Data science

It is a branch of study which deals with the large volume of data. Data science is the study of techniques to convert huge data into meaningful forms.
Data science is collecting, analysing, organizing and utilising the data that helps in decision making. Data science is the combination of programming skills, mathematics, artificial intelligence, machine learning algorithms and statistics to extract useful information from the data.

Data scientist
A data scientist is a person who determines the problem by asking the right questions and gaining understanding. The data scientist then determines the correct set of variables and data sets.
The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc. Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis.
Prequisites
There are some technical concepts you should be aware of before learning data science.

• Machine learning
• Modeling
• Statistics
• Programming
• Databases

Data Science Life Cycle

Data science involves certain steps to create a redefined look into raw data.

1 - Capture

This stage is when data scientists gather raw and unstructured data.
The capture stage typically includes data acquisition, data entry, signal reception and data extraction.

2 - Maintain

This stage is when data is put into a form that can be utilized. The maintenance stage includes data warehousing, data cleansing, data staging, data processing and data architecture.

3 - Process

This stage is when data is examined for patterns and biases to see how it will work as a predictive analysis tool. The process stage includes data mining, clustering and classification, data modeling and data summarization.

4 - Communicate

This stage is when data scientists and analysts showcase the data through reports, charts and graphs. The communication stage typically includes exploratory and confirmatory analysis, predictive analysis, regression, text mining and qualitative analysis.

5 - Analyze

This stage is when multiple types of analyses are performed on the data. The analysis stage involves data reporting, data visualization, business intelligence and decision making.

Data Science Tools

There are plenty of tools in data science which helps us in successful making the data science life cycle easier.

• Data Analytics
• Data Warehousing
• Data Visualization
• Machine Learning

Applications

There are numerous applications of data science in vast fields . It's flexibility and characteristics has popularly increased its demand in the software market. Healthcare, Gaming, Internet, Marketing, Detecting fraud, Forecasting, Image and pattern recognition, Forecasting,Regression, Augmented reality, Airline Route Planning, etc...

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