in Data Analytics
Date
Courses
Provides an overview of quantitative methods essential for analyzing data, with an emphasis on business applications. Topics include identification of appropriate metrics and measurement methods, descriptive and inferential statistics, experimental design, parametric and non-parametric tests, simulation, and linear and logistic regression, categorical data analysis, and select unsupervised learning techniques. Standard and open source statistical packages are used to apply techniques to real-world problems.
Contemporary techniques of multivariate analysis, including association rules, classification methods, time series, text analysis, and machine learning methods with an emphasis on applications in science and industry. Introduction to state-of-practice computational statistical and data analysis methods and tools.
Provides an introduction to collecting, storing, managing, retrieving, and processing datasets. Techniques for large and small datasets are considered, as both are needed in data science applications. Emphasizes applications and includes many hands-on projects.
This course covers the latest methods for discovery and learning from large data sets. Topics complemented by hands-on projects using data discovery and statistical learning software.
This course covers the latest methods for discovery and learning from large data sets. Topics complemented by hands-on projects using data discovery and statistical learning software.