What is Data Science?

Data science is the practice of turning raw, complex data into meaningful insights using statistics, programming, machine learning, and analytical reasoning. By uncovering patterns and building predictive models, data science helps organizations make smarter decisions, optimize operations, and anticipate what will happen next.

In a world where every business generates massive amounts of data, data science provides the tools to transform that information into real competitive advantage. It powers everything from personalized recommendations and fraud detection to forecasting, automation, and strategic planning.

What Is the data science process?

The data science process is a structured, iterative workflow used to turn raw data into meaningful insights. While every organization adapts it slightly, most data science teams follow six core stages:

1. Business understanding

‍Define the problem you are trying to solve and the outcome you want to achieve. Clear business objectives guide the rest of the workflow.

2. Data understanding and preparation

Collect raw data from all relevant sources and prepare it for analysis:

• Gather structured and unstructured data
• Clean and correct errors
• Handle missing values
• Standardize formats
• Combine datasets for a unified view

This step ensures the data is accurate and usable.

3. Exploratory data analysis (EDA)

Explore the data to uncover trends, correlations, distributions, and anomalies. EDA helps validate assumptions, highlight biases, and determine which features matter most.

4. Modeling

Choose the right statistical or machine learning technique and train the model using historical data. Common approaches include:

• Regression
• Classification
• Clustering
• Forecasting

Models are refined until they meet performance expectations.

5. Model evaluation

Test how well the model performs using accuracy, precision, recall, or other metrics. Confirm that it meets the project’s goals and works on new, unseen data.

6. Deployment and communication

Deploy the model so business users can take action—or present insights through dashboards and visualizations. Data scientists monitor performance over time and retrain models as new data arrives.

This lifecycle ensures data science work is measurable, repeatable, and aligned to meaningful business outcomes.

Data scientist vs. data analyst vs. data engineer

Data science involves several roles working together to turn raw data into insights.

Data scientist

• Defines problems and experimental approaches
• Prepares unstructured and structured data
• Builds and evaluates machine learning models
• Communicates insights and recommends actions

They focus heavily on modeling, predictive analytics, and experimentation.

Data analyst

• Interprets data to answer defined business questions
• Uses reporting, visualization, and basic statistical techniques
• Performs less coding and predictive modeling than data scientists

Their work is essential for understanding “what happened” and “why.”

Data engineer

• Designs and maintains data pipelines and architecture
• Manages data lakes, warehouses, and ETL processes
• Ensures data is clean, reliable, and accessible

Engineers make the data usable so analysts and scientists can work efficiently.

Together, these roles ensure data flows smoothly, is well-modeled, and supports decision-making across the organization.

Data science and business intelligence

Business intelligence (BI) and data science both support data-driven decisions, but they focus on different questions.

Business Intelligence (BI):

Analyzes historical and current data to understand what happened and why.

Data Science:

Uses modeling, statistics, and machine learning to predict what will happen next.

In short:

• BI = descriptive insights (past + present)
• Data science = predictive insights (present + future)

Most organizations need both to fully understand and act on their data.

Why data science is important?

Data science matters because it helps organizations:

• Make better decisions by modeling root causes and likely outcomes
• Predict trends and risks before they escalate
• Find hidden patterns such as anomalies or fraud
• Improve efficiency through automation and optimization
• Enhance customer experiences via personalization and recommendations

What you can do with data science?

Data science applications are wide-ranging. Common use cases include:

• Detecting fraud or anomalies
• Classifying data (e.g., emails, inventory, documents)
• Delivering personalized recommendations
• Automating routine processes
• Forecasting trends and outcomes
• Segmenting customers or products
• Enabling recognition of faces, text, images, or audio
• Optimizing processes to reduce risk and maximize reward

How data science works?

Because data science is such a large field that deals with a variety of tasks, it can be difficult to narrow down exactly how each question is answered. Generally, the data science process, also known as the data science lifecycle, involves these steps:

1. Capture

‍Data scientists gather raw structured and unstructured data using many different methods from all the relevant sources available. Tasks include: 

• Data acquisition
• Data entry
• Signal reception
• Data extraction

2. Maintain

‍Data scientists put raw data into a standardized format so that it can be used for analytics, machine learning, and other forms of modeling. Tasks include:

• Data cleansing
• Data processing
• Data staging
• Data warehousing
• Data architecture

3. Process

‍Data scientists examine the data to find patterns, ranges, and distributions of values and to check for biases. All of this information informs whether or not the data is suitable for predictive analytics, machine learning, and other analytical methodologies. Tasks include:

• Data mining
• Clustering and classification
• Data modeling 
• Data summarization

4. Analyze

‍Data scientists perform functions to extract insights from the data. Tasks include:

• Predictive analysis
• Regression
• Text mining
• Qualitative analysis

5. Communicate

‍Data scientists present their findings in data visualizations like reports and charts that make insights easy to understand. They help decision makers understand how findings will impact their business. Tasks include:

• Data reporting
• Data visualization
• Business intelligence
• Decision making

What to I look for in a data science tool?

The best data science platforms support both technical users and business users. Key capabilities include:

• Connecting to cloud, on-premises, and proprietary data sources
• Preparing and cleansing datasets
• Offering multiple model options
• Integrating models into data pipelines
• Writing predictive results back to source systems
• Visualizing insights across the organization
• Enforcing strong data governance and permissions

Modern platforms also support citizen data scientists through no-code/low-code tools.

Domo’s data science capabilities combine automated machine learning, drag-and-drop transformations, and embedded Python/R for unlimited flexibility.

How do different industries use data science?

Every organization across industries can benefit from the insights and opportunities that data science brings. Data science helps make processes more efficient and helps improve the customer experience. Here are a few examples: 

• Airlines – Predicting flight delays and optimizing scheduling.
• Public Safety – Using statistical analysis to allocate resources effectively.
• Healthcare – Detecting diseases and improving treatment tools.
• Financial Services – Identifying fraud and managing risk.
• Streaming Platforms – Delivering personalized content recommendations.
• Logistics – Optimizing routes and improving delivery efficiency.
• Automotive – Powering real-time object detection in autonomous vehicles.

How will data science evolve in the future?

In the future, automated machine learning will be utilized more broadly to help enterprises achieve outcomes and understand the variants that drove impact. Data integration combined with domain knowledge tools will create even more opportunities to automate business processes. 

Additionally, productionizing data science will become easier for business users and analysts, requiring less core computer science, advanced statistics, and linear algebra skills. Tools for data scientists will expand, but more solutions for citizen data scientists will encompass end-to-end workflows to accelerate the data life cycle.