Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale
Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale
This book provides a unique perspective on applying data science with Hadoop by explaining what data science with Hadoop is all about, its practical business applications, and then diving deep into the details and providing a hands-on tutorial and showcase of various use-cases from the real world. The authors bring together all the practical knowledge students will need to do real, useful data science with Hadoop.
The full text downloaded to your computer
With eBooks you can:
- search for key concepts, words and phrases
- make highlights and notes as you study
- share your notes with friends
eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps.
Upon purchase, you will receive via email the code and instructions on how to access this product.
Time limit
The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
- Buch 41,72 €
- eBook (Kortext PDF) 22,46 €
- eBook (Kortext ePub) 41,30 €
Produktdetails
Artikelbeschreibung
The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students
Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials.
The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization.
Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP).
This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives.
Learn
- What data science is, how it has evolved, and how to plan a data science career
- How data volume, variety, and velocity shape data science use cases
- Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark
- Data importation with Hive and Spark
- Data quality, preprocessing, preparation, and modeling
- Visualization: surfacing insights from huge data sets
- Machine learning: classification, regression, clustering, and anomaly detection
- Algorithms and Hadoop tools for predictive modeling
- Cluster analysis and similarity functions
- Large-scale anomaly detection
- NLP: applying data science to human language