Single-cell RNA sequencing (scRNA-seq) has transformed the field of transcriptomics by making it possible for researchers to address fundamental questions that could not be tackled by bulk-level ...

When normalizing data structures, attributes congregate around the business keys that identify the grain at which those attributes derive their values. Attributes directly related to a person, ...

Increasingly, senior executives are more heavily scrutinizing the business value of their software deployments by looking for cost savings and operational efficiencies. At the heart of this evaluation ...

There are many types of experimental methods that often use normalization to fix the differences induced by factors other than what is immediately being analyzed. In particular, normalization can be ...

Dr. James McCaffrey of Microsoft Research uses a full code sample and screenshots to show how to programmatically normalize numeric data for use in a machine learning system such as a deep neural ...

When the healthcare industry talks about data, the conversation usually focuses on interoperability and data standards. These are certainly important topics, but they don’t fully address the challenge ...

What is data cleaning in machine learning? Data cleaning in machine learning (ML) is an indispensable process that significantly influences the accuracy and reliability of predictive models. It ...

Data quality assessments help you avoid introducing errors into your database. Learn how they work and why you need them. Image: Sikov/Adobe Stock Data quality assessments have the same goal that data ...

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out. Novice data scientists sometimes have ...