data exceptions

USAGE SUMMARY

The phrase "data exceptions" is correct and usable in written English.
It can be used in contexts related to programming, data analysis, or data management, where it refers to errors or anomalies in data. Example: "The system encountered several data exceptions that need to be addressed before proceeding with the analysis."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

1 human-written examples

Only very few regional administrations benefit from their own statistical offices that produce highly detailed data; exceptions are economically advanced regions like Baden-Württemberg and the Basque Country.

Nature

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

Assuming that many data errors, such as jitter, data loss, and data exception, occur in the collected dataset D. The existence of these errors leads to lower metrics for these data indicators, i.e., q c, q t, and q a.

EURASIP Journal on Wireless Communications and Networking

For instance, when an attempt is made to divide Null by zero, platforms may return Null instead of throwing an expected "data exception - division by zero".

Wikipedia

We excluded field reports, case series, case reports, studies without any control group, abstracts which proceeded a full length publication, translations of already published manuscripts, double publication of similar data (exception is the presentation of further data), internal reports and unpublished manuscripts.

BMC Cancer

Unfortunately for Apple, matching is time-consuming to analyze data, identify exceptions and improve the system.

TechCrunch

"It treats every human face it encounters as data, without exceptions, neglecting religious or scale principles".

Vice

We compared our method with these alternatives using the same sets of data, the exception being mixture of experts where only EXP and clinical data were used.

Bioinformatics

The mean expression of six of the seven genes predicted to be represented by adjacent tags agreed with the DGE data, the exception being TNNT3.

BMC Genomics

(14) Research organisations and cultural heritage institutions, including the persons attached thereto, should be covered by the text and data mining exception with regard to content to which they have lawful access.

European Parliament

So if we introduce a text and data-mining exception only for certain organizations and startups are not included in that, then we're basically saying that any kind of startup that if you're using copyrighted content for training their AI would be performing a copyright infringement.

TechCrunch

With a few exceptions, data has no natural "look," no natural "visualization," and choices have to be made about how it should be displayed.

Columbia University

Expert writing Tips

Best practice

When discussing "data exceptions", be specific about the type of exception and its potential impact on analysis or decision-making. Clearly define what constitutes an exception in your context.

Common error

Avoid simply stating that there are "data exceptions" without providing context or details. Always specify the nature of the exceptions and their potential significance. Otherwise, the statement provides little value.

Linguistic Context

The phrase "data exceptions" functions as a noun phrase, typically used as a subject or object in a sentence. According to Ludwig AI, it refers to errors or anomalies within a dataset that require specific handling or attention. The examples show its application across diverse areas.

Expression frequency: Rare

Frequent in

Science

56%

News & Media

19%

Formal & Business

9%

Less common in

Encyclopedias

3%

Wiki

3%

Academia

7%

Ludwig's WRAP-UP

In summary, "data exceptions" is a noun phrase denoting irregularities or errors in data, used primarily in technical contexts such as science, news, and business. Ludwig AI confirms that the phrase is correct and usable in English. While not an extremely common phrase, it's vital for identifying and addressing issues that could impact data analysis and decision-making. To enhance clarity, consider using more specific terms like "data anomalies" or "data errors", depending on the specific context.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

data anomalies

Replaces "exceptions" with "anomalies", emphasizing the unusual or irregular nature of the data.

data errors

Substitutes "exceptions" with "errors", highlighting the incorrect or flawed aspect of the data.

data irregularities

Uses "irregularities" instead of "exceptions", focusing on the non-standard or deviating characteristics of the data.

data outliers

Replaces "exceptions" with "outliers", specifically referring to data points that significantly differ from the rest.

data discrepancies

Uses "discrepancies" to indicate inconsistencies or disagreements within the data.

data abnormalities

Replaces "exceptions" with "abnormalities", highlighting deviations from the norm.

data defects

Substitutes "exceptions" with "defects", emphasizing flaws or imperfections in the data.

data inconsistencies

Uses "inconsistencies" instead of "exceptions", focusing on the lack of uniformity or agreement in the data.

data faults

Replaces "exceptions" with "faults", highlighting errors or failures in the data.

data corruptions

Uses "corruptions" to indicate damaged or altered data, leading to exceptions.

FAQs

How can I identify "data exceptions" in a dataset?

Identifying "data exceptions" typically involves statistical analysis, data visualization, and domain expertise. Techniques such as outlier detection, range checks, and consistency checks can help pinpoint unusual or erroneous data points.

What is the difference between "data errors" and "data exceptions"?

"Data errors" generally refer to mistakes or inaccuracies in the data, while "data exceptions" can encompass a broader range of issues, including outliers, anomalies, and unexpected values that may or may not be errors. You can also consider "data anomalies" as an alternative.

What are some common causes of "data exceptions"?

Common causes of "data exceptions" include data entry errors, system glitches, integration issues, and unexpected events that deviate from normal patterns. In some cases, what appears to be an exception may simply be a rare but valid data point.

How should I handle "data exceptions" in my analysis?

Handling "data exceptions" depends on their nature and impact. Options include correcting errors, removing outliers, imputing missing values, or adjusting the analysis to account for the exceptions. It's important to document all decisions and justify them based on domain knowledge and statistical considerations. You might also consider addressing "data irregularities".