Big data training new york | Big data Analytics training in New york

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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