Business Analytics

Code:

EIG0067

Acronym:

AE

Keywords
Classification	Keyword
OFICIAL	Quantitative Methods

Instance: 2020/2021 - 2S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Engineering and Industrial Management

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEGI	89	Syllabus since 2006/2007	4	-	6	49	162

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)


• Text mining (data representation and analysis)

 

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

Tableau
RapidMiner
R

Evaluation Type

Evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	113,00
Frequência das aulas	49,00
Total:	162,00

Eligibility for exams

Students have to attend classes according to the "General Evaluation Rules" of FEUP Pedagogical Council.

Calculation formula of final grade

Final Grade = Exam Grade

Due to the covid-19 exceptional circunstances this year the project is not part of the evaluation.

P: Project Grade
E: Exam Grade

For students whose project grade exceeds the exam grade by more than 4, the final grade will be given a project grade of P = E + 4.
Students who obtain less than 8 points in the exam will not be approved regardless of the grade obtained in the project.

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.