- About the Course
- Intended Audience
- Syllabus
COURSE OVERVIEW
The Data Science and Big Data Analytics course gives down to earth establishment level preparing that empowers prompt and successful interest in Big Data and different Analytics ventures. It incorporates a prologue to Big Data and the Data Analytics lifecycle to address business challenges that influence Big Data. The course gives establishing in essential and progressed systematic techniques and a prologue to Big Data Analytics innovation and instruments. Lab sessions offer chances to see how these strategies and devices might be connected to true business challenges by a rehearsing Data Scientist. This course gives an industry accreditation to business investigators, information distribution center specialists or different experts with comparative foundations to help them change into the universe of Data Science and Big Data Analytics that has extraordinary difficulties and opportunities.
- Big Data Characteristics, Challenges with traditional system
- Anatomy of Hadoop Cluster, Installing and Configuring Hadoop
- Hands-On Exercise
- Name Nodes and Data Nodes
- Hands-On Exercise
- The HDFS command line and web interfaces
- The HDFS Java API (lab)
- How Map Reduce Works?
- The Mapper & Reducer, Input Formats & Output Formats, Data Type & Customer Writable
- Setting up Eclipse Development Environment, Creating Map Reduce Projects,Debugging and Unit Testing MapReduce Code, Testing with MRUnit
- More common algorithms: sorting, indexing and searching (lab)
- Hands-On Exercise
- Introduction to R tool
- R and Hadoop Integration
- Hadoop Streaming using R
- RHadoop Overview
- RHive Overview
- Analytics Project Overview
- Steps Invovled in Aanlytics Project
- Analytics Techniques and Applications in Business
- Implications of Big Data on Analytics
