in Data Analytics
Tuition & Fees
Provides an overview of quantitative methods essential for analyzing data, with an emphasis on business and industry applications. Standard and open source statistical packages are used to apply techniques to real-world problems.
Exploratory Data Analysis & Probability
Sampling, Inference, and Hypothesis Testing
Correlation & Regression: Generating Solutions
Course extends the concepts developed in ADTA 5130 Data Analytics 1 to multivariate and unstructured data analysis. Modern techniques of multivariate analysis, including association rules, classification methods, time series, text analysis and machine learning methods are explored and implemented with real-world business and industry data. Course provides hands-on introduction to state-of-practice technology and tools. The focus of the course is on the application and interpretation of the methods discussed.
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.
Storing & Harvesting Data
Principles of Data Wrangling and Queries
Principles of Data Structures
Presents strategies and methods for effective visualization and communication of large data sets. Standard and open source data visualization packages are used to develop presentations that convey findings, answer business questions, drive decisions and provide persuasive evidence supported by data.
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.