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Big Data Analysis with Python

You're reading from  Big Data Analysis with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Pages 276 pages
Edition 1st Edition
Languages
Authors (3):
Ivan Marin Ivan Marin
Profile icon Ivan Marin
Ankit Shukla Ankit Shukla
Profile icon Ankit Shukla
Sarang VK Sarang VK
Profile icon Sarang VK
View More author details

Table of Contents (11) Chapters

Big Data Analysis with Python
Preface
1. The Python Data Science Stack 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

Defining a Business Problem


A business problem in data science is a long-term or short-term challenge faced by a business entity that can prevent business goals being achieved and act as a constraint for growth and sustainability that can otherwise be prevented through an efficient data-driven decision system. Some typical data science business problems are predicting the demand for consumer products in the coming week, optimizing logistic operations for third-party logistics (3PL), and identifying fraudulent transactions in insurance claims.

Data science and machine learning are not magical technologies that can solve these business problems by just ingesting data into pre-built algorithms. They are complex in terms of the approach and design needed to create end-to-end analytics projects.

When a business needs such solutions, you may end up in a situation that forms a requirement gap if a clear understanding of the final objective is not set in place. A strong foundation to this starts with...

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