Difference between Data Cleaning and Data Processing Last Updated : 07 May, 2023 Comments Improve Suggest changes Like Article Like Report Data Processing: It is defined as Collection, manipulation, and processing of collected data for the required use. It is a task of converting data from a given form to a much more usable and desired form i.e. making it more meaningful and informative. Using Machine Learning algorithms, mathematical modelling and statistical knowledge, this entire process can be automated. This might seem to be simple but when it comes to really big organizations like Twitter, Facebook, Administrative bodies like Parliament, UNESCO and health sector organisations, this entire process needs to be performed in a very structured manner. So, the steps to perform are as follows: Data Cleaning: Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. It is one of the important parts of machine learning. It plays a significant part in building a model. Data Cleaning is one of those things that everyone does but no one really talks about. It surely isn’t the fanciest part of machine learning and at the same time, there aren’t any hidden tricks or secrets to uncover. However, proper data cleaning can make or break your project. Steps involved in Data Cleaning - Data Processing Vs Data CleaningSr. no. Data Processing Data Cleaning 1Data Processing is done after data cleaningData Cleaning is done before data Processing 2Data Processing requires necessary storage hardware like Ram, Graphical Processing units etc for processing the dataData Cleaning doesn't require hardware tools. 3Data Processing Frameworks like Hadoop, Pig Frameworks etcData Cleaning involves Removing Noisy data etc. No special Frameworks are used.4Data Processing is difficult when compared to data cleaning.Data Cleaning is easier than data Processing.5 Examples: Loading Student data in Hadoop Cluster(data storage) and retrieving (processing)the marks less than 60 percent.Percentage calculation. Examples: Finding the fraud data like age of the student is greater than the range and Percentage is not more than 100.Check whether the marks is not inserted or not. If not, we can verify and place the correct data in place of missed data.6Transforming and manipulating the data to extract insights and build models.Identifying and correcting errors, inconsistencies, and inaccuracies in the data to improve its quality and usability.7Second step, performed after data cleaning.First step, performed before data processing.8Statistical analysis, machine learning algorithms, visualizationHandling missing data, handling outliers, data transformation, data integration, data validation and verification, data formatting. Comment More infoAdvertise with us Next Article Difference between Data Cleaning and Data Processing sravankumar_171fa07058 Follow Improve Article Tags : Difference Between Machine Learning AI-ML-DS Practice Tags : Machine Learning Similar Reads Difference Between Traditional Data and Big Data Data is information that helps businesses and organizations make decisions. Based on volume, variety, velocity, and mode of handling data, traditional data, and big data. It is quite helpful for organizations to understand these key dissimilarities to enable them to select the right approach in data 8 min read Difference between Data Warehousing and Data Mining A Data Warehouse is built to support management functions whereas data mining is used to extract useful information and patterns from data. Data warehousing is the process of compiling information into a data warehouse. The main purpose of data warehousing is to consolidate and store large datasets 5 min read Difference between Program and Data 1. Program : Program, as name suggest, are simply set of instructions developed by programmers in programming languages that automate, collect, manage, calculate, analyze processing of data and information accurately. 2. Data : Data, as name suggests, are information processed and stored in files or 2 min read Difference between Data Management and Data Governance Data Management and Data Governance are both critical aspects of handling data within organizations, but they focus on different facets of the data lifecycle and have distinct roles. Data Management involves the comprehensive administration of data throughout its lifecycle, encompassing acquisition, 5 min read Difference Between Data Mining and Data Analysis 1. Data Analysis : Data Analysis involves extraction, cleaning, transformation, modeling and visualization of data with an objective to extract important and helpful information which can be additional helpful in deriving conclusions and make choices. The main purpose of data analysis is to search o 2 min read Like