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How do you guide data cleaning with exploratory data analysis?

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Data cleaning is the process of identifying and correcting errors, inconsistencies, and outliers in a data set. It is a crucial step before performing any data analysis, as dirty data can lead to misleading results and inaccurate conclusions. But how do you know what to clean and how to clean it? Exploratory data analysis (EDA) is a technique that can help you guide your data cleaning process by revealing the characteristics, structure, and patterns of your data. In this article, you will learn how to use EDA to identify and address common data quality issues, such as missing values, duplicates, outliers, and incorrect formats.

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Kajo Rudziński

Twórca KajoData ( kajodata.com ) ?? Za?o?yciel KajoDataSpace ?? Analytical Data Architect w Zooplus ??
Souheir Mehanna

Data Scientist | Ph.D in Data Science | Python | SQL | Data Quality
Mahmoud Tamer, MBA

Senior Data Analyst

1 What is EDA?

EDA is a process of exploring your data using descriptive statistics and visualizations. It helps you understand the distribution, shape, and relationships of your variables, as well as identify any potential problems or anomalies in your data. EDA is not a rigid or formal method, but rather a flexible and creative approach that allows you to ask questions, make hypotheses, and discover insights from your data. EDA can be done using various tools and languages, such as Excel, Python, R, or SQL.

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Kajo Rudziński

Twórca KajoData ( kajodata.com ) ?? Za?o?yciel KajoDataSpace ?? Analytical Data Architect w Zooplus ??
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Think of EDA as the detective work of data analysis. Just as Sherlock Holmes scrutinizes the smallest details at a crime scene, discovering hidden clues and making astute deductions, EDA delves into the heart of your data. It uncovers its patterns, whispers of anomalies, and tells tales of relationships. And just as Holmes doesn’t restrict himself to one magnifying glass or method, EDA employs a diverse toolkit, from the simplicity of Excel to the sophistication of Python or R. It's less about following a strict protocol and more about letting your data lead the dance, revealing its secrets one twirl at a time.

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Mahmoud Tamer, MBA

Senior Data Analyst
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Exploratory Data Analysis is like going on a first date with your data ;) . You're trying to get to know it better, finding out its quirks and preferences. You start by asking questions like, "Hey data, what's your distribution like?" and "Tell me about your relationship with outliers." Just like on a date, you might uncover surprises – data behaving unexpectedly, or maybe even hiding something from you! But the more you chat, the more you understand, and by the end of it, you'll know if you've found a data match made in heaven or if it's time to swipe left.

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2 Why use EDA for data cleaning?

Data cleaning can be a tedious and time-consuming task, especially with large or complex data sets. Utilizing EDA can simplify and streamline the process by providing a comprehensive overview of your data quality and structure. Through EDA, you can detect and remove errors, inconsistencies, and outliers that could affect your analysis and results. Additionally, EDA can identify and fill in missing values that may reduce your sample size and introduce bias. It can also check and correct the data types, formats, and scales of your variables to ensure accuracy when calculating or visualizing. Furthermore, it can find and eliminate duplicates in your data to avoid inflating counts or skewing statistics. Finally, EDA can explore and transform variables to create new features or improve their suitability for analysis.

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Souheir Mehanna

Data Scientist | Ph.D in Data Science | Python | SQL | Data Quality
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The quality of the data is one of other important tasks during EDA because with poor data quality, poor analytics are concluded. Accuracy is an essential data quality dimension, however, it is one among others. Missing values for example affect the completeness of the data, which is another crucial data quality dimension. During EDA, the quality of the data is studied in order to later on, in other stages of the process, clean the data and transform it.

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Mahmoud Tamer, MBA

Senior Data Analyst
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EDA involves crafting a comprehensive snapshot of more than just data patterns and outliers.it unveils the essence of the business itself. As you embark on your data exploration journey you're not just diving into data – you're unraveling the intricate threads that form the business fabric. This process is akin to giving your data a personality, it's almost like having a conversation with the business itself. Curiosities are sparked, questions emerge and suddenly you're not just analyzing data but you're inquiring about the heartbeat of the business. This unique dance between data and business insights elevates EDA from a routine task to a vibrant exploration that can spark innovative strategies and directions for the entire enterprise.

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3 How to perform EDA for data cleaning?

When performing EDA for data cleaning, there is no universal approach that works for every data set. However, a general workflow can consist of the following steps: importing and inspecting your data to check for any errors; summarizing your data with descriptive statistics to identify outliers; visualizing your data with graphs and charts to spot any trends or anomalies; cleaning your data with methods such as imputation or standardization; and repeating and refining the process until you are satisfied with your data. Documenting each step will help ensure accuracy and reproducibility.

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Mahmoud Tamer, MBA

Senior Data Analyst
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Invest significant time in your Exploratory Data Analysis (EDA). It's not merely a time-consuming task; instead, it acts as the initial spark that ignites critical thinking and guides the path toward accurate data cleaning. When commencing EDA, I rely on Python and SQL/SQLite as my primary tools. I initiate the exploration process with visualizations, particularly utilizing box plots to identify outliers. Following this, I delve into outlier investigation, comprehend data correlations, and delve into summary statistics. Additionally, I find writing SQL scripts to be one of my preferred EDA techniques. While the tools employed may vary, the ultimate objective remains consistent: to construct a comprehensive data representation.

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4 What are some best practices for EDA for data cleaning?

EDA for data cleaning is an iterative and exploratory process that requires some trial and error and experimentation. However, there are some best practices that can help you perform EDA for data cleaning more effectively and efficiently. Before you start exploring your data, have a clear idea of what you want to achieve and what questions you want to answer with your data. Additionally, it's important to use a combination of tools and methods, such as Excel, Python, R, or SQL, and descriptive statistics, visualizations, or machine learning. Furthermore, make a copy of your original data and store it in a safe place before cleaning your data. Lastly, test and validate your data to ensure that it meets your expectations and requirements. Use various methods and techniques such as sampling, cross-validation, or hypothesis testing to check the accuracy, completeness, consistency, and reliability of your data. If any errors or discrepancies are found, go back to your cleaning steps and fix them.

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5 What are some challenges and limitations of EDA for data cleaning?

EDA for data cleaning is a powerful and useful technique that can help you improve your data quality and structure. Nonetheless, it has some challenges and limitations, such as introducing subjectivity and bias into the data cleaning process. Additionally, complexity and scalability issues can arise when dealing with large or complex data sets. Finally, there may be trade-offs and limitations to consider when making decisions about your data, such as accuracy, completeness, consistency, and reliability. To overcome these issues, it is important to use objective criteria and methods to clean your data, as well as appropriate tools and techniques that can handle large or complex data sets. Furthermore, you should weigh the pros and cons of your options and decisions carefully to ensure the best results.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you guide data cleaning with exploratory data analysis?

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5

6

1 What is EDA?

2 Why use EDA for data cleaning?

3 How to perform EDA for data cleaning?

4 What are some best practices for EDA for data cleaning?

5 What are some challenges and limitations of EDA for data cleaning?

6 Here’s what else to consider

Data Analysis

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How do you guide data cleaning with exploratory data analysis?

1

2

3

4

5

6

1 What is EDA?

2 Why use EDA for data cleaning?

3 How to perform EDA for data cleaning?

4 What are some best practices for EDA for data cleaning?

5 What are some challenges and limitations of EDA for data cleaning?

6 Here’s what else to consider

Data Analysis

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