What is data?
In computing, data (information) is translated into a form for efficient processing. Relative to today’s computers and transmission media, data that is information convert into binary digital form. It is acceptable for data to use as a plural subject or a singular subject. Raw data is a term to describe data in its most basic digital format in the computer. Later in this content we will be dealing with what is data wrangling, how to achieve it and many more.
The concept of data in the background of computing has its roots in the great works of Claude Shannon, an American mathematician known as the father of Information Theory. He works in binary digital concepts based on applying two-value Boolean logic to electronic circuits. Binary digit formats underlie the CPUs, semiconductor memories and disk drives, pen drives as well as many of the modern devices common in computing today. Early computer input for (a card works according to code) data took the form of punch cards, followed by magnetic tape and the most popular hard disk.
DATA WRANGLING
Wrangling is also known as munging of data is the process of remapping and transforming data from one form into another form for the purpose of making it more valuable and appropriate for a variety of downstream purposes such as analytics. A data wrangler is a person who performs these kinds of transformation operations. This includes further data visualization, munging, training a statistical model, data aggregation, as well as many other important uses. Data munging is a process typically follows a set of general steps and rules which begin with extracting the data in a raw form from the data source, “munging” the raw data using algorithms. Such as parsing or sorting the data into predefined data structures, and finally depositing the resulting content into a data sink. This is for storage purposes and future use.
Data wrangling is also a process of cleaning and the complex data sets are combined for easy analysis and access.
- With the amount of data and data sources quickly expanding and growing. It is getting more and more essential for the large amounts of available data to well organize for clear study (analysis) and future operations.
- This process includes manually converting and mapping data from one raw form into another format for more convenience and easy consumption and organization of the data.
The goals of data wrangling:
- Reveal a “deeper intelligence” within given data, by gathering data from multiple sources
- Provide actionable, accurate data in the hands of business analysts in a timely matter
- Reduce the time spent in organizing and collecting unruly data before it can be utilized
- Enable analysts and data scientists to focus on the analysis of data, rather than the wrangling
The key steps to data wrangling:
Data Cleansing
Accordingly, redesigning the data into a usable and functional format. You have to remove/correct any bad data
Data Acquisition
Getting and establishing access to the given data
Joining Data
Combine the edited data for further usage and analysis purpose
Data Wrangling in Practice: What to expect
There are six iterative steps that make up the data wrangling process.
Discovering
Before you dive deeply into data wrangling. You must better understand what is in your data, which will inform and help, how you want to analyze.
Structuring
This means organizing the data, which is necessary because raw data comes in many different forms, shapes, and sizes. A single column can turn into several rows for convenience. One column may become two or more. Movement of data for easier computation works and analysis.
Cleaning
What happens when outliers and errors skew your data? You clean the data. Null values can change and standard formatting can implement in the data, ultimately increases data quality.
Enriching
Here, you take stock in your data and strategize about how other additional data might increase it. Questions asked during this data wrangling step might be: what new types of data can I get from existing data or what other information would better inform and take my decision making, about this current data?
Validating
Checking rules are repetitive programming sequences that verify data consistency, security, and quality.
Publishing
Analyst experts prepare the wrangle data for downstream – whether for a particular software or user – and document any particular steps taken or logic used to wrangled data. Hence, Data wrangling experts understand that implementation of insights depends upon the ease with which it can be utilized and accessed by others.
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