1. Data information
  2. Knowledge wisdom
  3. Data Management (I)
  4. Data Management (II)
  5. Corporate performance management
  6. Databases

  1. Business intelligence
  2. Datawarehousing
  3. Big data
  4. Hadoop 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: is it really free?
  3. Let's dive in: simple first report
  4. Power Query: data source
  5. Data transformation

  1. Data modelling
  2. Starting with DAX (I)
  3. Getting started with DAX (II)
  4. Mastering DAX (I)
  5. Mastering DAX (II)

  1. Table and matrix
  2. Trends
  3. How to filter your data properly
  4. Scoreboards
  5. Obtaining details

  1. Understanding Power BI Service
  2. Sharing content in Power BI Service
  3. Comparing Power BI Service and Power Report Service
  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. Standardisation
  5. Creating tables in SQL

  1. Manipulation of tables
  2. Querying tables in SQL
  3. Combining tables in SQL
  4. Combinations of tables and views
  5. Other SQL commands

  1. Functions for strings and numeric functions (I)
  2. Numerical functions (II)
  3. Date and time functions
  4. Other functions
  5. Loops, conditionals and triggers in SQL

  1. Introduction to datawarehousing
  2. Databases in a datawarehouse. Stage
  3. Databases in a datawarehouse. ODS (I)
  4. Data in a datawarehouse. ODS (II)
  5. Databases in a datawarehouse. DDS

  1. Introduction to Python
  2. Features and applications
  3. Installing Python
  4. Setting up a development environment

  1. Basic Python syntax
  2. Variables and data types
  3. Operators and expressions
  4. Use of comments

  1. Introduction to flow control
  2. Conditional structures (if, elif, else)
  3. Loops (for and while)
  4. Loop control (break and continue)

  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. 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. Concept of NoSQL Databases
  2. Advantages and disadvantages of NoSQL Databases
  3. Main characteristics of NoSQL Databases

  1. Documentary databases
  2. Columns databases
  3. Key-Value Databases
  4. Network database

  1. Introduction to MongoDB
  2. MongoDB features and architecture
  3. Data modelling in MongoDB
  4. MongoDB queries and operations
  5. Scalability and performance in MongoDB

  1. Apache Cassandra
  2. CouchDB
  3. Redis
  4. Amazon DynamoDB
  5. NeoJS

  1. Design of the data structure
  2. Configuration of the development environment
  3. Installation and configuration of MongoDB
  4. Creating and manipulating collections in MongoDB
  5. Importing and exporting data in MongoDB

  1. Indexes and query optimisation in MongoDB
  2. Data aggregation in MongoDB
  3. Transactions in MongoDB
  4. Replication and high availability in MongoDB
  5. Backup and recovery in MongoDB

  1. Web and mobile applications
  2. Big Data and data analysis
  3. Internet of Things (IoT)
  4. Recommender systems
  5. Social media and social networks

  1. Introduction to data integration
  2. Integration with programming languages (Python, Java, etc.)
  3. Integration with Business Intelligence (BI) tools
  4. Integration with cloud storage systems

  1. NoSQL Database Security Concepts
  2. Authentication and authorisation in MongoDB
  3. Data Encryption in NoSQL Databases
  4. Auditing and Access Control in NoSQL Databases

  1. Introduction
  2. Data literacy
  3. Working with data
  4. Solutions and techniques for data processing
  5. Data quality management

  1. Working with data in Excel
  2. Data set (DATASET)
  3. Data Cleasing with Excel
  4. Data Wrangling with Excel
  5. Data Blending in Excel

  1. Talend Data Preparation Desktop Installation
  2. Working with data in Talend
  3. Data Cleasing with Talend
  4. Data Wrangling with Talend
  5. Data Blending with Talend

  1. Registration in dataprep by Trifacta
  2. Working with data with Dataprep by Trifacta
  3. Data Cleasing with Trifacta
  4. Data Wrangling with Dataprep by Trifacta
  5. Data Blending with Dataprep by Trifacta

  1. Introduction
  2. Linear, multiple and logistic regression (I)
  3. Linear, multiple and logistic regression (II)
  4. Support Vector Machine (SVM)
  5. Decision trees

  1. KNN (K-Nearest Neighbors)
  2. Naive bayes
  3. Evaluation of supervised models
  4. Sample exercise
  5. Proposed exercise

  1. Introduction to clustering. purpose and metrics
  2. K-means clustering
  3. Hierarchical clustering, other techniques and examples
  4. Principal Component Analysis (PCA)
  5. Sample PCA exercise

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

  1. Introduction and Review of Artificial Neural Networks (ANN)
  2. Convolutional Neural Networks (CNN). Introduction and use cases
  3. CNN. Intuition
  4. CNN. Mathematical description
  5. CNN. Programming example with Python and TensorFlow
  6. Exercise. Artificial vision with CNN

  1. Time Series Review
  2. Recurrent Neural Networks (RNN). Introduction and use cases
  3. RNN. Intuition
  4. RNN. Mathematical description
  5. RNN. Programming example with Python AND TensorFlow
  6. Exercise. Time Series with RNN

  1. Review of Recommender Systems
  2. Deep Bolztmann Machines (DBM). Introduction and use cases [Video].
  3. DBM. Intuition
  4. DBM. Mathematical description
  5. DBM. Programming example with Python and TensorFlow
  6. Exercise. DBM Recommendation System

  1. Anomaly detection
  2. Self-Organising Maps (SOM). Introduction and intuition
  3. SOM. Mathematical description
  4. AutoEncoders (AE). Introduction and intuition
  5. AE. Mathematical description
  6. Exercise. Anomaly detection with SOM and AE.

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

  1. Machine learning
  2. Deep learning
  3. Transformers
  4. Synthetic data generation
  5. Hyperparameters in artificial intelligence models

  1. Linear regression
  2. Non-linear Regression and Support Vector Machine (SVM)
  3. Decision trees and random forests
  4. Fuzzy logic and gradient descent
  5. Recommender systems

  1. Preparation of the working environment. Anaconda, Visual Studio Code and Python.
  2. Data input and processing datasets
  3. TensorHub, TensorFlow and Keras
  4. Image processing
  5. Artificial intelligence modelling