1. Introduction to data management and governance.
2. Roles and responsibilities in data governance.
3. Main data characteristics.
4. Technologies and tools for data management.
5. Principles, legislation and ethics applicable to data management.
6. Security and Privacy.
7. Basic concepts of cryptography.
8. Security in communications, networks and the Internet.
9. Security and Privacy in data processing.
10. Cloud and IoT security.

1. Introduction and Business Analysis key concepts
2. Business Analysis planning and monitoring
3. Elicitation and collaboration
4. Requirements life cycle management
5. Strategy analysis
6. Requirements analysis and design definition
7. Solution evaluation
8. Business Intelligence perspective
9. The information technology perspective
10. The business architecture perspective
11. The business process management perspective

1. Introduction
   1.1. Data, Information and Information Systems
   1.2. Concepts regarding data and data structures
   1.3. Data privacy and protection
2. Data analysis
   2.1. Normalization
   2.2. Metadata
   2.3. Data storage systems: Relational databases and NoSQL databases
   2.4 Models describing data and its relationships: conceptual, logical and physical
   2.5. Data quality criteria
   2.6. OLTP and OLAP paradigms
3. Data manipulation languages: SQL
   3.1. Creating data structures
   3.2. Data manipulation operations
   3.3. Basic information retrieval queries
4. Data Integration: techniques and platforms
   4.1. Planning
   4.2. Data extraction
   4.3. Data treatment
   4.4. Data integration
   4.5 Star models
   4.6. Techniques and tools
5. Data analysis platforms

1 – Introduction to R software
2 – Data manipulation
3 – Summary of data
4 – Point estimation
5 – Confidence intervals
6 – Hypothesis testing
7 – Association and correlation between variables
8 – Statistics topics for machine learning

• Principal component analysis
• Cluster analysis
• Naive Bayes classification algorithm

1. Linear regression
   a) Interpretation and estimation
   b) Model assumptions analysis
   c) Inference on regression parameters
   d) Quality and model comparison measures
   e) Point and interval prediction
   f) The use of dummy variables
2. Logistic regression
   a) Logit link function
   b) Interpretation and estimation of the models
   c) Analysis of model’s assumptions
   d) Inference on regression parameters
   e) Quality and model comparison measures
   f) Cutoff point and the ROC curve
3. Models based on panel data
   a) Fixed effects models
   b) Random effect models
4. Introduction to time series
   a) Fundamental concepts
   b) Components of a time series
   c) Decomposition of a time series

1. Introduction to Data Mining
2. Methodologies for the Data Mining Process: CRISP-DM
3. Data exploration
4. Data Preparation
5. Data Reduction: Characteristics, Cases, Values
6. Data Mining Algorithms: Naïve Bayes, Trees and Decision Rules, Logistic Regression, K-Nearest Neighbors, NeuralNetworks, Support Vector Machines, Association Rules, K-Means, Random Forests, Boosting
7. Model Evaluation and Selection
8. Advanced Data Mining Topics
9. Privacy, Security and Challenges

1. Introduction to Business Intelligence (BI):
   1.1. Background;
   1.2. Life cycle;
   1.3. Case studies (Banks; Finance; Health; Education; etc.).
2. Data Warehousing and OLAP:
   2.1. Data Warehouse concept;
   2.2. Multidimensional model;
   2.3. Facts, dimensions and metrics;
   2.4. Hierarchies;
   2.5. OLAP operations;
   2.6. OLAP Servers.
3. Report Development:
   3.1. Data Preparation;
   3.2. Structuring reports with visual elements.
4. Data Analytics and Data Visualization:
   4.1. Types of visual elements and selection factors;
   4.2. Technical challenges;
   4.3. Visual exploration;
   4.4. Design and development of standard graphics;
   4.5. Dashboards, scorecards and indicators (KPIs);
   4.6. Knowledge extraction to support decision making.
5. Presentation, communication and storytelling:
   5.1. To identify and transmit the data story;
   5.2. To structure narratives;
   5.3. To identify visual elements;
   5.4. Context and direction.

1. Python Programming
   a. Python
   b. Machine learning platforms
   c. TensorFlow
   d. Keras
2. Neural networks
   a. Structure of an artificial neuron
   b. The perceptron
   c. Multi-layer perceptron networks
   d. Activation functions
   e. Stochastic gradient descent
   f. Backpropagation algorithm
3. Auto-encoders
   a. Architectures
   b. Applications
4. Regularization and optimization
   a. L1 and L2 regularization
   b. Dropout
   c. Batch
   d. AdaGrad
   e. Adam
5. Convolutional neural networks
   a. Architecture of a convolutional neural network
   i. Convolutional layers
   ii. Pooling layers
   iii. Fully connected layers
   b. Training a convolutional neural network
   c. Transfer learning
   d. Image processing applications
6. Recurrent neural networks
   a. Architectures:
   i. RNN
   ii. Stacked Long-Short Term Memory (LSTM)
   iii. Gated recurrent units (GRU) networks
   b. Applications
   i. Time series analysis and prediction
   ii. Natural language processing
   iii. Voice and text recognition

1. Introduction

• The importance of textual information in organizations and its main challenges
• Main stages of the text mining process

1. Information Extraction
   2.1 Document pre-processing using REGEX and Natural Language Processing
   2.2 Identification of relevant information (terms, phrases and entities)
2. Text mining techniques
   3.1 Introdução
   3.2 Information Retrieval

• Terms and occurrences lists
• Indexes
• Models and similarity measures
• Evaluation
  3.3 Documents classification
• Approaches for text classification/categorization
• Evaluation
  3.4 Document clustering
• Clustering tasks used in text analysis
• Algorithms for documents clustering
• Text clustering
  3.5 Text Summarization
• Main concepts
• Approaches for summaries generation
  3.6 Text mining tools

1. Cases studies

• Sentiment Analysis
• Chatbots
• Web mining
• Topic modelling
• Digital Libraries

1. Introduction to Big Data
   1.1 Big Data: Why?
   1.2 Big Data Features and Scalability Dimensions
   1.3 Data Science: Getting Big Data Value
   1.4 Fundamentals for Big Data systems and programming
2. Big Data Modeling and Management Systems
   2.1 Big Data Modeling
   2.2 Data models
   2.3 Big Data Management
3. Big Data Integration and Processing
   3.1 Integration and processing
   3.2 Retrieval in the relation model, NoSQL, data aggregation and data frames
   3.3 Big Data Integration
   3.4 Big Data Analytics
4. Machine Learning with Big Data
   4.1 Introduction to Machine Learning with Big Data
   4.2 Data exploration and visualization
   4.3 Data preparation
   4.4 Classification
   4.5 Evaluation of Machine Learning models

1. Introduction to Multivariate Analysis
   a) Covariance and Correlation Matrices
   b) The multivariate Normal density function
   c) The multivariate central limit theorem
   d) Scatterplot matrix
2. Principal Components Analysis
   a) Model
   b) Principal components method
   c) Maximum likelihood method
   d) Rotation factor
   e) Goodness of fit
   f) Dimensionality reduction in liner regression
   g) Score reliability
3. Cluster Analysis
   a) Dissimilarity measures
   b) Dendrogram
   c) Hierarchical methods
   d) Non-hierarchical methods
4. Discriminant Analysis and Classification
   a) Discriminant variables selection
   b) Classification statistic
   c) Classification of new data
5. Structural equation modelling
   a) Fundamental concepts
   b) Stages in structural equation modelling
   c) Applications of structural equation models

1.1 Time Series: definition
1.2 Components of a time series
1.3 Use of software to plot time series and to identify their components
1.4 Stacionarity
1.5 Autocorrelation and partial autocorrelation
1.6 Generalities on forecasting methods and precision measurements

2. Decomposition models
   2.1 Classical Decomposition
   2.2 Moving Averages
   2.3 Exponential smoothing
   2.4 STL Method (Seasonal-Trend Decomposition Procedure Based on Loess)
3. Box-Jenkins’ Linear models
   3.1 Stationary models (AR, MA and ARMA)
   3.2 Non-Stationary models (ARIMA and SARIMA)
   3.3 Parameters estimation, diagnostic checking and forecasts
   3.4 Applications with software
4. Conditional heterocedasticity models
   4.1 Volatility
   4.2 ARCH and GARCH models
   4.3 Applications with software