Data Science: Building Recommender Systems with SAS and Hadoop

Business Knowledge Series course

Presented by Catherine Truxillo, Ph.D., Director, Analytical Education, Education Division, SAS; or Bob Lucas, Ph.D., Analytical Training Consultant, Education Division, SAS; or Terry Woodfield, Ph.D., Analytical Training Consultant, Education Division, SAS; or David Ghan, Technical Training Consultant, Education Division, SAS; or Andy Ravenna, Technical Training Consultant, Education Division, SAS

This introduction to working with Apache Hadoop is designed for statisticians, as well as experienced SAS programmers with a background in statistics. The course is problem-driven and focuses on helping you understand what data scientists do, the problems they solve, and their methods. By taking a practical approach to the subject, including multiple hands-on exercises, you will leave class with skills that you can immediately apply to real-world problems. You also learn how recommender systems can be leveraged in industries such as Health Care, Finance, and Telecom.

Learn how to

• describe the role and responsibilities of a data scientist
• explain several ways in which data scientists create value for organizations across many industries
• locate and acquire data from diverse sources
• use transformation and normalization techniques on both structured and unstructured data
• determine the most appropriate type of analysis and modeling tool to use for a given problem
• be able to implement an automated recommendation system
• develop, evaluate, and refine scoring systems for recommenders
• understand the considerations involved in working at scale
• identify meaningful, actionable, and business-oriented results from the analysis.

Who should attend

Data scientists, SAS programmers, and statisticians who have some basic familiarity with Apache Hadoop

Formats available	Duration
Classroom:	2.0 days

Before attending this course, you must

• be familiar with Base SAS software
• be able to write basic SQL queries
• have predictive modeling knowledge at the level acquired in Predictive Modeling Using Logistic Regression
• have a basic understanding of Apache Hadoop at the level acquired in Introduction to SAS and Hadoop.

This course addresses SAS/STAT software.

Introduction

Data Science Overview

Apache Hadoop Overview

Use Cases

Project Lifecycle

Data Acquisition

Evaluating Input Data

Data Transformation

Fundamentals of Machine Learning

Recommender Overview

Implementing Recommenders with MapReduce and SAS

Experimentation and Evaluation

Production Deployment and Beyond

Conclusion