At the end of the curricular period, the student must:
LG1. Apply the concepts inherent to each of the phases of the CRISP-DM methodology concepts with real data.
LG2. Understand and apply summarization techniques.
LG3. Understand and apply segmentation techniques.
LG4. Understand and apply association techniques.

P1. Exploratory data analysis
P1.1. Data classification
P1.2. Data quality: missing values and outliers
P2. Summarization techniques:
P2.1. Descriptive measures
P2.2. Graphs
P2.3. Principal Components Analysis
P3. Segmentation techniques
P4. Association techniques
P5. Applications of descriptive techniques with real data, using IBM SPSS Modeler and IBM SPSS Statistics; or others.

1) Periodic evaluation:
a) 2 individual Exercises realized in class with a weight of 15% each with a minimum grade of 8,5 points (out of 20)
b) A written individual final test with 70% of weight and a minimum grade of 8,5 points (out of 20)
The periodic evaluation requires attendance in at least 2/3 of the classes and approval will be obtained by students with a final minimum overall grade of 10, provided that they did not have a grade lower than 8.5 in any of the written individual tests.
2) Exam: Exam with a theoretical component (70%) + Exam with a practical component (30%), requiring a min grade of 8,5 in each exam, provided that the final classification is at least 10 points.

At the end of this learning unit?s term, the student must be able to:
LG1. To identify and apply concepts and technologies associated with text mining and social network analysis in order to recommend action or to guide decision making rooted in unstructured data;
LG2. To apply text mining techniques to better understand and handle existing business problems;
LG3. To develop the personal capacity of studying, searching and communicating at the level of Text Mining.

PC1. Introduction to Text Mining
PC2. Tokenization, Dictionary Creation and Corpus Preparation
PC3. Clustering Methods for Text Mining
PC4. Practical cases on using clustering methods in text mining for business
PC5. Classification Methods for Text Mining
PC6. Practical cases on using classification methods in text mining for business
PC7. Sentiment Analysis
PC8. Practical cases on using sentiment analysis for business

1) Continuous evaluation:
a) Written test (50%)
(LG 1, 2, 3)
b) A group coursework (30%)
(LG 1, 2, 3)
c) Participation in class (10%)

Requires a minimum grade of 7.5 points in each element, attendance to classes of at least 2/3, and a minimum of 10 points in the final classification.

2) Exam (1st sitting): written test (100%), requiring minimum 10 points to get approval.
3) Exam (2nd sitting): written test (100%), requiring minimum 10 points to get approval.

Scale: 0-20 points.

LO1. To know CRISP-DM methodology
LO2. To understand the framework to implement business analytics projects
LO3. To know the main computer tools for business analytics
LO4. To know same business analytics applications

P.1. Business analytics in the organizational context
P1.1. Framework to implement
P1.2. CRISP-DM methodology
P1.3. Analytics maturity
P2. Business analytics applications
P2.1. Data problems
P2.2. Business intelligence problems
P2.3. Analytics problems

1) Periodic evaluation:
a) Individual work with oral and digital presentation (70%)
(LO 1, 2, 3, 4)
b) Group work with possible discussion (30%)
(LO 3, 4)

Requires a minimum grade of 7.5 points in each element, attendance to classes of at least 4/5, and a minimum of 10 points in the final classification

Scale: 0-20 points

Given the practical nature of the content of the course, no exam is scheduled.

LG1. To develop some of the concepts underlying the strategic management process and understand the ways different theoretical perspectives approach this process.
LG2. To understand and apply the concept of dynamic capabilities and its approach to strategy.
LG3. To introduce some of the concepts underlying the formulation and development of business and corporate strategies, including business environment evaluation and stakeholder analysis.
LG4. To understand the importance of implementation and control in the strategic management process and its relationship with reporting and business analytics.
LG5. To create control systems for performance evaluation and information reporting for business management.
LG6. To develop critical thinking.
LG7. To have autonomy to plan learning processes and advance knowledge in the area under study.

T1. Strategy as starting point for business analytics.
T2. The strategic management process.
T3. Internal analysis of the organization based on its resources and dynamic capabilities.
T4. External environment analysis.
T5. Business strategies, new business models and digital transformation.
T6. Corporate strategies.
T7. Strategy implementation and control, reporting and business analytics.

Option 1 (Continuous Assessment)

i. Individual Written Test 50%
ii. Group Assignment 40%
iii. Seminar Report 10%

To successfully complete the course, students must obtain a weighted average of 10 points or more, with at least 8 in each component of the continuous assessment.

Option 2 (Final Exam)
To pass: 10 or more points.

20-point scale.

1. Data base management background in the data-science universe
2. Develop data modeling capabilities in the context of operational and analytical systems.
3. Ability to use with efficiency and effectiveness the languages and tools for information research in structured and semi-structured data

A. Data management background in the data-science universe
B. Relational Schemas Design
1. Relations and primary keys
2. Foreign keys and rules of integrity;
3. Critical analysis and construction of a relational model;

C. Language S.Q.L
1 Simple Interrogations;
2 Aggregation and Grouping Functions;
3 Chained Interrogations;
4. Creation of Views

D. Optimization

E. Dimensional Model
1. Dimensional model design
2. Dimensional vs relational model
3. ETL & data quality

F. Semi-structured data

1st sitting
1)Periodic evaluation:
i) group work with phased deliveries and discussion (50%);
ii) individual written test (50%).
Approval: Minimum final score of 10 values; minimum written test score 8 values.

2)Evaluation by exam: written test (100%), minimum score 10 values.

2nd sitting
Evaluation by exam: written test (100%), minimum score 10 values.

Scale: 0-20 values.

LO1. Identify variables and metrics relevant to classification and regression and define the goals of the analysis
LO2. Classification techniques: decision trees, propositional rules and neural networks
LO3. Regression techniques: linear regression, decision trees and neural networks
LO4. Applications of classification and regression techniques on real data

P1. Exploratory data analysis and metrics for classification and regression
P2. Classification techniques:
P2.1. Decision trees and propositional rules
P2.2 Neural networks: the backpropagation algorithm
P2.3 Other algorithms for classification problems
P3.1. Linear regression
P3.2. Decision trees
P3.3 Neural networks: the backpropagation algorithm
P3.4. Other algorithms for regression problems
P4. Applications of classification and regression with real data, using IBM SPSS Modeler and IBM SPSS Statistics; or other

1) Periodic evaluation:
a) Written test (50%)
(LO 1, 2, 3)
b) A group project with digital presentation (50%) and possible discussion
(LO 4)

Requires a minimum grade of 7.5 points in each element, attendance to classes of at least 2/3, and a minimum of 10 points in the final classification.

2) Exam (1st sitting): project with discussion and digital presentation (50%) and written test (50%), requiring minimum 10 points in each assignment to get approval.
3) Exam (2nd sitting): project with discussion and digital presentation (50%) and written test (50%), requiring minimum 10 points each assignment to get approval.

Scale: 0-20 points.

At the end of this Curricular Unit, the student is expected to be able to:
LO1: Identify prescriptive decision support models appropriate for applications in management.
LO2: Identify prescriptive decision support techniques appropriate for applications in management.
LO3: Develop decision support models for applications in management.
LO4: Use general software to solve decision support models.
LO5: Interpret and produce recommendations based on the results of decision support models.

S1. Mathematical optimization models and techniques
1.1 Linear Optimization
1.2 Optimization with binary variables
1.3. Nonlinear Optimization
1.4 Applications of mathematical optimization in Management
1.5 Resolution using software
1.6 Make prescriptive recommendations
S2. Multiobjective optimization models and techniques
2.1 Modelos multiobjectivo
2.2 Applications of multiobjective optimization in Management
2.3 Resolution using software
2.4 Make prescriptive recommendations
S3. Heuristic optimization techniques
3.1 Introduction to heuristic optimization
3.2 Applications of heuristic optimization in Management
3.3 Resolution using software
3.4 Make prescriptive recommendations
S4. Other prescriptive models and techniques for Management

1. PERIODIC ASSESSMENT:
a) Written test (60%);
b) Group coursework with discussion (40%);
c) Attendance of at least 2/3 of the classes.
2. ASSESSMENT BY EXAM (1st and 2nd Season):
a) Written test (60%);
b) Individual project with discussion (40%);
Approval (in the Periodic or Exam evaluation):
i) Requires a minimum mark of 8.5 in each examination;
ii) An oral discussion may be required.
Scale: 0-20 points.

LG1. Understand the basic concepts of Big Data and its implications in different fields of management
LG2. Apply analytical models with Big Data
LG3. Evaluate alternative analytical solutions with Big Data

P1. Big Data: Introduction, challenges, trends and applications
P2. Big Data characterization: The V's of Big Data
P3. Big data technologies
P4. Stream Analysis and analytical models for Big Data
P5. Business cases about Big Data problems and analytical solutions

1. Periodic evaluation: a) Group assignment with presentation (50%). (LG 2, 3); b) Individual test (50%). (LG 1, 2, 3).
Approval by periodic evaluation requires:
- students attendance of at least 75% of classes;
- minimum grade of 7,5 in the individual test and the group assignment;
- minimum final grade of 10.

2. Evaluation by exam: Individual test (50%) and Individual assignment with presentation (50%).
Approval by exam requires:
- minimum grade of 10 for each of the evaluation elements;
- minimum final classification of 10.

LG1. To understand the key concepts and methodologies of a business case
LG2. To formulate an analytical business case
LG3. To deliver and communicate an analytical business case

P1. Business case concept and elements
P1.1. Fundaments of project management
P1.2. The main frameworks
P2. Types of analytical business cases
P3. The role of consulting
P3.1. Analytical business case design
P3.2. Analytical business case delivery
P4. Analytical business cases examples

Individual written work and its presentation and discussion (50%)
(LG 1, 2, 3)
Team work and its presentation and discussion (50%)
(LG 1, 2, 3)

Requires a minimum grade of 10 points in both works, and attendance to classes of at least 2/3.

Scale: 0-20 points.

There is only no final exam in this curricular unit.

LG1. To know how to differentiate: a dissertation and a business analytics project
LG2. To understand the philosophy that underpins the choices and decisions to be made in taking a research position
LG3. To know how to select a research problem and review the literature
LG4. To know how to write a business analytics thesis project
LG5. To know how to communicate a research problem to managers

P.1 The stages of research
P1.1.Types of thesis
P1.2. Identifying the research problems
P1.3. Planning the research stages
P2. Doing a literature review and identifying the main literature sources
P3. Types and stages of empirical work in business analytics
P3.1. Identifying and using the business analytics tools to solve the research problem
P4. Research topics in business analytics
P5. Development and formal presentation of the research project
P5.1. Tutorial and individual orientation, supporting each student in the development of his project

Periodic assessment:
- Thesis project with oral and digital presentation (70%)
- Critical review of a business analytics research (10%)

- Protocol for literature review in the format of a scientific article with, possible, oral presentation (20%)

Requires a min. grade of 7.5 pts. in each element, attendance to classes of at least 2/3, and a min. of 10 pts. in the final classification.

Scale: 0-20 points.

This course does not have a final exam.

LG1. Writing a dissertation
LG2. Writing a synthesis of the thesis
LG3. Public oral presentation of the synthesis of the thesis

P1. Writing the introduction and abstract (resumo);
P2. Definition of a research problem;
P3. Definition of research goals;
P4. Literature review;
P5. Defining hypothesis or the analytical objectives and monitoring metrics;
P6. Data collection methods and/or data understanding and preparation;
P7. Data analysis methods (modelling) and evaluation;
P8. Writing conclusions and defining new research paths;
P9. Recommendations and Implications in academic and practical terms

- Written presentation of the dissertation (80%)
- Oral presentation with the synthesis of the dissertation followed by a public defense with a jury (20%)

LG1. Writing a master project
LG2. Writing a synthesis of the master project
LG3. Preparing a public oral presentation of the synthesis of the master project

P1. Writing the introduction and abstract (resumo);
P2. Definition of the business problem and diagnosis of organizational environment;
P3. Definition of the project goals;
P4. Applied literature review;
P5. Defining the analytical objectives and monitoring metrics;
P6. Data understanding and preparation;
P7. Data analysis methods (modelling) and evaluation;
P8. Writing conclusions and defining new projects paths;
P9. Evaluation of impacts and possibilities of control of results.

- Written presentation of the thesis (80%)
- Oral presentation with the synthesis of the project followed by a public defense with a jury (20%)