Data Mining Techniques: Predictive Analytics on Big Data

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Not attempted Data Mining Techniques: Predictive Analytics on Big Data

Duration: 17.5 hours

This course introduces applications and techniques for assaying and modeling large data. The course also presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models, and mixture distribution models. Students perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics, and SAS In-Memory Statistics.

Learn How To

Use applications designed for big data analyses.

Explore data efficiently.

Reduce data dimensionality.

Build predictive models using decision trees, regressions, generalized linear models, random forests, and support vector machines.

Build models that handle multiple targets.

Assess models using validation and cross-validation techniques.

Implement models and score new predictions.

Who Should Attend

Business analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, and others who work in related fields

Prerequisites

Before attending this course, you should have at least an introductory-level familiarity with basic statistics and linear models. Previous SAS software experience is helpful but not required.

SAS Products Covered

SAS In-Memory Statistics;SAS Enterprise Miner;SAS Visual Statistics

Course Outline

Introduction

Introduction to big data and SAS big data tools.Data Exploration

Data exploration and clustering (SAS Visual Statistics).

Data exploration and dimension reduction.Analysis Methods for Categorical Targets

Categorical targets (SAS Visual Statistics).

Categorical targets (SAS In-Memory Statistics).Analysis Methods for Interval Targets

Interval targets (SAS Visual Statistics).

Interval targets (SAS In-Memory Statistics).

Interval targets (SAS Enterprise Miner).