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Big Data Analytics

In today’s environment of data abundance, the ability to discover unique insight enables organizations to improve decision making. This will empower the organization to identify new revenue generating opportunities, minimize risks, and control costs. Big Data Analytics is not just about managing more or diverse data. Rather, it is about asking new questions, formulating new hypotheses, exploration and discovery, and making data-driven decisions.

Course Objectives:

1) Introduction to Big Data Analytics

Defining Big Data Analytics

  • Discovering value from large data sets
  • Exploiting data to optimize decision-making
  • Planning your analytics life cycle project
  • Outlining steps in the life cycle
  • Contrasting traditional analytics with Big Data analytics

Planning your analytics life cycle project

  • Outlining steps in the life cycle
  • Contrasting traditional analytics with Big Data analytics

2) 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 charts to display relationships
  • Displaying relationships across categories

3) Modeling and Predictive Data Analysis

Categorizing Analytic Approaches

  • Predictive vs. descriptive analytics
  • Supervised vs. unsupervised learning

Applying Appropriate Mining Techniques

  • Discovering unknown groups through clustering
  • Detecting relationships with association rules
  • Uncovering decision tree classifications
  • Identifying patterns with time series analysis

4) 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

5) 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

6) 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
  • Inspecting social network interactions

7) Planning and Implementing a Complete Data Analytics Solution

Transforming business objectives to analytic projects

  • Arguing your business case for analytics
  • Mapping analytics models to business objectives
  • Identifying performance metrics targets

Implementing the analytics life cycle

  • Finding core data sets
  • Preparing the data for analysis
  • Modeling the data
  • Executing the model
  • Communicating results

8) Ensuring a Successful Data Analytics Solution

  • Identifying barriers to Big Data analytics
  • Managing and mitigating risks
  • Employing an implementation checklist

Who should go for this course?

This course is intended for fresher’s, managers, data and business analysts, database professionals and others involved in forecasting and trends management. Programming and a background in statistics is helpful, but not required.

Pre-Requisites

To complete this course successfully and gain the maximum benefits from it, a student should have the following knowledge and skill sets:

  • Familiarity with basic statistics
  • Good command over a scripting language
  • Beginner level knowledge in SQL

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