Data Science

 Data science is a field study related to data, to bring out meaningful business insights for effective decision making.

Stages of Analytics:

1. Descriptive analytics.
2. Diagnostic analytics.
3. Predictive analytics.
4. Prescriptive analytics.
5. Highest level of analytics.

Descriptive analytics:

        

                Answers questions on what happened in the past and present.

                Example: Number  of Covid-19 cases to date across various countries.

Diagnostic Analytics:

                Answers questions on why something happened.

                Example:Answers questions on why Covid -19 cases are increasing?

Predictive Analytics:

                Answers questions on what might happens in the future.

                Example: what will be Number of covid 19 cases in the next month.

Prescriptive Analytics:

                Provides Remedies and solutions for what might happen in the future.

                Example: what should be done to avoid the spread of covid 19 cases. which might                                             increase in the next one month

                

CRISP -DM: Cross Industry Standard Process for Data Mining:

Business Understanding ---> Data Collection ---> Data Cleansing and exploratory data analysis--->

Data Mining and Model Development --> Model evaluation ---> Deployment.

CRISP - DM for Business understanding:

Articulate the business problem by understanding the client/customer requirement.

Formulate Business Objective       Formulate Business constraint

Few Example of Business objective and Business Constraint:

Business problem: Significant proportion of customers who taken loan are unable to pay.

Business objective: Minimize loan defaulters.

Business Constraint: Maximize profits.

Key points to remember:

Ensure that objective and constraints are SMART.

specific measurable achievable relevant time bound