94280 - Data Management

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Peace, justice and strong institutions

Academic Year 2021/2022

Learning outcomes

At the end of the course the student will be aware of the importance to manage data sets for an efficient chemical assessment. The student is expected to be able to: 1. use statistical methods to validate analytical data and to in findi out correlation and trends; 2. identify the best experimental framework for an efficient data collection; 3. mamage large dataset.

Course contents

This CU is composed of three modules:

(1) Measuring Variability and Statistical Decision:
Deviations in experimental results: random, systematic and gross. How to measure and how to minimize random and systematic deviations. Parameters for measuring data dispersion. Parameters for measuring data location. The concept of probability and the role of statistics in Quality Control. Random distributions. Gaussian distribution. The Central Limit Theorem. Confidence intervals. Statistical decision tests: comparison of a mean to a reference value or comparison of two means (z-tests and t-tests, including paired t-test), comparison of variances (F-test), comparison of distributions (chi-square tests) and tests for outliers (Q-test). Analysis of variance: use of one-way and two-ways ANOVA in the analysis of data variability caused by different sources.

(2) Experimental Design and Optimization:
Description of a general optimization flowchart.
Assessment of optimization criteria. Single and multi-objective responses.
Application of screening methods for searching for relevant variables.
Application of statistic methods focused on the study of factors and interactions.
Implementation of strategies for simultaneous optimization of interacting variables.

(3) Reference Materials and Proficiency Testing Schemes
Different types of reference materials available and its proper use in an analytical laboratory, both in the validation step of an analytical method and in different steps of the laboratory quality control system (internal and external quality control). The preparation steps involved in production of RMs as well as all the analytical work required for homogeneity and stability tests. Different types of intercomparison exercises and the different steps involved in the organization of a PT scheme. The minimum statistical tools for the evaluation of the technical competence of a laboratory participating in a PTS will be introduced.


Lecture notes and selected papers will be available for students.

Recommended reading:

  • James N. Miller, Jane C. Miller, “Statistics and Chemometrics for Analytical Chemistry”, 4th ed., Prentice Hall, 2000.
  • Proficiency Testing in Analytical Chemistry. R.E. Lawn, M. Thompson, R. F. Walker. Royal Society of Chemistry. LGC (Teddington), 1997. ISBN: 0-85404-432-9.
  • Practical statistics for the analytical scientist. A bench guide. T. Farrant. Royal Society of Chemistry. LGC (Teddington), 1997. ISBN: 0-85404-442-6

Teaching methods

The course unit is divided in three modules taught independently at different times in the academic year.

Each module is organized in theoretical classes where main concepts are explained, as well as tutorial classes with discussion of case-study examples.

Assessment methods

Each module is assessed independently through homework assignments (20%) and a written assignment (80%).

The Curricular Unit grade will be the arithmetic mean of grades from the three modules.

Office hours

See the website of Isabel Maria Palma Antunes Cavaco

See the website of Javier Vicente Saurina Purroy

See the website of Ana Maria De Juan Capdevila