Data Processing in Chemistry

Code:

Q1010

Acronym:

Q1010

Level:

100

Keywords
Classification	Keyword
OFICIAL	Chemistry

Instance: 2018/2019 - 1S

Active?	Yes
Responsible unit:	Department of Chemistry and Biochemistry
Course/CS Responsible:	Bachelor in Chemistry

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:BQ	85	Official Study Plan	1	-	3	28	81
L:Q	59	study plan from 2016/17	1	-	3	28	81

Teaching language

Portuguese

Objectives

Search scientific information on the Internet in a systematic way. Handle and present numerical data in a clear and rigorous way.

Learning outcomes and competences

At the end of this course the student should be able to:

1- Search for published scientific articles, by using various search criteria in specific databases.

2- Find data related to properties of chemical compounds using appropriate databases.

3- Perform elementary treatment of numerical data in a spreadsheet, plot graphs and do basic statistical analysis.

4- Visualize and manipulate 2D and 3D molecular models using appropriate computer programs.

Working method

Presencial

Program

1. Searching for scientific information on the Internet: specific databases.

1.1. Scientific publications - Scopus ; Web of Science.

1.2. Physicochemical properties of compounds - NIST Chemistry WebBook ; ChemSpider

1.3. Structure of proteins - PDB Protein Data Bank.

1.4. Spectra of organic compounds - SDBS

1. Handling numeric data in a spreadsheet.

2.1. Significant figures.

2.2. Basic calculations.

2.3. Statistical analysis of experimental data.

2.3.1. Descriptive statistics. Confidence intervals for the sample mean.

2.3.2. Basics of Statistical Hypothesis testing.

2.4. Fitting functions to experimental data. The method of least squares and the particular case of linear regression. Interpolation.

2.5. Plotting graphs.

2.6. Roots of nonlinear equations. Concept of iterative methods. Newton method.

1. Drawing molecular structures. Freeware for visualization and manipulation of molecular models: Avogadro; ACD / Labs ; VMD.

Mandatory literature

Natália Cordeiro, Alexandre Magalhães,; Introdução à Estatística: Uma Perspectiva Química, LIDEL- Edições Técnicas Lda, Lisboa, 2004

Teaching methods and learning activities

Classes take place in a room equipped with video projector and computers (one per student).

Synchronization between presentation of theoretical topics and direct application on the computer by the students.

Solving practical exercises using the computer.

 

Distributed evaluation without final exam. The classification shall be calculated on the basis of two practical assessments conducted throughout the semester (with 50% weight each).

On the one hand, the syllabus allows the student to gain experience of search relevant scientific information on the internet in a rational and critical way. On the other hand, the student will learn how to process and present numerical data clearly and accurately. These skills are fundamental to beginners in science.

Learning based on digital media aims to stimulate autonomy, rigor and critical spirit in information retrieval and processing of data in science. In a field where a constant knowledge update is necessary, the development of cognitive capacities is as important as the contents the student learns in the course.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Teste	100,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	51,00
Frequência das aulas	28,00
Trabalho laboratorial	2,00
Total:	81,00

Eligibility for exams

The students must attend at least 75% of the total number of practical classes.

Calculation formula of final grade

Distributed evaluation without final exam. The classification shall be calculated on the basis of two practical assessments to take place during the semester (with 50% weight each).