1. Data information knoledge wisdom
  2. Data management (i)
  3. Data management (ii)
  4. Corporate performance management
  5. Databases

  1. Business intelligence
  2. Datawarehousing
  3. Big data
  4. Hadoop
  5. Spark

  1. Hadoop ecosystem (I)
  2. Hadoop ecosystem (II)
  3. Hadoop ecosystem (III)
  4. Spark ecosystem
  5. Installation and configuration of big data architectures

  1. Analytics
  2. Main algorithms (I)
  3. Main algorithms (II)
  4. Machine learning and deep learning
  5. Internet of things

  1. Introduction to Power BI
  2. Different types of Power BI
  3. First simple report
  4. Power Query. Data Sources
  5. Data Transformation

  1. Data Modeling
  2. Starting with DAX (I)
  3. Starting with DAX (II)
  4. Getting proficient in DAX (I)
  5. Getting proficient in DAX (II)

  1. Table and Matrix
  2. Trends
  3. How to properly filter your data
  4. Bookmarks
  5. Drill through

  1. Understanding Power BI Service in depth
  2. Sharing content in Power BI Service
  3. Comparing Power BI Service and Power BI Report Server
  4. Integrating Python and R in Power BI Desktop
  5. Introducing Bravo for Power BI Desktop

  1. Introduction to SQL
  2. Database manipulation
  3. Data types
  4. Normalization
  5. Creating tables in SQL

  1. Table manipulation
  2. SQL table query
  3. Table joining in SQL
  4. Table combinations and views
  5. Other SQL commands

  1. String functions and numeric functions (I)
  2. Numeric function (II)
  3. Date and time functions
  4. Other functions
  5. Loops, conditionals and triggers in SQL

  1. Data warehousing introduction
  2. Databases in a data warehouse. Stage
  3. Databases in a data warehouse. ODS (I)
  4. Databases in a data warehouse. ODS (II)
  5. Databases in a data warehouse. DDS

  1. Introduction
  2. Simple, multiple and logistic linear regression (I)
  3. Simple, multiple and logistic linear regression (II)
  4. Support vector machines (SVM)
  5. Decision trees

  1. KNN (k-nearest neighbours)
  2. Naive Bayes
  3. Evaluation of supervised models
  4. Example exercise
  5. Proposed exercise

  1. Introduction to clustering. purconsider and metrics
  2. K-means clustering
  3. Hierarchical clustering, other techniques and examples
  4. Principal component analysis (PCA)
  5. PCA example exercise

  1. Artificial Neural Networks (ANN) (I)
  2. Artificial Neural Networks (ANN) (II)
  3. Artificial Neural Networks (ANN) (III)
  4. Example exercise
  5. Proposed exercise

  1. Introduction
  2. Review. Artificial neural network (ANN)
  3. Review. ANN exercises
  4. Convolutional Neural Networks (CNN)
  5. CNN Exercises

  1. Natural language processing (I)
  2. Recurrent neural networks (RNN) (I)
  3. Recurrent neural networks (RNN) (II)
  4. Natural language processing (II)
  5. RNN Exercise

  1. Boltzmann Machines (BM)
  2. Restricted Boltzmann Machines (RBM)
  3. Recommender systems
  4. Recommender systems. metrics
  5. RBM exercise

  1. Self-organising maps (SOM)
  2. SOM exercises
  3. Autoencoders (AE)
  4. AE exercises
  5. Proposed exercise

  1. State of the art of artificial intelligence
  2. Philosophy of artificial intelligence
  3. Future of artificial intelligence
  4. Project development process with artificial intelligence
  5. Data, your greatest asset

  1. Machine learning
  2. Deep learning
  3. Transformers
  4. Generation of synthetic data
  5. Hyperparameters in artificial intelligence models

  1. Linear regression
  2. Non-linear regression and support vector machines (SVM)
  3. Decision trees, random forests
  4. Fuse logic and gradient down
  5. Recommendation systems

  1. Preparation of the working environment: Anaconda, Visual Studio Code and Python
  2. Input dataset and data preprocessing
  3. TensorHub, TensorFlow and Keras
  4. Image processing
  5. Generation of artificial intelligence models