Business Analytics

Introduction :
Any business thrives on business strategies. Ever wondered how business strategies are being made? It’s definitely not made out of the blues and surely not according to the gut feeling. It’s the historical data, previous performances, skills and technologies applied in the past and deriving conclusion out of the analysis of these data.

Business analytics work in the same way; it takes previous data into consideration to analyze, investigate and explore new insights and thus help in better planning.

In this session, you would be introduced to the subject Business Analytics which in simple terms can be defined as the discovery and communication of meaningful patterns in data using tabulation and visualization techniques to communicate insights.
Business Analytics:
• It focuses on developing new insights and better understanding of business performance based on extensive dataand various statistical methods.
• It uses data, statistical and quantitative analysis, explanatory and predictive modeling and fact-based management for improved decision making and enriched data representation.
Types of Analytics would include:

Descriptive Analytics

Predictive analytics

Prescriptive analytics

Along with reporting scorecards,
clustering and past data,
it gains insight from these.
Using statistical and machine learning techniques,
Predictive modeling is being made.
Using optimization, simulation etc,
recommend decisions are provided.

The real challenge is data volume and size, computational power and skilled professionals

Types of Data Variables :

Numerical variables

Categorical variables

Arises from counting
Can take only a set of particular values including negative and fractional values
Example: Credit score, number of credit cards owned by a person, number of states in a country, charge on electron etc.
• Binary (or Dichotomous) Has only two categories Example: yes/no, male/female, pass/fail etc.
Arises from measuring
Within a specified range, It can take any value
Example: Height, Amount of money, Age etc.
Has several unordered category
Examples: Type of bank account
Has several ordered category
Examples: questionnaire responses such as "strongly Like / … / strongly dislike".

Data being the foundation for all analysis, so it forms a major and vital step in data analysis. It is basically an elaborate process and has a lot of detailing and precision to it. EduPristine has a vast coverage of these topics, to learn more about it, write an email to us at

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