Data science is the field that deals with extracting insights and knowledge from complex data sets to
provide actionable solutions for businesses and organizations. It is the intersection of computer science,
mathematics, and business expertise, and requires collaboration across these three disciplines. There
are different types of data science methods used for answering various questions of varying complexity
and value. The data science life cycle includes business understanding, data mining, data cleaning,
exploration, advanced analytics, and visualization.
Types of Data Science Methods
Descriptive Analytics: It involves answering the question of what is happening in the business by having
accurate data collection.For example, whether sales went up or down.
Diagnostic Analytics: It involves drilling down to the root cause of a problem. For example, why did sales
go up or down?
Predictive Analytics: It involves using historical patterns in the data to predict outcomes in the future.
For example, what will the sales performance be next quarter?
Prescriptive Analytics: It involves recommending the best action for a particular outcome. For example,
what do I need to do to improve sales
Science Life Cycle
The data science life cycle includes the following stages:
Business Understanding: Defining the right question to ask before starting a data science initiative,
which requires domain and business expertise.
Data Mining: The process of procuring data for analysis.
Data Cleaning: Preparing and cleaning the data before it is analyzed.
Exploration: Using analytical tools to answer questions and gain insights.
Advanced Analytics: Using machine learning tools to perform predictive and prescriptive analytics.
Visualization: Visualizing insights and outcomes of the analysis.
Roles in Data Science
There are different roles involved in the data science life cycle, such as business analysts, data engineers,
and data scientists. Business analysts formulate the questions, data engineers find and clean the data,
while data scientists perform exploration and advanced analytics. There is overlap between these roles,
and collaboration across them is critical for successful data science initiatives
provide actionable solutions for businesses and organizations. It is the intersection of computer science,
mathematics, and business expertise, and requires collaboration across these three disciplines. There
are different types of data science methods used for answering various questions of varying complexity
and value. The data science life cycle includes business understanding, data mining, data cleaning,
exploration, advanced analytics, and visualization.
Types of Data Science Methods
Descriptive Analytics: It involves answering the question of what is happening in the business by having
accurate data collection.For example, whether sales went up or down.
Diagnostic Analytics: It involves drilling down to the root cause of a problem. For example, why did sales
go up or down?
Predictive Analytics: It involves using historical patterns in the data to predict outcomes in the future.
For example, what will the sales performance be next quarter?
Prescriptive Analytics: It involves recommending the best action for a particular outcome. For example,
what do I need to do to improve sales
Science Life Cycle
The data science life cycle includes the following stages:
Business Understanding: Defining the right question to ask before starting a data science initiative,
which requires domain and business expertise.
Data Mining: The process of procuring data for analysis.
Data Cleaning: Preparing and cleaning the data before it is analyzed.
Exploration: Using analytical tools to answer questions and gain insights.
Advanced Analytics: Using machine learning tools to perform predictive and prescriptive analytics.
Visualization: Visualizing insights and outcomes of the analysis.
Roles in Data Science
There are different roles involved in the data science life cycle, such as business analysts, data engineers,
and data scientists. Business analysts formulate the questions, data engineers find and clean the data,
while data scientists perform exploration and advanced analytics. There is overlap between these roles,
and collaboration across them is critical for successful data science initiatives
