Data wrangling helps businesses gain a competitive edge over other companies. In this blog, we will explore more about data wrangling, the different tools and techniques used in the data wrangling processes, use cases, and so on.
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What is Data Wrangling?
Data wrangling, also known as data munging, is the process of cleaning, transforming, and organizing raw data into a format that is easier to analyze and use for decision-making.
To understand data wrangling in data science, think of it like a chef preparing a meal in a restaurant kitchen. As a chef gathers and prepares ingredients for a dish, data scientists collect and optimize data for analysis. It is crucial to ensure that the data is correct, consistent, and well-structured to make it suitable for analysis. Avoiding the data wrangling process can be problematic. It may lead to inaccurate model building, which may harm the credibility of the analysis.
Importance of Data Wrangling in Data Science
Data wrangling is significant due to the fact that real-world data is frequently disorganized, insufficient, and inconsistent. In order to solve these challenges and prepare data for insightful analysis, data wrangling is essential. The below-mentioned points highlight the importance of data wrangling:
- Data Quality Improvement: Raw data from different sources may contain multiple inconsistencies, errors, missing numbers, and outliers. Data quality is improved by data wrangling procedures, including data cleaning and validation, which makes the data more dependable for analysis.
- Data Integration: The raw data is often extracted from multiple sources, and to have a complete and accurate dataset, the data must be gathered in some single central spot. Data wrangling enables you to do so and makes the data organized.
- Reproducibility: Reproducibility is possible through well-described data wrangling procedures. To achieve consistent results, which are crucial for research and decision-making, others can take the same actions.
- Time and Resource Efficiency: By addressing data quality issues early in the process, data wrangling can save significant time and resources down the line. It reduces the likelihood of errors during analysis, which can be time-consuming to correct.
- Data Communication: Cleaned and well-organized data makes it easier to communicate with stakeholders and non-technical audiences. Effective data visualization and reporting rely on well-wrangled data.
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Working of Data Wrangling in Data Science
Data wrangling involves six steps in general: discovery, organization, cleaning, enrichment, validation, and publishing. Let us discuss this in detail.
- Data Discovery: Firstly, it is crucial to consider what might be underlying your data before beginning the wrangling process. You can collect your data once you’ve decided on your goals. For instance, you might group customer data by geography, discount codes, and transactions if your business wants to understand customer behavior.
- Data Organization: The second step entails categorizing related data into groups, arranging it logically, and assigning labels. Additionally, hierarchies are created to indicate links between data components. Data organization makes data more manageable, accessible, and understandable.
- Data Cleaning: This step focuses on finding and fixing inconsistencies in the data, such as missing values, duplicate records, and errors. This includes a number of careful procedures, such as replacing missing data, removing duplicates, and correcting mistakes.
- Data Enrichment: This step is to make sure that the data obtained after cleaning is sufficient for further processes. The business can add insights into client preferences to the data by examining online behavior and previous purchases. For instance, they can recognize clients who have previously purchased expensive goods or who have expressed interest in watch-related information.
- Data Validation: The process of ensuring that the data being dealt with is accurate and adheres to anticipated rules or limitations is known as data validation in data wrangling. This step ensures the quality of the data. The logic used in data validation is similar to data normalization, a standardization process involving validation rules.
- Publishing: It is the final step of the data wrangling process. The process of making cleaned, sorted, and structured datasets accessible to a larger audience is known as data publishing in data wrangling. This published data can be used for research, reporting, or additional analysis.
Data wrangling can be performed by using numerous tools. The choice of the tool or skill depends on the complexity of the data or the users’ personal preferences. Some of the data wrangling tools are Python, R, Excel, MySQL, Apache Spark, Tableau, etc.
- Python: It uses data manipulation libraries such as Pandas and NumPy for data wrangling.
- R: This creates an environment for statistical computing and visualization while data wrangling.
- Excel: It aids in data cleaning and analysis tasks during data wrangling.
- MySQL: A relational database management system that is used to store and query structured data
- Apache Spark: A distributed data processing framework with capabilities for handling and analyzing massive amounts of data
- Tableau: A platform for data visualization that also offers fundamental capabilities for data manipulation, enabling users to prepare data for visualization
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Use Cases of Data Wrangling
Data wrangling has a wide range of use cases. A few of them are listed below:
In the context of research and academia, data wrangling plays a crucial role in the data analysis pipeline, serving as the initial and fundamental step in the process of preparing raw data for scientific investigations and academic studies.
Data wrangling is used for preparing data for reporting and dashboarding in business intelligence (BI). For business intelligence, data wrangling processes, such as collection, cleaning, and data transformation, are used.
Data Governance and Compliance
By correctly identifying sensitive or confidential information, data wrangling can assist organizations in ensuring compliance with data governance rules and data security standards.
In the future, data wrangling will play a vital role in the advancement of automation and making data preprocessing more efficient. With the growing focus on privacy, the way data is collected and manipulated is also changing. In order to ethically make valuable insights and business decisions, data wrangling is going to be a pivotal and crucial step.
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