Data science is the advanced level of learning in programming and computer science. In the modern technologies, everything is relied on the data. Data says everything to study and develop things.

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specialized subject matter expertise to unearth useful insights hidden in an organization's data. These insights can be used to inform decisions and strategic planning.

Data Science is an umbrella term which encompasses multiple domains.

Data Visualization:

The representation of information by means of conventional graphics, such as infographics, charts, and even animations, is known as data visualizations. These informational visual displays make complex data relationships and data-driven insights which are simple to understand.

Data manipulation:

Data manipulation is the process of modifying information to make it more readable and structured. For this, we make use of DML. What does DML mean? Data Manipulation Language is a computer language that can add, remove, and change databases, converting the data into text that can be read. Thanks to DML, we can clean and map the data to make it palatable for interpretation.

Statistical Analysis:

The collection and interpretation of data in order to discover patterns and trends is known as statistical analysis. It is part of data analytics. Statistical analysis can be used for a variety of purposes, including gathering research interpretations, statistical modelling, and designing studies and surveys.

Machine Learning:

The process of collecting and analyzing data to identify patterns and trends is referred to as statistical analysis. It's a component of data analytics. Statistical analysis can be used for a variety of purposes, such as collecting research interpretations, statistical modelling, and designing studies and surveys.

Data Analytics:

Data analytics (DA) is the process of examining data sets to discover trends and draw conclusions about the information contained within them. Computational modeling is rapidly increasing with the assistance of highly specialised systems and software.

1. Descriptive Analytics: It analysis the data that is coming in real time and also checks the cause of success and failure of the history for future references.

2. Diagnostic Analytics: It includes techniques like drill-down, data discovery, data mining and correlations for understanding the root cause of the events

3. Predictive Analytics: It predicts the future outcome using various techniques that involve statistical and machine learning algorithms

4. Prescriptive Analytics: It prescribes what action you have to take, so that there will be no future problems happening again.

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