Types of Data Science Models

Type of Data Science” can be interpreted in several ways. I’ll break it down into the three most common interpretations:

By Role or Specialization (The most common meaning)
By Data Type
By Methodology or Approach

Table of Contents

1. By Role or Specialization (The “Who does what?”)

This is the most practical way to look at it, especially for someone considering a career in data science. The field has evolved into several distinct, though often overlapping, specializations.

Specialization	Primary Focus	Key Skills	Common Job Titles
Data Analyst	Finding insights from past data to answer business questions.	SQL, Excel, BI Tools (Tableau, Power BI), Statistics, Visualization.	Business Analyst, Reporting Analyst, BI Analyst
Data Scientist	Building predictive models and machine learning systems to solve complex problems.	Python/R, SQL, Machine Learning, Statistics, Deep Learning, Big Data Tools.	Machine Learning Scientist, Data Scientist
Data Engineer	Building and maintaining the data infrastructure (pipelines, warehouses) that enables analysis.	SQL, Python, Java/Scala, Big Data Tech (Spark, Hadoop, Kafka), Cloud Platforms (AWS, GCP, Azure), ETL.	Data Architect, Big Data Engineer
Machine Learning Engineer	Taking models from prototype to production; focuses on scalability, efficiency, and deployment.	Python, Software Engineering, ML Frameworks (TensorFlow, PyTorch), MLOps, Cloud, Docker/Kubernetes.	ML Ops Engineer, AI Engineer
Statistician	Designing experiments and using rigorous statistical methods for inference and understanding causality.	R/Python, Experimental Design, Statistical Theory, Hypothesis Testing.	Research Scientist, Biostatistician
Business Intelligence (BI) Developer	Creating easy-to-use dashboards and reports for business users to monitor KPIs.	SQL, BI Tools (Tableau, Power BI, Looker), Data Warehousing.	BI Analyst, Reporting Specialist

Analogy: If data science were a restaurant:

Data Engineer = The kitchen manager and supply chain. They ensure ingredients (data) are available, fresh, and stored correctly.
Data Scientist = The chef. They create new recipes (models) using the ingredients.
ML Engineer = The kitchen tech who scales the chef’s recipe for a chain of restaurants (deployment).
Data Analyst = The food critic and menu planner. They analyze which dishes are popular and suggest what to serve.
BI Developer = The menu designer. They create the clear, beautiful menu (dashboards) customers use to order.

2. By Data Type (The “What data is being used?”)

The nature of the data itself often defines the type of d a ta science being performed.

Structured Data: Working with tabular data found in databases (e.g., SQL tables, Excel spreadsheets). This is the most common starting point.
Unstructured Data: Working with data that has no pre-defined model. This is more complex and requires specialized techniques.
- Natural Language Processing (NLP): Text data (reviews, documents, social media).
- Computer Vision: Image and video data.
- Audio Processing: Speech and sound data.
Geospatial Data: Data with a geographic component (maps, GPS coordinates). Used in logistics, real estate, and environmental science.
Graph Data: Data about relationships and networks (social networks, recommendation systems, fraud detection).
Time Series Data: Data points indexed in time order (stock prices, sensor readings, weather data).

3. By Methodology or Approach (The “How is it done?”)

This refers to the primary goal of the analysis or modeling.

Descriptive Analytics: “What happened?” The most basic type, using historical data to identify trends and patterns. (e.g., “Sales in Q3 were down 10%.”)
Diagnostic Analytics: “Why did it happen?” Goes deeper to find the root cause of a phenomenon. (e.g., “The sales drop was due to a competitor’s new product launch.”)
Predictive Analytics: “What is likely to happen?” Uses statistical models and machine learning to forecast future outcomes. (e.g., “Based on historical data, we predict customer churn will increase by 15% next quarter.”)
Prescriptive Analytics: “What should we do?” The most advanced type, which recommends actions you can take to affect desired outcomes. It often involves optimization and simulation. (e.g., “To reduce churn, we should offer a 20% discount to these 500 high-risk customers.”)
Causal Inference: “Did X cause Y?” Goes beyond correlation to determine cause-and-effect relationships, often using controlled experiments (A/B testing). (e.g., “Did the new website design cause the increase in sign-ups?”)

Summary

When someone asks about the “type of data science,” they are most likely referring to the specializations and roles. However, a real-world data science project will often combine elements from all three categories.

For example, a Data Scientist (Role) might build a Computer Vision model (Data Type) to Predict (Methodology) product defects on an assembly line.