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CRISP - Business Understanding

  • November 23, 2020
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Descriptive Analysis – What happened in the Past – Taking historical data Diagnostic Analytics – Why something happened

Predictive Analysis – What is likely to happen – What happens in future

Prescriptive Analysis – What needs to be done – giving suggestions to control

According to Standish Group, their chaos report 2019 shows that more than 80% of the IT project partially completed or failed across the globe.

So, to attain accuracy and for the successful completion of the projects there is a methodology called: PROJECT MANAGEMENT METHODOLOGY

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There are 6 steps involved from end to end in a life of Data Science

  • Understand the Business problem & create a project charter
  • Data Collection
  • Data Cleansing / Preparation / EDA / Feature Engineering
  • Data Mining / Machine Learning
  • Model Evaluation
  • Model Deployment

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Survival Analysis mainly deals with the time to an event, including death, the onset of disease, and bankruptcy, etc. Only the occurrence of either time to event or censoring time is observed.

Step 1:  Business Understanding – Once the Business problem is understood, one needs to record 2 things, one is Business Objective (Primary metric) and the other is Business Constraint (secondary metric)

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Data Collection

There are 2 types of data collection i.e. Primary Data Sources and Secondary Data Sources

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Secondary Data Source – Data collected beforehand – Publicly available data- open source as well as syndicate data. Pro: Data is available easily. Cons: Data may or may not have a variable of interest

Primary Data Source – If the data is not available, it will be collected by source by conducting surveys, design of experiment, simulation. Pro: Get data for the exact variable of interest. Con: It is very expensive and time-consuming.

Data Collection Using Survey – Understand Business Reality → Root Cause Analysis → Decision Problem (a problem on which decision to be taken) → Research Objective (should be a one-line statement with an action verb and actionable object) → Construct (Multi-dimensional object) → Aspects(Single-dimensional) → Survey Questionnaire.

Design of Experiment – When is not possible to practically capture the data

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