The focus of this course is to introduce students to scientific methods, role of experimental design and statistics in ecological and environmental studies, nature of experimentation and principles of experimental design, random variables, probability and stochastic distributions, exploratory data analysis, design of experiments, inferential statistics , hypothesis testing, statistical modeling, regression, Analysis of Variance and Analysis of Covariance, mixed model and multivariate analyses in ecological and environmental studies. In addition, this course aims to give students a practical skill in working with data using a powerful statistical analysis tool – R programming language.
This course gives an overview of fundamental topics in data science. Topics include: history and
recent advances in data science, overview of data manipulation, data
exploration, introduction to data mining and model evaluation. The first
half of the course focuses on analyzing real datasets and the second
half on various techniques for prediction tasks. This course will allow
students to have a hand-on experience on different datasets using the
Weka software package for data analysis.
Introduction to data science, overview of data
manipulation, data exploration, introduction to data mining, model evaluation, case
studies in data science