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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

Introduction


One of the most important stages, and the initial step of a data science project, is understanding and defining a business problem. However, this cannot be a mere reiteration of the existing problem as a statement or a written report. To investigate a business problem in detail and define its purview, we can either use the existing business metrics to explain the patterns related to it or quantify and analyze the historical data and generate new metrics. Such identified metrics are the Key Performance Indicators (KPIs) that measure the problem at hand and convey to business stakeholders the impact of a problem.

This chapter is all about understanding and defining a business problem, identifying key metrics related to it, and using these identified and generated KPIs through pandas and similar libraries for descriptive analytics. The chapter also covers how to plan a data science project through a structured approach and methodology, concluding with how to represent a problem...

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