MODULE 1. DIGITAL TRANSFORMATION

1. UNIT 1. INTRODUCTION TO DIGITAL TRANSFORMATION
2. Introduction to digital transformation
3. Concept of innovation
4. Concept of technology
5. Typology of technology
6. Competitive advantage point of view
7. According to its disposition in the company
8. From the point of view of a project
9. Other types of technology
10. Technological innovation
11. Core competencies of technological innovation
12. The process of technological innovation
13. Tools to innovate
14. Competitiveness and innovation
16. UNIT DIDACTIC 2. SOCIETY 3.0
17. Web 3.0 philosophy and its impact on the business world
18. Socialization of the Web
19. Adapting the business world to new technologies
21. UNIT 3. NEW DIGITAL ECOSYSTEM
22. Community Manager
23. Chief Data Officer
24. Data Protection Officer
25. Data Scientist
26. Other profiles
27. IT Skills Development
28. The Role of the CEO as a Leader in Transformation
30. UNIT DIDACTIC 4. NEW BUSINESS MODELS IN THE DIGITAL ENVIRONMENT
31. The Digital Transition from the Traditional Business Model
32. New Business Models
33. Freemium
34. Long Tail Model
35. Cloud and SaaS Model
36. Subscription Model
37. Dropshipping
38. Affiliation
39. Infoproducts and E-Learning
40. Others
42. UNIT DIDACTIC 5. DIGITAL TRANSFORMATION PLAN
43. Diagnosis of the digital maturity of the company
44. Analysis of innovation in the company
45. Preparation of the roadmap
46. Provision of financing and technological resources
47. Implementation of the digital transformation plan
48. Follow-up of the digital transformation plan
50. UNIT DIDACTIC 6. SUCCESS CASES IN DIGITAL TRANSFORMATION
51. BBVA and the smart company
52. DKV Salud y #MédicosfrentealCOVID
53. El Corte Inglés
54. Cepsa y su apuesta por los servicios cloud de AWS
56. UNIDAD DIDÁCTICA 7. THE NEW DIGITAL CUSTOMER
57. Redesigning the customer experience
58. The transformation of distribution channels: omnichannel
59. Digital marketing plan
60. Buyer's Journey
61. Growth Hacking: growth strategy
62. The new role of marketing in the conversion funnel
64. UNIT TEACHING 8. NEW MARKETS, NEW OPPORTUNITIES
65. Innovation opportunities arising from globalization
66. How to Invent Markets through Innovation
67. Development stages and life cycles
68. Market entry
69. Development methodologies
71. UNIT DIDACTIC 9. INNOVATION IN ORGANIZATIONAL PROCESSES
72. The digital transformation of the value chain
73. Industry 4.0
74. Organizational adaptation through talent and innovation
75. Innovation process models
76. Innovation management
77. Innovation system
78. How to reinvent companies by innovating in processes
79. Innovation in Processes through ICT
80. E-Commerce: innovating in distribution channels
81. Voluntary case study: Innovation according to Steve Jobs
82. Helvex case: continuous change
83. Business automation: RPA, RBA and RDA

MODULE 2. BIG DATA AND STORYTELLING

1. UNIT 1. INTRODUCTION TO BIG DATA
2. What is Big Data?
3. The era of large amounts of information: history of big data
4. The importance of storing and extracting information
5. Big Data focused on business
6. Open data
7. Public information
8. IoT (Internet of Things-Internet of Things)
10. UNIT DIDACTIC 2. DATA SOURCES
11. Definition and relevance of data source selection
12. Nature of Big Data sources
14. DIDACTIC UNIT 3. OPEN DATA
15. Definition, Benefits and Features
16. Example of Open Data usage
18. UNIT DIDACTIC 4. PHASES OF A BIG DATA PROJECT
19. Initial diagnosis
20. Project design
21. Implementation process
22. Project monitoring and control
23. Responsible and available resources
24. Scheduling
25. Scope and economic valuation of the project
27. UNIT 5. BUSINESS INTELLIGENCE AND THE INFORMATION SOCIETY
28. Defining the concept of Business Intelligence and the Information Society
29. Architecture of a Business Intelligence solution
30. Business Intelligence in the company's departments
31. Concepts of Master Plan, Strategic Plan and Annual Operating Plan
32. Operational Systems and ETL Processes in a BI system
33. Advantages and Risk Factors of Business Intelligence
35. UNIT 6. MAIN BUSINESS INTELLIGENCE PRODUCTS
36. Balanced Scorecards (BSC)
37. Decision Support Systems (DSS)
38. Executive Information Systems (EIS)
40. UNIT 7. BIG DATA AND MARKETING
41. Big Data support in the decision making process
42. Operational decision making
43. Strategic marketing and Big Data
44. New trends in management
45. Practical exercises
47. UNIT DIDACTIC 8. FROM BIG DATA TO LINKED OPEN DATA
48. Semantic Web Concept
49. Linked Data Vs. Big Data
50. SPARQL Query Language
52. UNIT DIDACTIC 9. INTERNET OF THINGS
53. Internet of Things (IoT) context
54. What is IoT?
55. Elements that make up the IoT ecosystem
56. IoT architecture
57. Devices and elements used
58. Examples of use
59. Challenges and future lines of work
61. UNIT DIDACTIC 10. STORYTELLING
62. What is Data Storytelling?
63. Key elements of Data Storytelling
64. Why is Data Storytelling important?
65. How to do Data Storytelling?

MODULE 3. DATA SCIENCE: DATA STORAGE, ANALYSIS AND PROCESSING

1. UNIT 1.INTRODUCTION TO DATA SCIENCE
2. What is data science?
3. Tools needed for the data scientist
4. Data Science & Cloud Computing
5. Legal Aspects in Data Protection
7. UNIT DIDACTIC 2.RELATIONAL DATABASES
8. Introduction
9. The Relational Model
10. SQL Query Language
11. MySQL A Relational Database
13. UNIT DIDACTIC 3. NOSQL DATABASES AND SCALABLE STORAGE
14. What is a NoSQL database?
15. Relationship Databases Vs NoSQL Databases
16. Type of NoSQL Databases CAP Theorem
17. NoSQL Database Systems
19. UNIT DIDACTIC 4. INTRODUCTION TO A NOSQL DATABASE SYSTEM: MONGODB
20. What is MongoDB?
21. How MongoDB works and how to use it
22. First steps with MongoDB: Installation and command shell
23. Creating our first NoSQL Database: Data Model and Insertion
24. Updating data in MongoDB: Set and update statements
25. Working with indexes in MongoDB for data optimization
26. Querying data in MongoDB
28. UNIT DIDACTIC 5. WEKA AND DATA MINING
29. What is Weka?
30. Data Mining Techniques in Weka
31. Weka Interfaces
32. Attribute Selection
34. UNIT 6. PENTAHO
35. An approach to PENTAHO
36. Solutions offered by PENTAHO
37. MongoDB & PENTAHO
38. Hadoop & PENTAHO
39. Weka & PENTAHO
41. TEACHING UNIT 7. R AS A TOOL FOR BIG DATA
42. Introduction to R
43. What do you need?
44. Data Types
45. Descriptive and Predictive Statistics with R
46. Integrating R into Hadoop
48. UNIT DIDACTIC 8. PRE-PROCESSING & DATA PROCESSING
49. Obtaining and cleaning data (ETL)
50. Statistical inference
51. Regression models
52. Hypothesis testing
54. TEACHING UNIT 9. DATA ANALYSIS
55. Analytical business intelligence
56. Graph theory and social network analysis
57. Presentation of results

MODULE 4. DATA ANALYSIS WITH PYTHON

1. UNIT 1. INTRODUCTION TO DATA ANALYSIS
2. What is data analysis?
4. UNIT 2. DATA ANALYSIS LIBRARIES: NUMPY, PANDAS AND MATPLOTLIB
5. Data analysis with NumPy
6. Pandas
7. Matplotlib
9. DIDACTIC UNIT 3. DATA FILTERING AND EXTRACTION
10. How to use loc in Pandas
11. How to delete a column in Pandas
13. UNIT DIDACTIC 4. PIVOT TABLES
14. Pivot tables in Pandas
16. UNIT DIDACTIC 5. GROUPBY AND AGGREGATION FUNCTIONS
17. The pandas group
19. UNIT DIDACTIC 6. MERGING DATAFRAMES
20. Python Pandas merging data frames
22. UNIT DIDACTIC 7. DATA VISUALIZATION WITH MATPLOTLIB AND SEABORN
23. Matplotlib
24. Seaborn
26. DIDACTIC UNIT 8. INTRODUCTION TO MACHINE LEARNING
27. Machine Learning
29. UNIT DIDACTIC 9. LINEAR REGRESSION AND LOGISTIC REGRESSION
30. Linear Regression
31. Logistic Regression
33. UNIT DIDACTIC 10. DECISION TREE
34. Tree structure
36. TEACHING UNIT 11. NAIVE BAYES
37. Naive bayes algorithm
38. Types of Naive Bayes
40. TEACHING UNIT 12. SUPPORT VECTOR MACHINES (SVM)
41. Support Vector Machines (Support Vector Machine-SVM)
42. How does SVM work?
43. SVM kernels
44. Classifier construction in Scikit-learn
46. TEACHING UNIT 13. KNN
47. K-nearest Neighbors (KNN)
48. Python implementation of KNN algorithm
50. TEACHING UNIT 14. PRINCIPAL COMPONENT ANALYSIS (PCA)
51. Principal Component Analysis
53. TEACHING UNIT 15. RANDOM FOREST
54. Random Forest Algorithm

MODULE 5. DATA MINING, AUTOMATIC LEARNING AND MACHINE LEARNING

1. UNIT 1. DATA MINING OR DATA MINING AND AUTOMATIC LEARNING
2. Introduction to Data Mining and Machine Learning
3. KDD Process
4. Data Mining Models and Techniques
5. Application Areas
6. Text Mining and Web Mining
7. Data mining and marketing
9. UNIT DIDACTIC 2. THE DATA MINING CYCLE: STAGES AND TYPES OF PROBLEMS
10. Types of problems
11. Data implications, domains, techniques in the phases of the process
12. Use cases
14. DIDACTIC UNIT 3. DATA MINING TECHNIQUES
15. Classification or Decision Trees or Naive Bayes
16. Clustering or K-means or EM
17. Association or A priori
19. UNIT 4. HADOOP ECOSYSTEM
20. What is Hadoop? Relationship with Big Data
21. Installation and configuration of Hadoop infrastructure and ecosystem
22. HDFS file system
23. MapReduce with Hadoop
24. Apache Hive
25. Apache Hue
26. Apache Spark
28. UNIT 5. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
29. Introduction to Artificial Intelligence
30. History
31. The Importance of AI
33. TEACHING UNIT 6. ALGORITHMS APPLIED TO ARTIFICIAL INTELLIGENCE
34. Algorithms applied to artificial intelligence
36. TEACHING UNIT 7. RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE AND BIG DATA
37. Relationship between artificial intelligence and big data
38. AI and Big Data combined
39. The role of Big Data in AI
40. AI technologies being used with Big Data
42. UNIT DIDACTIC 8. EXPERT SYSTEMS
43. Expert systems
44. Structure of an expert system
45. Inference: Types
46. Phases of building a system
47. Performance and improvements
48. Application domains
49. Creating an expert system in C#
50. Adding uncertainty and probabilities
52. UNIT DIDACTIC 9. INTRODUCTION TO MACHINE LEARNING
53. Introduction
54. Classification of machine learning algorithms
55. Examples of machine learning
56. Differences between machine learning and deep learning
57. Types of machine learning algorithms
58. The future of machine learning
60. UNIT DIDACTIC 10. DATA STRUCTURE EXTRACTION: CLUSTERING
61. Introduction
62. Algorithms
64. TEACHING UNIT 11. RECOMMENDATION SYSTEMS
65. Introduction
66. Collaborative filtering
67. Clustering
68. Hybrid recommender systems
70. TEACHING UNIT 12. CLASSIFICATION
71. Classifiers
72. Algorithms
74. UNIT 13. NEURAL NETWORKS AND DEEP LEARNING
75. Components
76. Learning
78. UNIT 14. ELECTION SYSTEMS
79. Introduction
80. The process of moving from DSS to IDSS
81. Application cases

MODULE 6. DATA VISUALIZATION

1. UNIT 1. INTRODUCTION TO DATA VISUALIZATION
2. What is data visualization?
3. Importance and tools of data visualization
4. Data visualization: Basic Principles
6. UNIT 2. TABLEAU
7. What is Tableau? Uses and Applications
8. Tableau Server: Architecture and Components
9. Tableau Installation
10. Workspace and Navigation
11. Data Connections in Tableau
12. Types of filters in Tableau
13. Sorting data, groups, hierarchies and sets
14. Tables and charts in Tableau
16. UNIT DIDACTIC 3. D3 (DATA DRIVEN DOCUMENTS)
17. D3 Fundamentals
18. D3 Installation
19. D3 Operation
20. SVG
21. Data types in D3
22. Bar chart with D3
23. Scatter plot with D3
25. UNIT DIDACTIC 4. LOOKER STUDIO (GOOGLE DATA STUDIO)
26. Data visualization
27. Chart typologies
28. Data sources
29. Report creation
31. UNIT 5. QLIKVIEW
32. Installation and architecture
33. Loading data
34. Reporting
35. Data transformation and modeling
36. Data analysis
38. UNIT 6. POWER BI
39. Introduction to Power BI
40. Installing Power BI
41. Data modeling
42. Data visualization
43. Dashboards
44. Data sharing
46. UNIT DIDACTIC 7. CARTO
47. CartoDB
48. What is CARTO?
49. Loading and using data. Types of analysis
50. Programming a viewer with the CARTO.js library
51. Using examples and help from the API documentation

MODULE 7. DATA VISUALIZATION IN R WITH GGPLOT2

1. UNIT 1. GGPLOT2 AS A LIBRARY FOR DATA VISUALIZATION IN R
2. Introduction to Gplot
3. The ggplot2 package
5. UNIT 2. AXIS
6. Change axis titles
7. Increase spacing between axes and axis titles
8. Change Axis title aesthetics
9. Change axis text aesthetics
10. Rotate axis text
11. Remove axis text and marks
12. Remove axis titles
13. Axis range limit
14. Force the plot to start at the origin
15. Axes with the same scale
16. Use a function to modify labels
18. TEACHING UNIT 3. TITLES
19. Add a title
20. Adjust the position of titles
21. Use a non-traditional font in your title
22. Change spacing in multi-line text
24. TEACHING UNIT 4. CAPTIONS
25. Working with captions
26. Turn caption off
27. Remove caption titles
28. Change caption position
29. Change caption direction
30. Change caption title style
31. Change caption title
32. Change the order of legend keys
33. Change legend labels
34. Change background boxes in the legend
35. Change the size of legend symbols
36. Leave a layer outside the legend
37. Manually add legend elements
38. Use other legend styles
40. TEACHING UNIT 5. BACKGROUNDS AND GRID LINES
41. Change panel background color
42. Change grid lines
43. Change grid line spacing
44. Change raster background color
46. UNIT 6. MARGINS
47. Working with margins
49. TEACHING UNIT 7. MULTIPLE PANEL CHARTS.
50. Working with multiple panel charts
51. Create small multiples based on a variable
52. Allow axes to roam freely
53. Use facet_wrapwith two variables
54. Modify the style of strip texts
55. Create a panel of different plots
57. UNIT DIDACTIC 8. COLORS
58. Working with colors
59. Specify individual colors
60. Assign colors to variables
61. Qualitative Variables
62. Manually select qualitative colors
63. Use built-in qualitative color palettes
64. Use qualitative color palettes from extension packages
65. Quantitative Variables
66. The Viridis color palette
67. Use quantitative color palettes from extension packages
68. Modify color palettes afterwards
70. TEACHING UNIT 9. THEMES
71. Change the overall plot style
72. Change the font of all text elements
73. Change the size of all text elements
74. Change the size of all line and rectangle elements
75. Create your own theme
76. Update the current theme
78. TEACHING UNIT 10. LINES
79. Add horizontal or vertical lines to a chart
80. Add a line within a chart
81. Add curved lines and arrows to a chart
83. UNIT 11. TEXT
84. Add tags
85. Add text annotations
86. Use Markdown and HTML Rendering for annotations
88. TEACHING UNIT 12. COORDINATES
89. Flip a plot
90. Fix an axis
91. Invert an axis
92. Transform an axis
93. Circularize a plot
95. TEACHING UNIT 13. TYPES OF CHARTS
96. Alternatives to a box plot
97. Create a carpet representation in a plot
98. Create a correlation matrix
99. Create a contour plot
100. Create a heat map
101. Create a ridge plot
103. TEACHING UNIT 14. RIBBONS
104. Working with ribbons (AUC, CI, etc.)
106. UNIT DIDACTIC 15. INTERACTIVE GRAPHICS
107. Working with interactive graphics

MODULE 8. WEB ANALYTICS

1. UNIT 1. INTRODUCTION TO WEB ANALYTICS
2. What is web analytics?
3. Setting goals and KPIs
4. Main and advanced metrics
5. Objectives and advantages of measuring
6. Measurement plan
8. UNIT DIDACTIC 2. GOOGLE ANALYTICS 4
9. Introduction to Google Analytics 4
10. Interface
11. Metrics and dimensions
12. Basic reports
13. Filters
14. Segments
15. Events
16. Custom reports
17. User behavior and data interpretation
19. UNIT DIDACTIC 3. GOOGLE TAG MANAGER
20. Introduction to GTM
21. Implementation with GTM
22. Measurement with GTM
23. Using Debug/Preview Mode
25. TEACHING UNIT 4. ATTRIBUTION MODELS
26. Attribution
27. Multichannel
28. Customer Journey
29. Main attribution models
30. Custom attribution models
32. TEACHING UNIT 5. CREATING DASHBORAD WITH GOOGLE DATA STUDIO
33. Dashboard Planning
34. Dashboard Features
35. Introduction to Data Studio
36. Connectors
37. Chart Types
38. Customizing Reports
39. Control Elements
40. Dimensions and Metrics
41. Calculated Fields
42. Sharing Reports
44. UNIT DIDACTIC 6. SEO ORIENTED WEB ANALYTICS
45. Introduction to SEO
46. History of search engines
47. Components of a search engine
48. Organization of results in a search engine
49. The importance of content
50. The concept of authority on the Internet
51. SEO campaign
53. UNIT 7. SEM ORIENTED WEB ANALYTICS
54. Introduction to SEM
55. Main concepts in SEM
56. Bidding system and Ad Quality
57. First contact with Google Ads
58. Creating quality ads
59. Key performance indicators in SEM
61. UNIT DIDACTIC 8. WEB ANALYTICS ORIENTED TO SOCIAL NETWORKS
62. Traffic analysis in social networks
63. Setting objectives in social networks
64. Facebook
65. Twitter
66. Youtube
67. LinkedIn
68. Tik tok
69. Instagram
71. UNIDAD DIDÁCTICA 9. TECHNIQUES AND STRATEGIES
72. Usability
73. Heat maps
74. User session recordings
75. Card sorting
76. Test A/B
77. Multivariate test
78. KPI, key performance indicators
79. Changes to be made to optimize a web page
81. UNIT DIDACTIC 10. OTHER WEB ANALYTICS TOOLS
82. Hotjar
83. Microsoft Power BI
84. Google Search Console
85. Matomo
86. Awstats
87. Chartbeat
88. Adobe Analytics
90. UNIT 11. COOKIES AND TRACKING TECHNOLOGIES
91. What are cookies?
92. Types of cookies
93. GDPR
94. Tools to manage cookie consent

MODULE 9. FINAL MASTER PROJECT (FPM)