Statistics has proven to be the biggest game changer in the context of a business in the 21st century, leading to the boom of the new oil, that is “Data”.

Through this blog, we aim to provide a definitive understanding to the reader on how the process of Statistical Analysis for Data Science be done on an actual business use case.

Let’s get started :

Data can be analysed to get valuable insights, but when analysis isn’t done, data is just a bunch of numbers that wouldn’t make any sense.

According to Croxton and Cowden,

Statistics is a Science of Collection, Presentation, Analysis and Interpretation of any numerical data.

A few examples include:

  1. Route Optimisation in Airlines Industry
  2. ROI Prediction of a company
  3. Stock Market Share Price Prediction
  4. Predictive Maintenance in Manufacturing

For any data set, statistical analysis for Data Science can be done according to the six points as shown below. They form the skeleton of statistical analysis.

The steps are as follows :

  1. Defining business objective of analysis
  2. Collection of Data
  3. Data Visualization
  4. Data Pre-Processing
  5. Data Modelling
  6. Interpretation of Data

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