The data exploring helps to understand the complexity of data and Data Processing and transformation make the data ready for analysis.
You can easily identify unusual problems and complex data issues with real-world data in data processing.
The wrong or messy data needs to pre-process. which you mostly collect directly from different sources.
you can find the very important data processing steps below in these articles which are helping to evaluate the type and quality of data.
Main Goal of Data Processing
Mainly two types of goals in the data processing step which is crucial to make data in the right form.
- Address data quality issues using data cleaning involves removing unnecessary and wrong data to make it in the right format.
- Sort of data transformation like arranging the rows and columns for the easy structured form for the right analysis.
A very important part of data preparation is to address quality issues in data using data cleaning and data transformation.
Real-world Data is Untidy.
Customers with two different addresses recorded with different sales locations data can be the quality issues with inconsistent data.
Few Quality issues like six-digit zip code, outliers like a sensor malfunction such type of wrong data affect the analysis.
We have the data that we get, and we must address quality issues by identifying and correcting them.
The complex and unorganized data is hard to process or you can face a lot of issues at the time of the data analysis process.
Address Data Quality Issues.
There are some approaches we can take to address these data quality issues.
We can remove the data records with missing values, we can merge duplicate records.
A missing value of employee age filled based on a reasonable estimate on employment.
Outliers effect on analysis, it can be removed if they are not important for analysis.
The domain knowledge is essential to making informed decisions on how to handle incomplete or incorrect data.
You need to be careful about the changes and keeping records of the changes is important that avoid incorrect conclusions.
Getting data in format
The second part of preparing data is to manipulate the clean data into a needed format for analysis or you can use data processing software as well.
To format data certain data processing examples or data processing tools can use like data manipulation, data pre-processing, data wrangling, data Munging, etc.
Some operations for data formating like data mining, pre-processing include scaling, data manipulation, transformation, feature selection, dimensionality reduction, etc.
Following are the certain types of data processing techniques which are generally used in data science or analytics application.
Scaling of data includes substituting the series of values in a specified range between zero to one for tunning the data.
In data processing technique the data scaling performs the crucial role that benefits to avoid certain features with large values from dominating the results.
It is significant to review the measures of variables between height and weight variables for correct data scaling.
Scaling all values between zero and one needs to be equal contributions for height and weight features.
Transformation of data
In data preprocessing you need to reduce noise and variability from data using transformation or multiple transformations.
Aggregation of data can be the best transformation process to generate the results of data with less variation.
The might be false changes in daily sales figures, aggregating values to weekly or monthly sales figures generate better results.
The data processing requires a data transformation in most cases to remove variability from the data using several filtering techniques.
Feature selection for data
Feature selection is the process of removing redundant or irrelevant features, combining features, and creating new features for analysis.
It is the part of data preprocessing that necessary to identify a correlation among two features for the correct feature selection process.
The example of this process like the purchase price of a product and the number of sales with paid taxes these features should be correlated if not you can remove them.
Eliminating the redundant and irrelevant features from data can make the consequent analysis easier.
Another way you can do for the feature selection process that can consolidate two different features to create new ones.
Another example would be Adding an applicant’s education level as a feature to a loan approval application that can make the right sense.
The internet is the rich source of unstructured and big volume data where it mostly you can get in huge dimensions with less variability those type of data need to be processed.
The big amount of data carry a huge amount of dimensionality and that needs to reduce for right and fast analysis that process called Dimensionality reduction.
The process of dimensionality reduction involves finding a smaller subset of dimensions that captures maximum variance in the data.
This process decreases the dimensions of the data which firing the pointless features and makes the analysis easy and accurate.
The techniques generally utilized for dimensionality reduction and exploratory data analysis are the principal component analysis and factor analysis.
Data manipulation usually does on raw or messy data which can be manipulated and prepare data for correct analysis.
The share market data like Stock prices of the daily movement of a specific stock or different market segments require data manipulation for correct analysis.
Selecting the stock grouping them together and computing the mean, range, and standard deviation for data manipulation.
Similarly, the data in various sectors need the data manipulation process to get the expected trends to link seasonal views, sentimental views, etc.
Data preparation is a significant part of the data science process and data analytics as compared to other processes that you deal with while working on data.
In this process, you probably require to spend more time and effort to create the right form of data for getting expected results from the analysis.
However, If you avoid spending more time on the data preparation process then you can not get the expected output from data.
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