Now that we've covered the common security use cases and discussed some of the tools that a data scientist needs to be aware of in their everyday activities, there's one last important item to note. While in their custody, the responsibility for data, including its security and integrity, lies with the data scientist. This is usually true whether or not you are explicitly told. Therefore, it is crucial that you take this responsibility seriously and take all the necessary precautions when handling and processing data. If needed, also be ready to communicate to others their responsibility. We all need to ensure that we are not held responsible for a breach off-site; this can be achieved by highlighting the issue or, indeed, even having a written contract with the off-site service provider outlining their security arrangements. To see a real-world example of what can go wrong when you don't pay proper attention to due diligence, have a look at some security notes...
Mastering Spark for Data Science
By :
Mastering Spark for Data Science
By:
Overview of this book
Data science seeks to transform the world using data, and this is typically achieved
through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs.
This book deep dives into using Spark to deliver production-grade data science
solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more.
You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Related Content you might be interested in
Current Title:
Mastering Spark for Data Science
Table of Contents (22 chapters)
Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
The Big Data Science Ecosystem
Data Acquisition
Input Formats and Schema
Exploratory Data Analysis
Spark for Geographic Analysis
Scraping Link-Based External Data
Building Communities
Building a Recommendation System
News Dictionary and Real-Time Tagging System
Story De-duplication and Mutation
Anomaly Detection on Sentiment Analysis
TrendCalculus
Secure Data
Customer Reviews