WHAT IS DATA WRANGLING?
Suppose you have identified a business use case that requires you to create a machine learning model that will predict asset failure probability in the coming one month period. You have access to the operation dataset which records all the historical maintenance and failures of the asset you are investigating. Besides the maintenance data, you are considering using historical vibration data of the engine to understand the signals prior to the failure events.
You start your journey by performing an exploratory data analysis, and creating data processing steps that will cleanse, combine, and transform your raw data into consumable format required by the machine learning algorithms. These data steps are known as data wrangling processes.
Data wrangling is data steps we perform to improve the quality and value of our data by performing data selection, cleansing, aggregation, merge, and augmentation. The other goal is to re-shape our data as input for advanced analytics tasks such as business intelligence and machine learning.
HOW CAN ZX ANALYTIX HELP?
According to O’Reilly’s 2016 Data Science Salary Survey, 69% of data scientists will spend a significant amount of time in their day-to-day dealing with basic exploratory data analysis, while 53% spend time cleaning their data. These laborious tasks usually require data scientists to write a large and complex code in order to perform these data wrangling processes.
ZX AnalytiX data wrangling service has two main features that can help data scientists in building their data workload, i.e., automated exploratory data analysis (EDA), and data pipeline design canvas. Some of the key benefits of using these two features are:
DATA WRANGLING CAPABILITIES IN ZX ANALYTIX
The detail capabilities of our data wrangling service are as follows:
Automated Exploratory Data Analytics
The main purpose of this feature is to help data scientists quickly understand the data they are currently working on by providing pre-calculated information:
Physical data profile which includes:
Association between numerical columns using Pearson correlation metric
Data Pipeline Design Canvas
Fig 1. Design Canvas of ZX AnalytiX Data Wrangling
The design canvas provides a drag and drop functionalities to create data pipelines with predefined processors:
Other functionalities includes:
Fig 2. Pipeline List of ZX AnalytiX Data Wrangling