Learn about data science — definition, importance, and more
Data science definition
Data science is a field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
Data science is multifaceted and can be described as a science — as well as a research method, discipline, and profession. It’s intended to unify statistics, data analysis, informatics, and related methods to understand and analyze actual data phenomena.
Importance of data science
Increasingly, modern businesses are realizing the importance of data science, artificial intelligence (AI), and machine learning (ML). No matter the industry or size, companies that want to remain competitive in the age of big data need to efficiently develop and implement data science capabilities.
There are many benefits to using data science, such as:
- Making better business decisions. Collecting and analyzing data can assist company management by providing objective information that informs business decisions.
- Measuring performance. Collecting data and using metrics to evaluate the information in different ways and over periods of time can help businesses assess performance, identify patterns and opportunities, and find evidence-backed solutions.
- Providing information about internal finances. Companies use data science to make predictions, generate reports, and analyze economic trends that paint a more accurate picture of internal finances and guide decisions on budget, expenses, and more.
- Developing better products. Data analysis can provide valuable consumer insights that help a company reach its target audiences, learn what they like and don’t like, and tailor its products accordingly.
- Increasing efficiency. Using data, businesses can test and measure different internal processes and workplace operations, helping to improve efficiency, refine workflows, and fuel productivity.
- Mitigating risk and fraud. Data science allows businesses to increase security, protect sensitive information, detect fraud with ML algorithms, and more to reduce business risks.
- Predicting outcomes and trends. Data scientists and statisticians can develop sophisticated predictions and projections to help executives spot patterns, prepare for future challenges, and optimize outcomes.
- Improving customer experiences. Collecting consented data enables companies to tailor the customer experience to individuals and increase personalization, which develops trust, builds loyalty, and boosts conversions.
What does a data scientist do?
Data scientists examine which questions need answering and explore where to find the data to answer these questions. Many data scientist-practitioners have business and analytical skills. They support the data lifecycle by collecting, transforming, and analyzing information, and then communicate results that guide decision-making.
Data scientists use ML and AI to develop automated systems, which can perform tasks that ordinarily require human intelligence. With the help of this technology, data scientists are able to generate insights that analysts and business users can translate into tangible business value.
Simplify the science behind data
Understanding data science can help businesses to efficiently develop and implement data science capabilities, which are critical to remaining competitive in today’s digital world.
When you’re ready to get started, you can discover meaningful insights with machine learning and artificial intelligence in just one click.
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Watch the Sensei video to learn more.