Data Science hands on session

Hi,
The demo class for datascience on Monday (06/08/2015) at 8.00 PM(CST)/ 9.00 PM EST OR Tuesday 9th(INDIA) at 6.30 AM IST
Please join my gotowebinar
https://meetings.webex.com/collabs/#/meetings/detail?uuid=M8PKHO4IJ60WIJKYFV71KD3229-1IIH&rnd=239402.20069
Meeting Number:235 371 217

please confirm us your availability for the same.

For any further information on demo or Queries please revert back to admin@cloudera.training or contact us on 615 266 6667

Course duration - 30 days
session timing - every day one and half hour (8.00 pm CST / 9.00 PM EST) OR at 6.30 AM (INDIA) IST
Lab assistance - any time available
material - reference material provided
Course curicullum

Data Science course Curriculum

Trainer : Balakrishna Data Scientist from IIT

1.Introduction

   Introduction to Big Data
    Roles played by a Data Scientist
    Analysing Big Data using Hadoop and R
    Different Methodologies used for analysis in Data Science

2. Data Analsysis using R

Understanding vectors in R
Reading Data
Combining Data
sub-setting data
sorting data and some basic data generation functions

3.Learning Technics using R

    Machine Learning Overview
   ML Common Use Cases and techniques
    Clustering and Similarity Metrics
    Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models
    Understanding K-Means Clustering in Data Science
    Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
    Implementing Association rule mining in R.
    Understanding Process flow of Supervised Learning Techniques
    Decision Tree Classifier
    How to build Decision trees
    Random Forest Classifier
    What is Random Forests concept in data science
    Features of Random Forest
    Out of Box Error Estimate and Variable Importance
    Naive Bayes Classifier
    Integrating R with Hadoop using R
    Hadoop and RMR package
    Exploring RHIPE (R Hadoop Integrated Programming Environment)
    Writing MapReduce Jobs in R and executing them on Hadoop


Project Discussion
Problem Statement and Analysis
Various approaches to solve a Data Science Problem
Pros and Cons of different approaches and algorithms

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