1. What is Big Data?
  2. What is the nuance in differentiating between the two terms?
  3. The big data boom
  4. The importance of storing and extracting information
  5. What is the role of data sources?
  6. Innovative solutions through data selection
  7. Nature of Big Data sources

  1. Thick Data, the value of qualitative. Understanding human emotions, intentions and feelings
  2. Phases in a Big Data project
  3. Big Data for business
  4. Supporting Big Data in the decision-making process
  5. Operational decision-making

  1. Strategic marketing and Big Data
  2. Open data
  3. Example of the use of Open Data
  4. IoT (Internet of Things)

  1. Relationship between artificial intelligence and big data
  2. AI and Big Data combined
  3. The role of Big Data in AI
  4. Big Data in health
  5. The need for Big Data in healthcare
  6. Challenges of big data in health
  7. Big Data and People Analytics in HR

  1. Defining the concept of Business Intelligence and the Information Society
  2. Architecture of a Business Intelligence solution
  3. Business Intelligence in company departments
  4. Concepts of Master Plan, Strategic Plan and Annual Operating Plan
  5. Operational Systems and ETL Processes in a BI System
  6. Business Intelligence Advantages and Risk Factors

  1. Balanced Scorecards (BSC)
  2. Decision Support Systems (DSS)
  3. Executive Information Systems (EIS)

  1. Introduction to data mining and machine learning
  2. KDD Process
  3. Data Mining Models and Techniques
  4. Areas of application
  5. Text Mining and Web Mining
  6. Data mining and marketing

  1. Approach to the DataMart concept
  2. OLTP databases
  3. OLAP Databases
  4. MOLAP, ROLAP & HOLAP
  5. Tools for OLAP cube development

  1. Overview: Why DataWarehouse?
  2. Structure and Construction
  3. Phases of implementation
  4. Features
  5. Data Warehouse in the cloud

  1. Internet of Things (IoT) Context
  2. What is IoT?
  3. Elements that make up the IoT ecosystem
  4. IoT architecture
  5. Devices and elements used
  6. Examples of use
  7. Challenges and future lines of work

  1. What is Data Storytelling?
  2. Key elements of Data Storytelling
  3. Why is Data Storytelling important?
  4. How to do Data Storytelling?

  1. What is Hadoop? Relationship with Big Data
  2. Installation and configuration of Hadoop infrastructure and ecosystem
  3. HDFS file system
  4. MapReduce with Hadoop
  5. Apache Hive
  6. Apache Hue
  7. Apache Spark

  1. What is data science?
  2. Tools needed for the data scientist
  3. Data Science & Cloud Computing
  4. Legal aspects of Data Protection

  1. Introduction
  2. The relational model
  3. SQL query language
  4. MySQL A relational database

  1. What is a NoSQL database?
  2. Relationship Vs NoSQL Databases
  3. Type of NoSQL Databases CAP Theorem
  4. NoSQL Database Systems

  1. What is MongoDB?
  2. How MongoDB works and how to use it
  3. Getting started with MongoDB: Installation and command shell
  4. Creating our first NoSQL database: Model and Data Insertion
  5. Updating data in MongoDB: set and update statements
  6. Working with indexes in MongoDB for data optimisation
  7. Data query in MongoDB

  1. What is Weka?
  2. Data Mining Techniques in Weka
  3. Weka Interfaces
  4. Attribute selection

  1. An approach to PENTAHO
  2. Solutions offered by PENTAHO
  3. MongoDB & PENTAHO
  4. Hadoop & PENTAHO
  5. Weka & PENTAHO

  1. Introduction to R
  2. What do you need?
  3. Data types
  4. Descriptive and Predictive Statistics with R
  5. R integration in Hadoop

  1. Data collection and cleansing (ETL)
  2. Statistical inference
  3. Regression models
  4. Hypothesis testing

  1. Business Analytics Intelligence
  2. Graph theory and social network analysis
  3. Presentation of results

  1. What is data analysis?

  1. Data analysis with NumPy
  2. Pandas
  3. Matplotlib

  1. How to use loc in Pandas
  2. How to delete a column in Pandas

  1. Pivot tables in pandas

  1. The group of Pandas

  1. Python Pandas merging data frames

  1. Matplotlib
  2. Seaborn

  1. Machine learning

  1. Linear regression
  2. Logistic regression

  1. Tree structure

  1. Naive Bayes algorithm
  2. Types of Naive Bayes

  1. Support Vector Machines (Support Vector Machine-SVN)
  2. How does SVM work?
  3. SVM cores
  4. Classifier construction in Scikit-learn

  1. K-nearest Neighbors (KNN)
  2. Python implementation of KNN algorithm

  1. Principal component analysis

  1. Random forest algorithm

  1. What is data visualisation?
  2. Importance and tools of data visualisation
  3. Data visualisation: Basic principles

  1. What is Tableau? Uses and applications
  2. Tableau Server: Architecture and Components
  3. Tableau Installation
  4. Workspace and navigation
  5. Data connections in Tableau
  6. Types of filters in Tableau
  7. Organisation of data, groups, hierarchies and assemblies
  8. Tables and charts in Tableau

  1. D3 Fundamentals
  2. Installation D3
  3. Operation D3
  4. SVG
  5. Data types in D3
  6. Bar chart with D3
  7. Scatter diagram with D3

  1. Google Data Studio

  1. Installation and architecture
  2. Data upload
  3. Reports
  4. Data transformation and modelling
  5. Data analysis

  1. Introduction to Power BI
  2. Installing Power BI
  3. Data modelling
  4. Data visualisation
  5. Dashboards
  6. Data sharing

  1. CartoDB
  2. What is CARTO?
  3. Loading and use of data. Types of analysis
  4. Programming a viewer with the CARTO.js library
  5. Using examples and help from the API documentation

  1. What is web analytics?
  2. Setting targets and KPIs
  3. Core and advanced metrics
  4. Objectives and advantages of measuring
  5. Measurement plan

  1. Introduction to Google Analytics 4
  2. Interface
  3. Metrics and dimensions
  4. Basic reporting
  5. Filters
  6. Segments
  7. Events
  8. Customised reports
  9. User behaviour and data interpretation

  1. Introduction to GTM
  2. Implementation with GTM
  3. Measurement with GTM
  4. Using Debug/Preview Mode

  1. The attribution
  2. Multichannel
  3. Customer Journey
  4. Main attribution models
  5. Customised attribution models

  1. Dashboard planning
  2. Dashboard features
  3. Introduction to Data Studio
  4. Connectors
  5. Types of graphs
  6. Customisation of reports
  7. Control elements
  8. Dimensions and metrics
  9. Calculated Fields
  10. Sharing reports

  1. Introduction to SEO
  2. History of search engines
  3. Components of a search engine
  4. Organisation of results in a search engine
  5. The importance of content
  6. The concept of authority on the Internet
  7. SEO Campaign

  1. Introduction to SEM
  2. Main concepts in SEM
  3. Bidding System and Ad Quality
  4. First contact with Google Ads
  5. Creating quality advertisements
  6. Key performance indicators in SEM

  1. Social media traffic analysis
  2. Setting social media goals
  3. Facebook
  4. Twitter
  5. Youtube
  6. LinkedIn
  7. Tik tok
  8. Instagram

  1. Usability
  2. Heat maps
  3. User session recordings
  4. Card sorting
  5. A/B Testing
  6. Multivariate test
  7. KPIs, key performance indicators
  8. Changes to make to optimise a website

  1. Hotjar
  2. Microsoft Power BI
  3. Google Search Console
  4. Matomo
  5. Awstats
  6. Chartbeat
  7. Adobe Analytics

  1. What are cookies?
  2. Types of cookies
  3. GDPR
  4. Tools to manage cookie consent

  1. The technological revolution
  2. Media and digital marketing
  3. Technology in the sports industry
  4. Technology in sporting events

  1. Sports biometrics and analytics
  2. Data Mining applied to sport
  3. BI system applied to sport
  4. Data Envelopment Analysis (DEA) applied to sport
  5. Sports data and market transformation

        1. What is Scouting?
        2. Importance of Scouting
        3. Profile of the video manager

        1. Hardware and software elements
        2. Video capture and playback elements
        3. Video match analysis software

        1. What can be analysed in a team?
        2. Tactics
        3. Methodology of tactical preparation
        4. Rational land use
        5. Transitions in football
        6. Some offensive tactics or actions with the ball
        7. Defensive positioning
        8. Basics of the game system

        1. Introduction to reporting
        2. Data and information collection
        3. Example of a player tracking form
        4. Example of a match scouting form

        1. Coach decision making
        2. Sports tactics
        3. Sports strategy