Course Offerings

Introduces the tools of statistical analysis. Combines theory with extensive data collection and computer-assisted laboratory work. Develops an attitude of mind accepting uncertainty and variability as part of problem analysis and decision-making. Topics include: exploratory data analysis and data transformation, hypothesis-testing and the analysis of variance, simple and multiple regression with residual and influence analyses.

This project-based course introduces data science by looking at the whole cycle of activities involved in data science projects. Students will learn how to think about problems with rigor and creativity, ethically applying data science skills to address those problems. The course project will address the theoretical, mathematical and computational challenges involved in data science.

The 21st century has seen a big increase in the amount of data which is made accessible. Social media such as Facebook, online shops such as Amazon and many others, are all gathering raw data. But what can be done about this data? Data Science covers tools and methods around the extraction of knowledge from data. Such tools cover its collection, storing, processing and analysis. In this course we will learn about several of the most important tools in the above flow and will apply them to real-world examples.

This course is designed to extend the statistical analysis of environmental and social science data: it will highlight the building blocks of multivariate analysis from the definition of the research problem to the interpretation of the results. Both dependence methods (that is in which one or several variables can be expressed in terms of the others – for instance Multivariate Analysis of Variance or Discriminant Analysis) and interdependence methods (where all the variables are analysed simultaneously – for instance Factor & Cluster Analyses or Multidimensional Scaling) will be studied.
Significant applications will be analysed and discussed so as to develop new insights.
Projects (individual or with peers), will allow the students to apply the multivariate models, thereby enhancing the importance of work and knowledge sharing.
Statistical software package: SPSS.
Prerequisite: MA 1020