Here is a simple definition of data science:
Data science combines multiple fields including statistics, scientific methods, and data analysis to extract value from data.
Those who practice data science are called data scientists, and they combine a range of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources.
Data: An untapped resource for machine learning
Data science is one of the most exciting fields out there today. But why is it so important?
Because companies are sitting on a treasure trove of data. As modern technology has enabled the creation and storage of increasing amounts of information, data volumes have exploded. It’s estimated that 90 percent of the data in the world was created in the last two years. For example, Facebook users upload 10 million photos every hour.
But this data is often still just sitting in databases and data lakes, mostly untouched and as a Data Scientist you need to be on top of it.
The wealth of data being collected and stored by these technologies can bring transformative benefits to organizations and societies around the world—but only if we can interpret it. That’s where data science comes in.
Data science reveals trends and produces insights that businesses can use to make better decisions and create more innovative products and services. Perhaps most importantly, it enables machine learning (ML) models to learn from the vast amounts of data being fed to them rather than mainly relying upon business analysts to see what they can discover from the data.
Data is the bedrock of innovation, but its value comes from the information data scientists can glean from it, and then act upon.
How data science is transforming business
Organizations are using data science to turn data into a competitive advantage by refining products and services. Data science and machine learning use cases include:
- Determine customer churn by analyzing data collected from call centers, so marketing can take action to retain them
- Improve efficiency by analyzing traffic patterns, weather conditions, and other factors so logistics companies can improve delivery speeds and reduce costs
- Improve patient diagnoses by analyzing medical test data and reported symptoms so doctors can diagnose diseases earlier and treat them more effectively
- Optimize the supply chain by predicting when equipment will break down
- Detect fraud in financial services by recognizing suspicious behaviors and anomalous actions
- Improve sales by creating recommendations for customers based upon previous purchases
Many companies have made data science a priority and are investing in it heavily. In Gartner’s recent survey of more than 3,000 CIOs, respondents ranked analytics and business intelligence as the top differentiating technology for their organizations. The CIOs surveyed see these technologies as the most strategic for their companies, and are investing accordingly.