Organizations today are grappling with how to make sense of an inordinate amount of disparate data.

The ability to transform a sea of data into actionable insights can have a profound impact—from predicting the best new diabetes treatment to identifying and thwarting national security threats. That’s why businesses and government agencies are rushing to hire data science professionals who can help do just that.

By extrapolating and sharing these insights, data scientists help organizations to solve vexing problems. Combining computer science, modeling, statistics, analytics, and math skills—along with sound business sense-data scientists uncover the answers to major questions that help organizations make objective decisions.

Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. The design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers. While each project is different, the process for gathering and analyzing data generally follows the below path:

1. Ask the right questions to begin the discovery process

2. Acquire data

3. Process and clean the data

4. Integrate and store data

5. Initial data investigation and exploratory data analysis

6. Choose one or more potential models and algorithms

7. Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence

8. Measure and improve results

9. Present final result to stakeholders

10. Make adjustments based on feedback

11. Repeat the process to solve a new problem

Common Data Scientist Job Titles

The most common careers in data science include the following roles.

• Data scientists: Design data modeling processes to create algorithms and predictive models and perform custom analysis
• Data analysts: Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions
• Data engineers: Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses.
• Business intelligence specialists: Identify trends in data sets
• Data architects: Design, create and manage an organization’s data architecture

Although the roles of data scientists and data analysts are often conflated, their responsibilities are actually quite different. Put simply, data scientists develop processes for modeling data while data analysts examine data sets to identify trends and draw conclusions. Because of this distinction and the more technical nature of data science, the role of a data scientist is often considered to be more senior than that of a data analyst; however, both positions may be attainable with similar educational backgrounds.