(English follows Japanese)
Japanese title: Big data? Solve data with JMP®.
Rawlins University, International Economy (Orlando, Florida) Associate Professor Michele Boulanger
SAS Institute Inc. JMP Academic Ambassador Mia Stephens
Our ability to acquire transaction data in most areas has opened the era of “big data”.What is “big data”?Does “big” necessarily mean dirty, messy, inconsistent or unwieldy? How much adjustment and adjustment is needed in the world of big data?What is the distinction from the big data world preparation and cleansing stage?In this talk, we will discuss the various challenges encountered in potentially the most time-consuming stages of big data analysis.In other words, we discuss data preparation.Two examples will be presented, but each has a very different purpose and requires different approaches to modeling data for modeling.Emphasis is placed on methods and platforms from JMP along with these approaches, such as queries, recoding, standardization, transformations, attribution, text mining, etc., which can be tracked to prepare big data at the modeling stage To develop a reproducible methodology.Demonstration using JMP13.
Shaping up Big Data? A data workout with JMP
Michele Boulanger, Professor, International Business Department, Rollins College, Orlando
Mia Stephens, JMP Academic Ambassador, SAS (Co-Author)
Our ability to capture transactional data in most fields has led us to the era of “big data”. What is big data? Does “big” necessarily mean dirty, messy, inconsistent, or unwieldy? How much toning and conditioning do we need to do in the big data world? What differentiates preparation in the big data world from the traditional data cleaning phase In this talk we discuss the different challenges encountered in potentially the most time-consuming phase of big data analytics: data preparation. We present two case studies with very different goals, requiring different approaches to shaping up the data for modeling. Along with these approaches, we also highlight techniques and platforms from JMP such as query, recode, standardization, transformation, imputation, text mining, and others to develop a traceable and reproducible methodology to prepare big data for the modeling phase. All demonstrations will be done live with JMP Pro 13.
