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Understand Cassandra’s distributed and decentralized structure
Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell
Create a working data model and compare it with an equivalent relational model
Develop sample applications using client drivers for languages including Java, Python, and Node.js
Explore cluster topology and learn how nodes exchange data
Maintain a high level of performance in your cluster
Deploy Cassandra on site, in the Cloud, or with Docker
Integrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene

Cassandra: The Definitive Guide 2nd

Build value from your data in a series of agile sprints, using the data-value pyramid
Extract features for statistical models from a single dataset
Visualize data with charts, and expose different aspects through interactive reports
Use historical data to predict the future via classification and regression
Translate predictions into actions
Get feedback from users after each sprint to keep your project on track

Agile Data Science 2.0

Learn how to import, manipulate, and export data with H2O
Explore key machine-learning concepts, such as cross-validation and validation data sets
Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification
Use H2O to analyze each sample data set with four supervised machine-learning algorithms
Understand how cluster analysis and other unsupervised machine-learning algorithms work

Practical Machine Learning with H2O

Delve into Pig’s data model, including scalar and complex data types
Write Pig Latin scripts to sort, group, join, project, and filter your data
Use Grunt to work with the Hadoop Distributed File System (HDFS)
Build complex data processing pipelines with Pig’s macros and modularity features
Embed Pig Latin in Python for iterative processing and other advanced tasks
Use Pig with Apache Tez to build high-performance batch and interactive data processing applications
Create your own load and store functions to handle data formats and storage mechanisms

Programming Pig 2nd

Familiarize yourself with the Spark programming model
Become comfortable within the Spark ecosystem
Learn general approaches in data science
Examine complete implementations that analyze large public data sets
Discover which machine learning tools make sense for particular problems
Acquire code that can be adapted to many uses

Advanced Analytics with Spark

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Learn how to apply the tidy text format to NLP
Use sentiment analysis to mine the emotional content of text
Identify a document’s most important terms with frequency measurements
Explore relationships and connections between words with the ggraph and widyr packages
Convert back and forth between R’s tidy and non-tidy text formats
Use topic modeling to classify document collections into natural groups

Text Mining with R

You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

Understand how data science fits in your organization—and how you can use it for competitive advantage
Treat data as a business asset that requires careful investment if you’re to gain real value
Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
Learn general concepts for actually extracting knowledge from data
Apply data science principles when interviewing data science job candidates

Data Science for Business

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