DATA WRANGLING: PREPARING DATA FOR ANALYSIS
Data wrangling makes sure that the data is accurate, consistent, and ready for analysis. Without proper data wrangling, data analysis can be unreliable and misleading, leading to incorrect conclusions and decisions. In this article, we will look at the most common data handling methods used in various stages of data wrangling.
Data wrangling is an essential step in the data science pipeline. Raw data can be messy, incomplete, or inconsistent, making it difficult to analyze and derive insights from. In addition, data may come from multiple sources, such as different databases or file formats, each with its own structure and syntax. Therefore, cleaning and pre-processing, in other words, data wrangling is a necessary step in preparing the data for analysis.
This includes tasks such as removing duplicates, handling missing data, correcting errors, formatting data and merging data from different sources.
Data wrangling makes sure that the data is accurate, consistent, and ready for analysis. Without proper data wrangling, data analysis can be unreliable and misleading, leading to incorrect conclusions and decisions. In this article, we will look at the most common data handling methods used in various stages of data wrangling.
Stage 1: Data Cleaning
This first step in data wrangling entails locating and addressing issues with the data’s quality, such as outliers, missing values, and inconsistencies. Cleaning data can be accomplished in a number of ways, including:
Removing missing values: Missing values can skew analysis results. To address this problem, missing values are either removed or replaced with a value that reflects the nature of the remainder of the data points.
Handling outliers: Extreme values that are significantly outside of a dataset’s typical range are known as outliers. By skewing the statistical measures used, outliers can affect the analysis results. To deal with outliers, you can either get rid of them or make them less extreme.
Resolving inconsistencies: Typos, different data formats, or errors in data collection can all lead to data inconsistencies. They can be fixed by using data validation rules to find and fix errors and standardizing the format of the data.
Stage 2: Data Transformation
Data transformation entails changing the data’s original format to improve the data analysis. Data transformation can be accomplished in a number of ways, including:
Normalization of data: The process of normalizing data entails scaling the data so that it falls within a predetermined range. Data normalization is used when variables forming the data have different units of measurement.
Aggregation of data: Combining data from multiple sources or summarizing data at a higher level of granularity are examples of data aggregation. As a result of data aggregation, data may become simpler to analyze.
Encoding data: The process of converting categorical data into a numerical format that can be used in the analysis is known as data encoding. This method is frequently used when the data contains non-numeric values like gender or product category.
Stage 3: Data Preparation
Data preparation is the final stage of data wrangling. Preparing the data for analysis entails selecting appropriate variables, inventing new variables, and formatting the data. Data preparation can be done in a number of different ways, including:
Variable selection: Variable selection entails removing irrelevant variables and locating the most important variables for analysis. Variable selection may improve the accuracy of the analysis and simplify the data to create a more parsimonious model.
Engineering features: New variables are created using the dataset’s existing variables in feature engineering. New features may bring out hidden patterns and improve the accuracy of the analysis.
Conclusion
Because it ensures that the data are in a format that is suitable for analysis, data wrangling is an essential step in the data science pipeline. There are a number of methods that can be used at each stage of the process, which include cleaning, transforming, and feature selection. Data wrangling improves the data quality prior to analysis and helps data scientists derive more accurate insights.
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