Industrial Statistics

Recommended Prerequisites

Numerical Linear Algebra, Statistics

Teaching Methods

The teaching methods are based on a combination of conventional classes where the topics are motivated and introduced, with the support of slides, software and illustrations (theoretical classes) and classes demonstrating concepts and their computational implementation (practical classes). In the course of the classes’ period, the students consolidate the learning outcomes with projects carried out in groups where the methods and tools are applied autonomously under the supervision of the teacher.

Learning Outcomes

The fundamental learning objectives for the course are broken down into objectives for the acquisition of hard skills and soft skills. Regarding hard skills, students are expected to: understand the functional pillars of a quality system and the importance of data analysis in each one of them; understand the fundamental differences between observational data and actively collected data, and their consequences; recognize the importance of exploratory data analysis especially regarding the use of graphical tools; acquire know-how in the methodologies for non-supervised learning and supervised learning addressed during the course. With regard soft skills, students are expected to be able to: systematically analyze complex problems, work in teams and communicate effectively results.

Work Placement(s)

No

Syllabus

1. Quality planning, improvement, control assurance in organizations
2. Industrial data and its characteristics
3. Typologies of problems in the analysis of industrial processes
4. Data pre-processing
5. Multidimensional visualization
6. Unsupervised learning in industrial processes
7. Supervised learning in industrial processes. Soft sensors.
8. Advanced process monitoring. Detection, diagnosis and prognosis.
9. Process Capability
10. Optimal design of experiments. Quality by Design

Head Lecturer(s)

Marco Paulo Seabra dos Reis

Assessment Methods

Assessment
Exam: 50.0%
Project: 50.0%

Bibliography

•EMC Education Services (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Wiley.

•Kenett, R.S. Zacks, D. Amberti (2014). Modern Industrial Statistics: with applications in R, MINITAB and JMP, 2nd Edition. Wiley.

•Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. NY: Springer.

•Eriksson, L., Johansson, E., Kettaneh-Wold, N., & Wold, S. (2001). Multi- and Megavariate Data Analysis – Principles and Applications. Umeå (Sweden): Umetrics AB.

•Jolliffe, I. T. (2002). Principal Component Analysis (2nd ed.). New York: Springer.

•Han, J., & Kamber, M. (2001). Data Mining - Concepts and Techniques: Morgan Kaufmann.

•Van der Heijden, F., Duin, R. P. W., De Ridder, D., & Tax, D. M. J. (2004). Classification, Parameter Estimation and State Estimation. Chichester: Wiley.