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What strategies can you use to ensure data sets are valid?

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Data sets are collections of information that can be used for analysis, visualization, or decision making. However, not all data sets are reliable, accurate, or relevant for your purposes. To ensure that you use data sets that are valid, you need to apply some critical thinking strategies before and after you obtain them. Here are some tips to help you evaluate and verify data sets.

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Yasir Bakare

Driving Positive Change in Health & Beyond | Strategy Development | Communications Professional| McKinsey FP Alumni
Ricardo Chang

| Strategic Business Manager | BDM | Sales & Commercial | B2B & B2C | Disruptive Innovator | Mentor
Austin Danner

| Top 1% SMM Voice | The Olivia Pope of Social Strategy |

1 Identify your needs

Before you look for data sets, you need to have a clear idea of what you want to achieve with them. What is your research question or problem? What kind of data do you need to answer it? What are the criteria for selecting and filtering data? How will you measure and compare data quality? By defining your needs, you can narrow down your search and avoid wasting time and resources on irrelevant or unreliable data sets.

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Divya S.

GGI Impact YLP Scholar||Digital Transformation||AI Enthusiast||Data Analyst||Product Mgmt||Author
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Data do talk & this requires assurance that our data set is valid. A Strategic Approach toward Dataset can ensure its validity: 1. Clear understanding of the problem statement & follow a narrow-down approach. 2. Having research questions around the problem ready. 3. Check on the data source- authority & Integrity 4. Check the data quality- if it has any duplicate/null values, any data type concern, or any outlier existing. 5. Review the metadata- size, format, references, location 6. Compare the dataset- to similar historical data and try extracting patterns from it. By comparing datasets, one can gain more perspective on the data.

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Ghazi Khan

AI/ML & Open Source
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Before diving into data sets, nail down your goals. What's the burning question? What data do you need to douse the flames? Set criteria for selection and quality measurement. It's like shopping with a list – stick to it and avoid impulse buys! ??????

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2 Check the source

Once you find a potential data set, you need to check its source and origin. Who created, collected, or published the data? What is their reputation, expertise, or authority? How did they obtain, process, or analyze the data? What is the purpose, scope, or context of the data? How recent, updated, or current is the data? By checking the source, you can assess the credibility, validity, and reliability of the data set.

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Yasir Bakare

Driving Positive Change in Health & Beyond | Strategy Development | Communications Professional| McKinsey FP Alumni
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Data validation is your safety net! Here are a few tips; Scrutinize your data sources: Are they credible and unbiased? Check for inconsistencies and errors: Look for outliers, missing values, or patterns that seem too good to be true. Cleanse your data: Correct errors and address missing values using appropriate techniques. Compare your data to external sources: See if it aligns with established benchmarks or industry trends. By being meticulous about data validation, you can ensure your analysis is based on a solid foundation and leads to trustworthy conclusions.

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3 Review the metadata

Metadata is the information that describes the data set, such as its title, description, format, size, date, location, variables, methods, or references. Metadata can help you understand the content, structure, and quality of the data set. It can also help you identify any errors, gaps, or limitations in the data. By reviewing the metadata, you can determine if the data set meets your needs and expectations.

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Ghazi Khan

AI/ML & Open Source
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Get dataset's description, format, variables. It's like reading the label before buying – you want to know what you're getting into! Look for red flags like errors or missing info. Metadata holds the keys to data kingdom; wield it wisely! ??????

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4 Examine the data

After you download or access the data set, you need to examine it closely and critically. You can use various tools or techniques to explore, visualize, or summarize the data. You can also perform some basic checks or tests to verify the accuracy, consistency, or completeness of the data. For example, you can look for outliers, duplicates, missing values, or anomalies in the data. By examining the data, you can detect and correct any problems or issues in the data set.

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Ricardo Chang

| Strategic Business Manager | BDM | Sales & Commercial | B2B & B2C | Disruptive Innovator | Mentor
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La limpieza de datos es esencial para garantizar que sean precisos y relevantes. Eliminar errores, duplicados y normalizar los datos mejora su calidad y reduce la probabilidad de errores en la toma de decisiones. La estandarización de los datos es clave para asegurar su coherencia y uniformidad. Seguir una convención de nomenclatura y utilizar el mismo formato en todos los datos contribuye a su validez.

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5 Compare the data

Another way to ensure that the data set is valid is to compare it with other data sets that are relevant or related to your topic or question. You can look for similarities, differences, or correlations between the data sets. You can also check if the data sets support, contradict, or complement each other. By comparing the data sets, you can evaluate and validate the data set from different perspectives and sources.

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Ghazi Khan

AI/ML & Open Source
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Compare your data set with its buddies in the pool. Look for similarities, differences, and juicy correlations. It's like hosting a data dinner party – the more, the merrier! Check if they're singing the same tune or dancing to a different beat. Multiple perspectives = better validation. Let the data love connections begin! ??

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6 Document the data

Finally, you need to document the data set and your process of using it. You need to record where, when, how, and why you obtained, used, or modified the data set. You also need to cite the data set and its source properly and acknowledge any limitations or assumptions in the data. By documenting the data set, you can demonstrate your transparency, accountability, and integrity in using data.

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Austin Danner

| Top 1% SMM Voice | The Olivia Pope of Social Strategy |
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This is arguably the most crucial part. If you don't document how you used the data, it becomes words or numbers, which isn't very useful. It's important to note down where, when, how, and why the data was obtained, used, or modified. acknowledge any limitations or assumptions within the data. This will be of immense help when looking back

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7 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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What strategies can you use to ensure data sets are valid?

1

2

3

4

5

6

7

1 Identify your needs

2 Check the source

3 Review the metadata

4 Examine the data

5 Compare the data

6 Document the data

7 Here’s what else to consider

Critical Thinking

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What strategies can you use to ensure data sets are valid?

1

2

3

4

5

6

7

1 Identify your needs

2 Check the source

3 Review the metadata

4 Examine the data

5 Compare the data

6 Document the data

7 Here’s what else to consider

Critical Thinking

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