IDENTIFYING DATA

2018_19

Subject (*)

COMPUTER SCIENCE AND STATISTICS APPLIED TO ARCHAEOLOGY

Code

12785101

Study programme

Quaternary Archaeology and Human Evolution (Erasmus Mundus) (2012)

Cycle

2nd

Descriptors

Credits

Type

Year

Period

Compulsory

First

Language

Anglès

Department

History and History of Art

Coordinator

LORENZO MERINO, CARLOS

E-mail

sergio.lozano@urv.cat
carlos.lorenzo@urv.cat

Lecturers

LOZANO PÉREZ, SERGIO

LORENZO MERINO, CARLOS

Web

http://http://www.urv.cat/ca/estudis/masters/oferta/arqueologia-quaternari/

General description and relevant information

Understand the usefulness of various areas of statistics in the elaboration of archaeological studies. Provide the graduate student with basic concepts regarding certain IT (information technologies) necessary for their scientific and professional work.

Competences

Type A	Code	Competences Specific
		Common
	AC1	To evaluate the updated information in the different fields of research.
	AC2	Have analytical decision capability.
	AC3	To perform technical analysis protocols.
	AC5	Plan the archeological exploration and excavation intervention.
	AC7	To train yourself in the treatment and registration of data.
	AC9	To plan research projects.
	AC11	To have research capacity, generation of paradigm and demonstration.
Type B	Code	Competences Transversal
		Common
	BC1	Learning to learn
	BC2	Effective solutions to complex problems
	BC3	Critical, logical and creative thinking, and an ability to innovate
	BC8	Management of complex technical or professional projects
Type C	Code	Competences Nuclear
		Common
	CC1	Have an intermediate mastery of a foreign language, preferably English
	CC2	Be advanced users of the information and communication technologies
	CC3	Be able to manage information and knowledge

Learning aims

Objectives	Competences
OE1.- Apply descriptive statistics correctly in specific situations.	AC2 AC3 AC7 AC9 AC11	BC1 BC2 BC3 BC8	CC1 CC2 CC3
OE2.- Determine the treatment of specific data.	AC1 AC2 AC3 AC7 AC9 AC11	BC1 BC3 BC8	CC1 CC2 CC3
OE3.- Distinguish the most outstanding aspects of the sampling theory.	AC2 AC3 AC5 AC7 AC9 AC11	BC1 BC3 BC8	CC1 CC2 CC3
OE4.- Formulate the probabilities of specific events correctly.	AC2 AC3 AC5 AC7 AC9 AC11	BC1 BC2 BC8	CC1 CC2 CC3
OE5.- Understand the normal distribution in practical cases and the use of confidence intervals.	AC2 AC3 AC7 AC11	BC1	CC1 CC2 CC3
OE6.- Acquire the knowledge as a tool for contrast tests.	AC2 AC3 AC7 AC11	BC1 BC2 BC3	CC1 CC2 CC3
OE7.- Evaluate the results obtained with multivariate analysis.	AC2 AC3 AC7 AC11	BC2	CC1 CC2 CC3

Contents

Topic	Sub-topic
1. Introduction.	- Type of data. - Tables and graphic representations. - Software for statistical analysis.
2. Descriptive statistics.	- Descriptive statistics. - Data processing.
3. Probabilities and distributions.	- Sampling theory. - Probability. - Normal distribution.
4. Hypotheses testing.	- Intervals of confidence. - Contrasts from intervals. - Ji-square test. - Others tests.
5. Bivariate analysis.	- Analysis of the variance. - Correlation. - Regression.
6. Distance and cluster measurements.	- Measures of similarity. - Clusters methods.
7. Multivariate analysis.	- Factorial analysis and principal components analysis. - Correspondence analysis.

Planning

Methodologies :: Tests

Competences

(*) Class hours

Hours outside the classroom

(**) Total hours

Introductory activities

Lecture

Problem solving, exercises in the classroom

Previous study

Assignments

Personal attention

(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

Methodologies

	Description
Introductory activities	History of statistical and computer applications in the archeology of human evolution.
Lecture	Presentation, discussion and analysis of statistical and computer applications in the archeology of human evolution.
Problem solving, exercises in the classroom	The student must solve in the classroom the practical questions that will be proposed during the course. The student must solve some proposed practices in group.
Previous study	The student must prepare, for each session, the theoretical contents from the provided material.
Assignments	Presentation of an individual work on a statistical problem proposed by the professor in the field of prehistory.
Personal attention	Follow-up of the comprehension of the concepts given in the theoretical classes.

Personalized attention

Personal attention

Description

1.- Evaluate the individual contribution to the participation of the group work. 2.- Detect the doubts of the contents, in order to be able to correct them. 3.- Promote the search for information and the critical evaluation of the information found. 4.- Promote the use of new technologies and the search for new applications in archeology. 5.- Train the student the ability to choose the right tool for the specific problem. 6.- Provide the student with a critical capacity when assessing the results obtained from computational research. 7.- Provide tools for self-learning in the area of statistics.

Assessment

	Description	Weight
Problem solving, exercises in the classroom	Solving the problems proposed in the classroom and follow-up of classes.	20%
Assignments	Individual report to solve a specific statistical problem.	80%

Other comments and second exam session
Continuous evaluation model is followed. The resolution of the problems raised in the classroom and follow-up of the classes will score 20% of the qualification. The personal work will have 80% of the subject's weight.

Sources of information

Basic
	General Bibliography: - Banning, E.B. (2002). The Archaeologist's Laboratory: The Analysis of Archaeological Data. New York: Kluwer Academic Publishers. - Baxter, M.J. (1994). Exploratory Multivariate Analysis in Archaeology. Edinburgh: Edinburgh University Press. - Buck, C.E., Cavanagh, W.G. & Litton, C.D. (1996). Bayesian approach to interpreting archaeological data. Statistics in Practice. Wiley. - Drennan, R.D. (1996). Statistics for Archaeologists: A Commonsense Approach. New York: Plenum. - Fletcher, M. & Lock, G.R. (1991). Digging numbers. Elementary statistics for Archaeologists. Oxford: Institute of Archaeology. [2nd edition 2005] () - Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge: Cambridge University Press. - Martínez-González, M.A., de Irala, J. & Faulin Fajardo, F.J. (2006). Bioestadística amigable 2ª Edición. Madrid: Ed. Díaz de Santos.() -Orton, C. (1980). Mathematics in Archaeology. London: Collins. - Shennan, S. (1988). Quantifying Archaeology. Edinburgh: Edinburgh University Press.[2nd edition 1997] () - Shennan, S. (1992). Arqueología cuantitativa. Crítica Arqueología. Barcelona: Ed. Crítica. - Sokal, R.R. & Rohlf, J. (2002). Introducción a la Bioestadística. Barcelona: Ed. Reverté. - Sokal, R.R. & Rohlf, J. (2003). Biometry. The principles and practice of statistics in biological research - Third Edition. New York: W.H. Freeman and Company. - Spiegel, M.R. (2001). Estadística (Segunda Edición). Madrid: McGraw-Hill. () Highly recommended.
Complementary

Recommendations

(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.