Data Science is a study to extract meaningful insight for a business. It is a multidisciplinary approach combining the principles and practices in mathematics, statistics, artificial intelligence and computer engineering, which analyze large amounts of data. If you want to know Important Data Science Techniques Used by Data Scientists, Join Data Science Course in Chennai at FITA Academy with course certification and placement training, which will help to enhance your career.
Techniques of Data Science:
Data science professionals use the computing system to follow the data science process. The most important techniques data scientists use are:
Classification:
Classification is generally the sorting of data into specific groups or categories. Computers are trained to analyze, identify and sort data. Available datasets are used to build decision algorithms in a computer which quickly processes and categorizes the data. For example:
- Sort the products which are popular or not popular
- Sort insurance applications which are high-risk or low risk
- Sort the social media comments into positive, negative or neutral.
- Data science professionals generally use computing systems to follow the data science process.
Regression:
Regression is finding a relationship between two unrelated data points. The connection is usually modeled with a mathematical formula and represented as a graph or a curve. When the value of one data point is known, Regression is used to predict another data point easily. Best examples of Regression:
- The rate of spread of airborne disease.
- The relationship which occurs between customer satisfaction and the total number of employees.
- The relationship between the total number of fire stations and several injuries from fire in a particular location.
Enroll Data Science Online Course, where you gain basics to advanced knowledge of Data Science at your flexible time and comfortable environment.
Clustering:
Clustering is a method which groups closely related data together and looks for patterns and anomalies. Clustering differs from sorting because the data cannot be accurately classified into fixed categories. The data is grouped which can be most likely relationships. New patterns and many relationships can be discovered with clustering. Some of the best examples are clustering:
- Group customers with similar purchase behaviour of improved customer service.
- Group network traffic identifies the daily usage patterns, and the network attacks faster.
- Cluster articles with multiple news categories use this information to find fake news content.
Conclusion:
This blog has helped you gather information about Data Science and important techniques Data Scientists use. Join Data Science Courses in Bangalore to learn more about Data Science, which will help you to develop your career by providing certification training with real-time projects.