Modern Applied Statistics: Data Mining

STATS315B

Stanford School of Humanities and Sciences


Description

Examine new techniques for predictive and descriptive learning using concepts that bridge gaps among statistics, computer science, and artificial intelligence. This course emphasizes the statistical application of these areas and integration with standard statistical methodology. The differentiation of predictive and descriptive learning will be examined from varying statistical perspectives.

Prerequisites

  • Complete Data Mining and Analysis (Stanford Course: STATS202), Introduction to Statistical Learning (Stanford Course: STATS216) OR Introduction to Applied Statistics (Stanford Course: STATS191)
  • A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.

Topics include

  • Classification & regression models
  • Multivariate adaptive regression splines
  • Prototype & near-neighbor methods
  • Neural networks

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.

Pre-register Now

Dates:March 29 - June 4, 2021
Units: 3.00
Instructors: Jerome Friedman
Delivery Option:
Online
Fees:
For Credit $4,056.00
Notes: Pre-registration Dates: February 1, 2021 at 9:00am to March 12, 2021 at 5:00pm

Pre-registration for this course will secure your enrollment request and ensure timely processing of your application for potential course approval. Please note: course enrollment will be confirmed after March 19, 2021; after completing your pre-registration, no further action is required on your part.

 

This course may not currently be available to learners in some states and territories.