Читать книгу Data Science For Dummies - Lillian Pierson - Страница 32

If science is a systematic method by which people study and explain domain-specific phenomena that occur in the natural world, you can think of data science as the scientific domain that’s dedicated to knowledge discovery via data analysis.

With respect to data science, the term domain-specific refers to the industry sector or subject matter domain that data science methods are being used to explore.

Data scientists use mathematical techniques and algorithmic approaches to derive solutions to complex business and scientific problems. Data science practitioners use its predictive methods to derive insights that are otherwise unattainable. In business and in science, data science methods can provide more robust decision-making capabilities:

 In business, the purpose of data science is to empower businesses and organizations with the data insights they need in order to optimize organizational processes for maximum efficiency and revenue generation.

 In science, data science methods are used to derive results and develop protocols for achieving the specific scientific goal at hand.

Data science is a vast and multidisciplinary field. To call yourself a true data scientist, you need to have expertise in math and statistics, computer programming, and your own domain-specific subject matter.

Using data science skills, you can do cool things like the following:

 Use machine learning to optimize energy usage and lower corporate carbon footprints.

 Optimize tactical strategies to achieve goals in business and science.

 Predict for unknown contaminant levels from sparse environmental datasets.

 Design automated theft- and fraud-prevention systems to detect anomalies and trigger alarms based on algorithmic results.

 Craft site-recommendation engines for use in land acquisitions and real estate development.

 Implement and interpret predictive analytics and forecasting techniques for net increases in business value.

Data scientists must have extensive and diverse quantitative expertise to be able to solve these types of problems.

Machine learning is the practice of applying algorithms to learn from — and make automated predictions from — data.