Datasets

 

A dataset is a collection of data that is organized for a specific purpose—analysis, reporting, training, or experimentation.

It is a structured collection of related data points organized for analysis, modeling , research, or decision-making.

A dataset could be as simple as an Excel sheet or as large as petabytes of distributed files.

 

Examples:

A CSV of customer transactions

A folder of labeled images used for machine learning

A database table containing sensor readings

A JSON dataset of API logs

 

Why Datasets Matter ?

Datasets are indispensable across industries because they enable:

Data-Driven Decision Making: Businesses analyze sales datasets to identify trends and refine strategies.​

Machine Learning: Training, validating, and testing models requires carefully prepared datasets.​

Scientific Discovery: Researchers rely on datasets to identify patterns, test hypotheses, and publish findings.​

Operational Excellence: Healthcare uses patient datasets for diagnosis and treatment planning; finance uses market datasets for risk assessment.​

Without quality datasets, organizations risk making decisions based on incomplete or inaccurate information, leading to significant financial and operational losses

 

Key terms :

Data: Individual facts or values, such as a single temperature reading or a customer’s age.​

Record / row / example: One item in the dataset (one customer, one transaction, one sensor reading).​

Variable / feature / column: One attribute measured for each record (e.g., age, amount, city).​

Schema: The blueprint of the dataset: which variables exist, their names, and their types.​

Metadata: Data about the dataset: where it came from, when it was collected, what it means, and how to use it.​

Realistically, what counts as “one dataset” is fuzzy: some people treat an entire research project’s data as one dataset; others treat each table or file as a separate dataset, and both views can be valid.​

 

Types of datasets :

Different sources classify datasets in slightly different ways, but several recurring dimensions are useful in practice.​

 

By structure :

Structured datasets

Organized into rows and columns with a consistent schema (like a spreadsheet or SQL table).​

Examples:

A customer table: one row per customer, columns for id, name, age, city, lifetime_value .​

A daily sales dataset: one row per store day, columns for store_id , date, total_sales .​

Semi structured datasets

Have some consistent markers (keys, tags) but no fixed table schema, such as JSON, XML, or log lines.​

Examples:

API responses in JSON storing user profiles with optional fields.​

Web server logs where each line is a structured text record.​

Unstructured datasets

No predefined schema; structure must be inferred (text, images, audio, video).​

Examples:

A corpus of customer support chat transcripts.​

A folder of MRI images used in a medical diagnosis study.​

Most real systems mix these: a project might have a structured sales dataset, semi structured clickstream JSON, and unstructured session recordings, each treated as separate datasets with their own life cycle.​

(Want to learn more about Data by Structure? Check out our post on – Data Classification: By Structure – DataGeeksHub  )

 

By statistical/analytic form :

Many analytics textbooks and data science guides categorize datasets by the kind of variables they contain and the relationships among them.​

Numerical datasets – Mainly numeric variables (e.g., temperature readings, share prices).​

Categorical datasets – Values are labels or categories (e.g., product type, country, color ).​

Time series datasets – Observations indexed by time (e.g., daily active users, hourly CPU usage).​

Bivariate / multivariate datasets – Two or many variables measured on each record (e.g., height and weight, or dozens of lab measurements per patient).​

 

In practice, a dataset used in data engineering or BI is often multivariate, mixing numeric, categorical, and temporal features.

 

By purpose in workflows :

From a data science / ML / analytics workflow perspective:​

Raw / landing datasets – Direct copies of source systems, minimally processed, used mainly as an audit trail or for reprocessing.

Curated / analytical datasets – Cleaned, standardized, and modeled (e.g., star schema fact and dimension tables) for BI and analytics.

ML training / validation / test datasets – Carefully prepared splits of labeled data for building and evaluating models.​

Public benchmark datasets – Released for research and comparison (e.g., on Kaggle, UCI, or similar platforms).​

 

A single real world project nearly always works with multiple datasets across these categories.

 

Dataset vs. related concepts :

The term “dataset” overlaps with several adjacent ideas;

Dataset vs. data

Data: individual facts.

Dataset: a structured, intentionally grouped collection of those facts.

Dataset vs. table vs. file

A table is one common implementation of a structured dataset in a database.

A file (CSV, JSON, Parquet) can store a dataset, but a dataset may span many files or live in memory.

In practice, engineers often say “orders dataset” and mean a specific table or file set.

Dataset vs. database

A database is a system that stores and manages many datasets, often with indexes and transaction logic.​

A dataset is one logical collection within or outside that database.

Dataset vs. Spark Dataset API (Scala/Java)

The Spark Dataset[T] type is a specific implementation: a distributed, strongly typed collection in Spark’s engine.​

Conceptually, it still represents a dataset in the broader sense but with tool specific semantics.

 

Key Distinction: A dataset is a snapshot—a collection of data at a specific point in time, often read-only and focused on a particular purpose. A database, by contrast, is a managed system that continuously accepts updates, enforces rules, and handles multiple concurrent users.

Different communities sometimes treat these terms differently; research articles usually define what they mean by “dataset” early in the paper precisely because the boundary is not rigid.

 

Dataset Quality Dimensions :

High-quality datasets share common attributes:

Poor dataset quality leads to failed analytics, wrong decisions, and biased

models.

 

Public Data Repositories

Kaggle: Hosts thousands of datasets for competitions and learning. The famous Titanic survival dataset is used by thousands learning machine learning.

Google Dataset Search: Indexes over 25 million datasets across the web, making data discovery easier.

UCI Machine Learning Repository: Specializes in datasets for academic research and algorithm benchmarking.

Google Cloud Public Datasets: Offers free access to datasets like USA Names (1879-2015) and GitHub repository activity.

 

 

A realistic view is that a “good” dataset is not just a pile of values. It has:

A clear definition and scope (what is in, what is out).

Documented variables and units.

Known provenance (where it came from, how it was processed).

Appropriate metadata and quality checks.

Without that context, the same raw dataset can be misleading or unusable, even if technically available