Say it like this
- We're working with a clean dataset for the experiment.
- This dataset contains user behavior from the past quarter.
- Let's split the dataset into training and testing portions.
Use it at work
Data work
- Can you share the dataset you used for this chart?
- The dataset is missing last month's data.
ML
- We need a bigger training dataset.
- Let's split the dataset into train and test.
Being careful
- This dataset includes personal data, so keep it private.
- How was this dataset collected?
Don't say this
Datasets are imported or stored within databases; they are not stored as databases themselves.
Practice
Try it
4 quick exercises
1. You need to prepare data for a machine learning project. What do you say?
2. The machine learning model performs better when we use a _______ dataset with clear labels.
3. Which sentence uses dataset incorrectly?
4. Rewrite in natural tech English: The information we are using for our machine learning project is very large and comes from customer interactions.
Say it out loud
Phrase 1 of 3
"We're working with a clean dataset for the experiment."
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More about dataset
Real examples(3)
- in a standup
"We received the customer dataset yesterday, so we can start building the analytics pipeline today."
- in a code review
"Make sure to validate the dataset before running it through the preprocessing function."
- in a team meeting
"The machine learning team needs a labeled dataset with at least 10,000 samples to improve model accuracy."
More mistakes(2)
Handle with care. Do not confuse dataset with database. A dataset is a collection of data points, while a database is a system for storing and managing data.
- ✕The dataset system crashed.✓The database system crashed.
Dataset refers to data itself, not a system. Use database when referring to the storage system.
- ✕Can you send me a dataset?✓Can you send me the dataset?
In tech contexts, use the definite article when referring to a specific, known dataset.
Other forms(3)
- dataset noun
"The dataset contains 50,000 product reviews."
- datasets noun
"We are combining three datasets to improve our analysis."
- dataset-driven adjective
"We use a dataset-driven approach to decision making."
Often used with(5)
- clean dataset · A dataset that has been processed and validated, free from errors and inconsistencies.
- labeled dataset · A dataset where data points have been tagged with correct answers, used for supervised machine learning.
- training dataset · The portion of data used to train a machine learning model.
- large dataset · A dataset containing a significant volume of records, often millions or billions of data points.
- split the dataset · To divide data into separate portions, typically for training, validation, and testing.
Similar words(2)
- data collection · More general term; dataset implies organized, structured data.
- corpus · Used specifically in linguistics and NLP; refers to a large collection of text or language data.
Questions(5)
- What does dataset mean in software engineering?
- A dataset is a structured collection of data points organized for a specific purpose. It differs from a database, which is a system for storing and managing data. Datasets are used in machine learning, testing, analysis, and research.
- How do you use dataset in a sentence at work?
- You can say: We need to clean the dataset before training the model. Or: The testing dataset revealed three critical bugs. Use dataset when referring to a specific collection of data prepared for a particular task.
- Is dataset formal or informal?
- Dataset is neutral in formality. It is standard technical vocabulary used in meetings, documentation, emails, and conversations in tech companies. It is equally appropriate in formal and casual contexts.
- What is the difference between a dataset and a database?
- A dataset is a collection of data organized for a specific purpose or analysis. A database is a structured system designed to store, manage, and retrieve data efficiently. Datasets are often stored within databases.
- Why do teams talk about cleaning datasets?
- Raw data often contains errors, missing values, duplicates, and inconsistencies. Cleaning a dataset means identifying and fixing these issues so the data is accurate and ready for analysis or model training.
