Checking date: 30/05/2021

Course: 2021/2022

Data analysis in marketing
Study: Master in Marketing (269)

Coordinating teacher: KAISER REMIRO, REGINA

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Not required.
COMPETENCES CB6... CG1... CG2 Effective knowledge of other disciplines / techniques used in Marketing and Market Research. CG6 Ability to search and analyze information from different sources. CG7 ... CE1 ... CE4 To learn the qualitative and quantitative tools for market research, to choose and apply the most appropriate technique to every problem, and understand the potential of computer tools in this area. CE5 To understand and use statistics and econometrics tools to analyze data and marketing problems through scientific models, using appropriate software. CE6 ... LEARNING OUTCOMES The student identifies essential methodologies for data analysis in the final stages of market research The student evaluates the results of the various types of market research used in marketing. The student analyzes the results of the studies from a marketing management perspective, and designs a marketing strategy in the company based on these results.
Skills and learning outcomes
Description of contents: programme
Chapter 1. Descriptive statistics for marketing analysis. 1.1 Introduction. 1.2 Types of marketing data. 1.3 Scalar measures. 1.4 Graphical displays. Sampling. SPSS examples. Chapter 2. Inferential statistics. 2.1 Basic foundation of inferential statistics. 2.2 Point and interval estimation of population parameters. 2.3 Testing of hypotheses about population parameters. 2.4 Hypotheses about the differences among two populations. SPSS examples. Chapter 3. Associative statistics. 3.1 Concept of association among two variables. 3.2 Types of relationships. 3.3 Cross tabulations and chi-square analysis. 3.4 Correlation. SPSS examples. Chapter 4. Predictive statistics. 4.1 Basic concept of prediction and regression analysis. 4.2 Bivariate and multiple linear regressions. 4.3 Other models. SPSS examples
Learning activities and methodology
TRAINING ACTIVITIES OF THE SUBJECT AF3 Theoretical practical classes AF6 Group work AF7 Individual student work Activity code / No. of total hours / No. of face-to-face hours /% face-to-face AF3 105 105 100 AF6 145 0 0 AF7 125 0 0 TOTAL SUBJECT 375 105 TEACHING METHODOLOGIES MD1 Lectures in the teacher's class supported by computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2 Critical reading of texts recommended by the professor of the subject: Sentences and resolutions, press articles, reports, manuals and / or academic articles, either for later discussion in class, or to expand and consolidate the knowledge of the subject. MD3 Resolution of practical cases, problems, etc.¿ raised by the teacher individually or in groups MD4 Presentation and discussion in class, under the moderation of the teacher, of topics related to the content of the subject, as well as practical cases MD5 Preparation of works and reports individually or in groups
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Devore, J.L., and N.R. Farnum. Applied Statistics for Enginners and Scientist, 2nd Edition. Duxbury Press, . 2004
  • Lind, D. Marchal, W.G. and Wathen, S. . Statistical Techniques in Business and Economics. 15th Edition.. Irwin/McGraw-Hill. 2011
  • Siegel, A.F.. Practical Business Statistics.. Academic Press.. 2011

The course syllabus may change due academic events or other reasons.