Big data training new york | Big data Analytics training in New york.
Introduction to Big Data Analytics
Defining Big Data analytics
- Discovering value from large data sets
- Exploiting data to optimize decision making
Planning your analytics lifecycle project
- Outlining steps in the lifecycle
- Contrasting traditional analytics with Big Data analytics
Representing Big Data with R and Rattle
Preparing the data
- Loading data for knowledge discovery
- Spotting outliers in the data
- Transforming and summarizing data
Visualizing data characteristics
- Revealing changes over time
- Displaying proportions within your data
- Leveraging maps to display spatial relationships
- Displaying relationships across categories
Modeling and Predictive Data Analysis
Categorizing analytic approaches
- Predictive vs. descriptive analytics
- Supervised vs. unsupervised learning
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Applying appropriate mining techniques
- Discovering unknown groups through clustering
- Detecting relationships with association rules
- Uncovering decision tree classifications
- Identifying patterns with time series analysis
- Employing genetic programming for data exploration
Leveraging Analytics with RHadoop
Expanding the analytic capabilities of your organization
- Exploring the MapReduce and Hadoop architecture
- Creating and executing Hadoop MapReduce jobs
Integrating R and Hadoop with RHadoop
- Examining the components of RHadoop
- Creating modules for RHadoop jobs
- Executing RHadoop jobs
- Monitoring job execution flow
Building a Recommendation Framework
Streamlining business decisions
- Considering motivations for a recommender engine
- Leveraging recommendations based on collaborative filtering
Developing the framework with Mahout
- Exploring the architecture of the recommendation framework
- Building programming components
- Executing the recommendation model
- Performing tradeoff analysis
Mining Unstructured Data
Investigating business value within unstructured data
- Making a business case for unstructured data mining
- Extending business intelligence with mining tools
Implementing text mining and social network analysis
- Analyzing the structure of text mining
- Evaluating mining approaches
- Building a text mining framework for Hadoop MapReduce
- Inspecting social network interactions
Planning and Implementing a Complete Data Analytics Solution
Transforming business objectives to analytic projects
- Making use of business analysis frameworks
- Selecting a perspective within the framework
- Identifying performance metrics targets
Implementing the analytics lifecycle
- Finding core data sets
- Preparing the data for analysis
- Modeling the data
- Executing the model
- Communicating results
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