Data science professionals use computing systems to follow the data science process.
The top techniques used by data scientists are:
Classification
Classification is the sorting of data into specific groups or categories. Computers are trained to identify and sort data. Known data sets are used to build decision algorithms in a computer that quickly processes and categorizes the data.
For example -
Sort products as popular or not popular·
Sort insurance applications as high risk or low risk·
Sort social media comments into positive, negative, or neutral.
Data science professionals use computing systems to follow the data science process.
Regression
Regression is the method of finding a relationship between two seemingly unrelated data points. The connection is usually modeled around a mathematical formula and represented as a graph or curves. When the value of one data point is known, regression is used to predict the other data point.
For example -
The rate of spread of air-borne diseases.·
The relationship between customer satisfaction and the number of employees.·
The relationship between the number of fire stations and the number of injuries due to fire in a particular location.
Clustering
Clustering is the method of grouping closely related data together to look for patterns and anomalies. Clustering is different from sorting because the data cannot be accurately classified into fixed categories. Hence the data is grouped into most likely relationships. New patterns and relationships can be discovered with clustering. For example: ·
Group customers with similar purchase behavior for improved customer service.·
Group network traffic to identify daily usage patterns and identify a network attack faster.
Cluster articles into multiple different news categories and use this information to find fake news content.
The basic principle behind data science techniques
While the details vary, the underlying principles behind these techniques are:
Teach a machine how to sort data based on a known data set. For example, sample keywords are given to the computer with their sort value. “Happy” is positive, while “Hate” is negative.
Give unknown data to the machine and allow the device to sort the dataset independently.
Allow for result inaccuracies and handle the probability factor of the result.