Statistics for Data Science

Course details

Do you wish to be a data scientist but don't know where to begin? Want to implement statistics for data science? Want to get acquainted with R programs? Want to learn about the logic involved in computing statistics? If so, then this is the course for you.

This course will take you through an entire statistics odyssey, from knowing very little to becoming comfortable with using various statistical methods with data science tasks. It starts off with simple statistics and then moves on to statistical methods that are used in data science algorithms. R programs for statistical computation are clearly explained along with the logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.

By the end of the course, you will be comfortable with performing various statistical computations for data science programmatically.

About the Author

James D. Miller is an IBM-certified expert, creative innovator, director, senior project leader, and application/system architect with 35+ years extensive application, system design, and development experience. He has introduced customers to new and sometimes disruptive technologies and platforms, integrating with IBM Watson Analytics, Cognos BI, TM1, web architecture design, systems analysis, GUI design and testing, database modeling and systems analysis. He has done design and development of OLAP, client/server, web, and mainframe applications.