About This Book
- Use Apache Spark for data processing with these hands-on recipes
- Implement end-to-end, large-scale data analysis better than ever before
- Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data
Who This Book Is For
This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful.
What You Will Learn
- Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning.
- Solve real-world analytical problems with large data sets.
- Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale.
- Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package.
- Learn about numerical and scientific computing using NumPy and SciPy on Spark.
- Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models.
In Detail
Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease.
This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
