODULE 6. ARTIFICIAL INTELLIGENCE (AI), MACHINE LEARNING (ML) AND DEEP LEARNING (DL)
  34. DIDACTIC UNIT 1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  35. DIDACTIC UNIT 2. TYPES OF ARTIFICIAL INTELLIGENCE
  36. DIDACTIC UNIT 3. ALGORITHMS APPLIED TO ARTIFICIAL INTELLIGENCE
  37. DIDACTIC UNIT 4. RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE AND BIG DATA
  38. DIDACTIC UNIT 5. EXPERT SYSTEMS
  39. DIDACTIC UNIT 6. FUTURE OF ARTIFICIAL INTELLIGENCE
  40. DIDACTIC UNIT 7. INTRODUCTION TO MACHINE LEARNING
  41. DIDACTIC UNIT 8. EXTRACTION OF DATA STRUCTURE: CLUSTERING
  42. DIDACTIC UNIT 9. RECOMMENDATION SYSTEMS
  43. DIDACTIC UNIT 10. CLASSIFICATION
  44. DIDACTIC UNIT 11. NEURAL NETWORKS AND DEEP LEARNING
  45. DIDACTIC UNIT 12. SYSTEMS OF ELECTION
  46. DIDACTIC UNIT 13. DEEP LEARNING WITH PYTHON, KERAS AND TENSORFLOW
  47. DIDACTIC UNIT 14. NEURAL SYSTEMS
  48. DIDACTIC UNIT 15. SINGLE LAYER NETWORKS
  49. DIDACTIC UNIT 16. MULTILAYER NETWORKS
  50. DIDACTIC UNIT 17. LEARNING STRATEGIES
  51. MODULE 7. PLN, CHATBOTS AND ARTIFICIAL INTELLIGENCE
  52. DIDACTIC UNIT 1. INTRODUCTION TO PLN
  53. DIDACTIC UNIT 2. PLN IN PYTHON
  54. DIDACTIC UNIT 3. COMPUTING THE SYNTAX FOR THE PLN
  55. DIDACTIC UNIT 4. COMPUTATION OF SEMANTICS FOR PLN
  56. DIDACTIC UNIT 5. RECOVERY AND EXTRACTION OF INFORMATION
  57. DIDACTIC UNIT 6. WHAT IS A CHATBOT?
  58. DIDACTIC UNIT 7. RELATIONSHIP BETWEEN AI AND CHATBOTS
  59. DIDACTIC UNIT 8. AREAS OF APPLICATION CHATBOTS
  60. MODULE 8. WEB ANALYTICS
  61. DIDACTIC UNIT 1. INTRODUCTION TO WEB ANALYTICS
  62. DIDACTIC UNIT 2. GOOGLE ANALYTICS 4
  63. DIDACTIC UNIT 3. GOOGLE TAG MANAGER
  64. DIDACTIC UNIT 4. ATTRIBUTION MODELS
  65. DIDACTIC UNIT 5. CREATION OF DASHBORAD WITH GOOGLE DATA STUDIO
  66. DIDACTIC UNIT 6. WEB ANALYTICS ORIENTED TO SEO
  67. DIDACTIC UNIT 7. SEM-ORIENTED WEB ANALYTICS
  68. DIDACTIC UNIT 8. WEB ANALYTICS ORIENTED TO SOCIAL NETWORKS
  69. DIDACTIC UNIT 9. TECHNIQUES AND STRATEGIES
  70. DIDACTIC UNIT 10. OTHER TOOLS FOR WEB ANALYTICS
  71. DIDACTIC UNIT 11. COOKIES AND MONITORING TECHNOLOGIES
  72. MODULE 9. FINAL DEGREE PROJECT