Data Analyst Q&A 11. What Are The Best Practices For Data Cleaning? ITECH podcast

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टेक Sominath Avhad

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Data analyst Q&A 11. What are the best practices for data cleaning?

4 yıl önce 10:43

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M4A•Bölüm sayfası

İçerik Sominath Avhad tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Sominath Avhad veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

11. What are the best practices for data cleaning? If you are sitting for a data analyst job, this is one of the most frequently asked data analyst interview questions. Data cleansing primarily refers to the process of detecting and removing errors and inconsistencies from the data to improve data quality. The sample answer is… 1. Make a data cleaning plan by understanding where the common error take place and keep communication open 2. Identity and remove duplicates values before working with the data. This will lead to an effective data analysis process 3. Focus on the accuracy of the data. Maintain the value types of data, provide a mandatory constraints and set cross-field validation. 4. Standardize the data at the point of entry so that it is less chaotic and you will be able to ensure that all information is standardized, leading to fewer errors on entry.

… continue reading

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#टेक #Sominath Avhad

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Data analyst Q&A 11. What are the best practices for data cleaning?

published 4 yıl önce

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M4A•Bölüm sayfası

İçerik Sominath Avhad tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Sominath Avhad veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

11. What are the best practices for data cleaning? If you are sitting for a data analyst job, this is one of the most frequently asked data analyst interview questions. Data cleansing primarily refers to the process of detecting and removing errors and inconsistencies from the data to improve data quality. The sample answer is… 1. Make a data cleaning plan by understanding where the common error take place and keep communication open 2. Identity and remove duplicates values before working with the data. This will lead to an effective data analysis process 3. Focus on the accuracy of the data. Maintain the value types of data, provide a mandatory constraints and set cross-field validation. 4. Standardize the data at the point of entry so that it is less chaotic and you will be able to ensure that all information is standardized, leading to fewer errors on entry.

… continue reading

90 bölüm

#टेक #Sominath Avhad

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28. Mention the steps of a Data Analysis project. We discuss this question in question number 9. What are the various steps involved in any data analytics projects…today we will discuss more details. The core steps of a Data Analysis project include: · The foremost requirement of a Data Analysis project is an in-depth understanding of the business requirements. · The second step is to identify the most relevant data sources that best fit the business requirements and obtain the data from reliable and verified sources. · The third step involves exploring the datasets, cleaning the data, and organizing the same to gain a better understanding of the data at hand. · In the fourth step, Data Analysts must validate the data. · The fifth step involves implementing and tracking the datasets. · The final step is to create a list of the most probable outcomes and iterate until the desired results are accomplished. https://open.spotify.com/show/7nQzL21xSX2Qcjup1FbiYH https://open.spotify.com/show/7nQzL21xSX2Qcjup1FbiYH…

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27. How should you tackle multi-source problems? To tackle multi-source problems, you need to: · Identify similar data records and combine them into one record that will contain all the useful attributes, minus the redundancy. · Facilitate schema integration through schema restructuring.

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