large datasets

USAGE SUMMARY

The phrase "large datasets" is correct and usable in written English.
You can use it when referring to collections of data that are significantly large in size, often used in contexts like data analysis, machine learning, or research. Example: "The study required the analysis of large datasets to draw meaningful conclusions about the trends."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Large datasets allow for analytic flexibility, and it is all too tempting to trawl a dataset for "significant" associations.

The Guardian

Collectively, large datasets, such as those of Twitter's 218 million users, can be analysed to identify connections between people, locations and interests.

The Guardian - Tech

Large datasets can be examined.

Chemistry Central Journal

Cloudera uses Hadoop to analyze and synthesize large datasets.

TechCrunch

True, most deep-learning algorithms need large datasets.

TechCrunch

This is what's used to train large datasets.

TechCrunch

Polypharmacology profiling requires carefully collated, large datasets.

Journal of Cheminformatics

Figure 30 Execution times for large datasets.

Journal of the Brazilian Computer Society

Figure 29 Results for large datasets.

Journal of the Brazilian Computer Society

For large datasets, our CUDA solution is superior.

Computers & Graphics

If most large datasets are useless, why talk about them at all?

TechCrunch

Expert writing Tips

Best practice

When discussing the analysis of "large datasets", specify the tools or techniques used to handle the data's volume and complexity, such as parallel processing, cloud computing, or specialized algorithms.

Common error

Avoid making broad generalizations about the insights derived from "large datasets" without providing specific examples or statistical validation. Ensure conclusions are supported by rigorous analysis and are not merely speculative.

Linguistic Context

The phrase "large datasets" primarily functions as a noun phrase acting as the object of a verb or the subject of a clause. It refers to collections of data that are of considerable size and complexity. Ludwig AI confirms its correct usage.

Expression frequency: Very common

Frequent in

Science

64%

News & Media

30%

Formal & Business

6%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

In summary, the phrase "large datasets" is a grammatically sound and frequently used term to describe substantial collections of data. Ludwig AI confirms its accuracy and appropriateness. Predominantly found in scientific and news contexts, it highlights the scale and complexity of the data being discussed. When using this phrase, ensure you provide context regarding the tools and techniques employed for analysis, while avoiding overgeneralizations without supporting evidence. Consider alternative phrases such as "extensive datasets", "vast datasets", or "massive datasets" to add variety to your writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Extensive datasets

Replaces "large" with "extensive", emphasizing the comprehensiveness of the datasets.

Vast datasets

Uses "vast" instead of "large", highlighting the immense scale of the datasets.

Massive datasets

Employs "massive" to underscore the substantial size of the datasets.

Sizable datasets

Substitutes "large" with "sizable", indicating a considerable quantity of data.

Voluminous datasets

Replaces "large" with "voluminous", emphasizing the sheer volume of data.

Substantial datasets

Uses "substantial" instead of "large", suggesting a significant amount of data.

Big data

A common synonym emphasizing the datasets are so large that they require specialized tools.

Extensive data collections

Rephrases to focus on data "collections" and uses "extensive" to describe them.

Comprehensive data sets

Replaces "datasets" with "data sets" and uses "comprehensive" to indicate thoroughness and scale.

Enormous datasets

Employs "enormous" to emphasize the exceptionally large scale of the datasets.

FAQs

How can I effectively analyze "large datasets"?

Effective analysis of "large datasets" often requires specialized tools and techniques, such as distributed computing frameworks like Hadoop or Spark, and statistical methods designed to handle high-dimensional data. Consider using programming languages like Python or R with relevant libraries for data manipulation and analysis.

What are common challenges when working with "large datasets"?

Common challenges include data storage limitations, computational complexity, and the need for efficient algorithms. You might need scalable infrastructure, optimized code, and strategies for data reduction or feature selection.

What can I say instead of "large datasets"?

You can use alternatives like "extensive datasets", "vast datasets", or "massive datasets" depending on the specific context and the aspect of size you want to emphasize.

Which tools are best suited for managing and processing "large datasets"?

Tools like Apache Hadoop, Apache Spark, and cloud-based solutions such as Amazon Web Services (AWS) and Google Cloud Platform (GCP) are well-suited for managing and processing "large datasets". These platforms provide scalable storage and computing resources that can handle the volume and complexity of big data.