Hadoop All in 1 and R Programming Training: Combo Course

After the completion of this Hadoop all-in-one course, you will be able to:

• Excel in the concepts of Hadoop Distributed File System (HDFS)
• Implement HBase and MapReduce integration
• Understand Data Science Project Life Cycle, Data Acquisition and Data Collection
• Execute various Machine Learning Algorithms
• Understand Apache Hadoop 2.7 framework and architecture
• Learn to write complex MapReduce programs in both MRv1 and MRv2
• Design and develop applications involving large data using Hadoop Ecosystem
• Understand Prediction and Analysis Segmentation through Clustering
• Learn the basics of Big Data and ways to integrate R with Hadoop
• Learn various advanced modules like YARN, Flume, Hive, Oozie, Impala, ZooKeeper and Hue.
• Set up Hadoop infrastructure with single and multi-node clusters using Amazon EC2 (CDH4)
• Monitor a Hadoop cluster and execute routine administration procedures
• Understand the functioning of R-Calculator
• Master Vector Creation and assigning values to variables
• Generate Repeats and Factor levels
• Gain insight into database connectivity, reading data to ODBC tables, linear regression and logistic regression
• Prepare a comprehensive case study on R Programming using Hadoop

Hadoop Projects

1. Project – Working with MapReduce, Hive, Sqoop

Problem Statement – It describes how to import MySQL data using sqoop and querying it using hive and also describes how to run the word count MapReduce job.

2. Project – Work on Movie lens data for finding top records

Data – Movie Lens dataset

Problem Statement – It includes:

• Write a MapReduce program to find the top 10 movies from the u.data file
• Create the same top 10 movies using PIG by loading u.data into pig
• Create the same top 10 movies using HIVE by loading u.data into HIVE

3. Project – Hadoop Yarn Project – End to End PoC

Problem Statement – It includes:

• Import Movie data
• Append the data
• How to use sqoop commands to bring the data into the HDFS
• End to End flow of transaction data
• How to process the real word data or a huge amount of data using map reduce program in terms of the movie etc.

4. Project – Partitioning Tables

Problem Statement – It describes the parting and How to perform portioning. It includes:

• Manual Partitioning
• Dynamic Partitioning
• Bucketing

5. Project – Sales Commission

Data – Sales

Problem Statement – In this we calculate the commission according to the sales.

6. Project – Connecting Pentaho with Hadoop Ecosystem

Problem Statement – It includes:

• Quick Overview of ETL and BI
• Configuring Pentaho to work with Hadoop Distribution
• Loading data into Hadoop cluster
• Transforming data into Hadoop cluster
• Extracting data from Hadoop Cluster

7. Project – Multinode Cluster Setup

Problem Statement – It includes following actions:

• Hadoop Multi-Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
• Running Map Reduce Jobs on Cluster

8. Project – Hadoop Testing using MR

Problem Statement – It describes that how to test map reduce codes with MR unit.

9. Project – Hadoop Weblog Analytics

Data – Weblogs

Problem Statement – The goal is to enable the participants to have a feel of the actual data sets in a production environment and how to load the data into a Hadoop cluster using various techniques. Once data is loaded, the next goal is to perform basic analytics on this data.

2. R Programming Project – Restaurant Revenue Prediction

Data – Revenue Dataset

Problem Statement – It predicts the annual restaurant sales based on the objective measurements. It uses following data fields:

• Id
• Opening Date
• Type of the City
• Type of the Restaurant
• Three categories of Obfuscated Data
• Revenue

It also includes:

• Data Overview
• Data Fields
• Evaluation using RMSE
• Feature Engineering / Selection

Prerequisites:

• Basic knowledge of UNIX
• Prior knowledge of Apache Hadoop is not required
• Background knowledge in Statistics

Recommended Audience:

• Programming Developers, System Administrators and ETL Developers
• Project Managers eager to learn new techniques of maintaining large data
• Experienced working professionals aiming to become Big Data Analysts
• Professionals aiming to build career in real-time Data Analytics with Apache Storm techniques and Hadoop Computing
• Professionals aspiring to be a ‘Data Scientist’
• Information Architects to gain expertise in Predictive Analytics domain
• Mainframe Professionals, Architects and Testing Professionals
• Graduates eager to learn the latest Big Data technology.

• This course provides an exploratory data analysis approach using concepts of R Programming and Hadoop.
• It gives the complete study of effective data handling, amazing graphical facilities for data analytics and user-friendly ways to create top-notch graphics.
• Big multinational companies like Google, Yahoo, Apple, eBay, Facebook and many others are hiring skilled professionals capable of handling Big Data using Hadoop and Data Science techniques.
• The training certifies you for the biggest, top-paid job opportunities in top MNCs working on Big Data, R Programming and Hadoop.