Business Analytics
Keywords |
Classification |
Keyword |
OFICIAL |
Quantitative Methods |
Instance: 2018/2019 - 2S
Cycles of Study/Courses
Acronym |
No. of Students |
Study Plan |
Curricular Years |
Credits UCN |
Credits ECTS |
Contact hours |
Total Time |
MIEGI |
78 |
Syllabus since 2006/2007 |
4 |
- |
6 |
49 |
162 |
Teaching Staff - Responsibilities
Teaching language
English
Objectives
Currently companies have invested in systems able to collect data that can be analyzed and transformed into useful knowledge to support decision making. It is in this context that arises the Business Analytics course.
Objectives:
- Make the students aware of the importance of data analytics techniques to support the decision making process;
- Promote the development of student's skills to correctly use data analytics techniques in the industry and services sectors;
- Promote the use of software to support the development of data analytics projects.
Learning outcomes and competences
At the end of the course, students will be able to:
- Identify decision support problems that can be solved by data analytics techniques;
- Understand the stages of the process of data analytics and knowledge extraction;
- Know the main data analytics techniques and understand their main characteristics;
- Apply these methods to decision support problems;
- Evaluate the result of a data analtycs project;
- Know a software to support the development of data analytics projects.
Working method
Presencial
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
Knowledge of statistics and basic knowledge on relational databases.
Program
This course will cover the following topics:
- Introduction to Business Analytics (Data mining, the KDD process, Data mining applications, Big data)
- Data characterization and data preprocessing (Types of data, data cleaning, data normalization and data reduction)
- Visualization as a tool for data understanding
- Non-supervised data mining (clustering analysis, association rules)
- Supervised data mining (classification, prediction, evaluation methods)
- Temporal mining (Time series clustering and classification)
- Text mining (data representation and analysis)
- Recommender systems (neighborhood-based collaborative filtering methods and SVD methods)
Mandatory literature
Pang-Ning Tan, Michael Steinbach, Vipin Kumar;
Introduction to Data Mining: Pearson New International Edition. ISBN: 1-292-02615-4
Complementary Bibliography
Yanchang Zhao;
R and Data Mining: Examples and Case Studies. . ISBN: 978-0-123-96963-7 (ftp://cran.r-project.org/pub/R/doc/contrib/Zhao_R_and_data_mining.pdf)
Jiawei Han, Micheline Kamber, Jian Pei;
Data Mining: Concepts and Techniques, Third Edition. ISBN: 9380931913
Vijay Kotu and Bala Deshpande;
Predictive Analytics and Data Mining - concepts and practice with rapidminer, Morgan Kaufmann, 2015. ISBN: 978-0-12-801460-8
Comments from the literature
Additional bibiography will be recommended in the classes.
Teaching methods and learning activities
This course will be composed by:
- Theoretical presentation and discussion of the concepts
- Laboratory sessions for practical application of the concepts learned
Software
RapidMiner
R
Evaluation Type
Distributed evaluation with final exam
Assessment Components
Designation |
Weight (%) |
Exame |
55,00 |
Trabalho prático ou de projeto |
45,00 |
Total: |
100,00 |
Amount of time allocated to each course unit
Designation |
Time (hours) |
Elaboração de projeto |
50,00 |
Estudo autónomo |
63,00 |
Frequência das aulas |
49,00 |
Total: |
162,00 |
Calculation formula of final grade
0.45 * Project Grade + 0.55 * Exam Grade
Special assessment (TE, DA, ...)
The project is mandatory to all students.
Students with special status (student union leaders and high-level competition athletes) can opt to be assessed as normal students, according to the rules previously described.
Alternatively, they can take the final exam at the period especially designed for students with special status.
In this case, the weight of the exam remains equal to 55% and the weight of the group assignment equal to 45%.
Classification improvement
Students may repeat the exam in the appeal period. Students may also improve their classification in the following academic year by repeating the exam.
The classification of the project will be considered in the case of repeating the exam to improve the classification in the appeal period, or in the following academic year.