Data science is a multi-sectoral field focused on extracting knowledge from data sets and applying the knowledge in actionable insights from data to solve problems in a wide range of application domains in any industry.
Data science collects all the available data and extracts valuable data from it, implementing that data to grow business.
Big data is becoming a booming tool for businesses of all sizes. Data scientists (data scientist who creates programming code and combine it with statistical knowledge to develop insights from data.) are responsible for breaking down big data into usable information and creating software that helps organizations and companies determine optimal operations.
It uses techniques and theories drawn from many fields within mathematics, computer science, information science, statistics, and domain knowledge.
Techniques used in data science:
There is various type of technologies used in data science.
- Linear regression
- Logistic regression
- Machine learning
- Naive Bayes classifiers
- Dimensionality reduction
- Cluster analysis
- Support-vector machine
- Decision trees
Data Science Uses
Data science helps us achieve some important goals for businesses and companies.
Examples:
Healthcare:
The healthcare industry used data science for EMRs to clinical databases to personal fitness trackers, practiced preventive medicine, diagnosed diseases faster, and explored new treatment options.
Entertainment:
The entertainment industry is like Netflix, Hotstar, and much more. Use data science for audience engagement.
Finance:
Data science and Machine learning have saved the financial industry unquantifiable amounts of time and millions of dollars.
Cybersecurity:
Data science is used in many industries sectors, but the most critical use of data science is in cybersecurity—data science and machine learning to detect over 360,000 new malware samples daily.
Resource:
https://builtin.com/data-science
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