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Big Data Analysis with Python

You're reading from   Big Data Analysis with Python Combine Spark and Python to unlock the powers of parallel computing and machine learning

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Length 276 pages
Edition 1st Edition
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Authors (3):
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Ivan Marin Ivan Marin
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Ivan Marin
Sarang VK Sarang VK
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Sarang VK
Ankit Shukla Ankit Shukla
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Ankit Shukla
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Table of Contents (11) Chapters Close

Big Data Analysis with Python
Preface
1. The Python Data Science Stack FREE CHAPTER 2. Statistical Visualizations 3. Working with Big Data Frameworks 4. Diving Deeper with Spark 5. Handling Missing Values and Correlation Analysis 6. Exploratory Data Analysis 7. Reproducibility in Big Data Analysis 8. Creating a Full Analysis Report Appendix

Translating a Business Problem into Measurable Metrics and Exploratory Data Analysis (EDA)


If a specific business problem comes to us, we need to identify the KPIs that define that business problem and study the data related to it. Beyond generating KPIs related to the problem, looking into the trends and quantifying the problem through Exploratory Data Analysis (EDA) methods will be the next step.

The approach to explore KPIs is as follows:

  • Data gathering

  • Analysis of data generation

  • KPI visualization

  • Feature importance

Data Gathering

The data that is required for analyzing the problem is part of defining the business problem. However, the selection of attributes from the data will change according to the business problem. Consider the following examples:

  • If it is a recommendation engine or churn analysis of customers, we need to look into historical purchases and Know Your Customer (KYC) data, among other data.

  • If it is related to forecasting demand, we need to look into daily sales data.

It needs...

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