Introduction to Data Analytics
Objectives
1. Be able to identify the most appropriate descriptive and inferential analytical methods to apply in order to address problems in the area of law and financial markets.
2. Be able to apply each technique and to interpret its results.
3. Be able to understand the limitations and conditions of application of the different techniques.
General characterization
Code
33163
Credits
6
Responsible teacher
Pedro Simões Coelho (IMS)
Hours
Weekly - 3
Total - 36
Teaching language
English
Prerequisites
Available soon
Bibliography
- Foster Provost, Tom Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data-analytic Thinking. ISBN 1449361323
- Hair, J., Anderson, R., Tattham, R. and Black, W., Multivariate Data Analysis with readings, Prentice Hall, 1995, ISNN 0023490209.
- Sharma, S., Applied Multivariate Techniques, Wiley, 1996..
- Vilares, M. J.; Coelho, P. A Satisfação e a Lealdade do Cliente. Metodologias de Avaliação, Gestão e Análise., 2ª Edição, Escolar Editora.,2011.
Teaching method
Presentation of the methods followed by real-life examples and exercises. Execution of a project with the application of descriptive and inferential techniques in real data.
Evaluation method
Apresentação dos métodos seguidos de exemplos e exercícios da vida real. Execução de um projecto com a aplicação de técnicas descritivas e inferenciais em dados reais.
Subject matter
- Introduction
- Statistical Variables and types of data
- Data analysis for one variable
- Frequency distributions and histograms
- Central tendency indicators
- Dispersion indicators
- Data analysis for two variables
- Scatter plots
- Covariance and correlation
- Contingency tables
- Statistical Inference
- Sampling
- Confidence intervals and accuracy measures
- Hypotheses testing
- Methodology
- Mean, total and proportion
- Differences of means, totals and proportions
- Association tests
- Index numbers
- Principal Components Analysis
- Regression Analysis