R training course outline

This program is designed for professionals who want to solve real-world business problems using complex structured and unstructured data with an emphasis on the importance of asking more meaningful research and business questions while effectively communicating findings.

We offer the blend of face-to-face week end classes and online courses .

Audience:

Just passed out college graduates
Business Intelligence professionals
Professionals aspiring to take up Data Scientist certification
Big Data Practitioners & Analytics Engineers
Business and Data Analysts
Analytics Project managers

Pre requisites : No Statistics or Java experience is required. Basics statistics knowledge is preferable but
                          not mandatory

Duration/Training/Fee related queries pl contact 9840014739

Module 1
Big data & Data Science Introduction	Introduction to Big Data &Analytics     Hadoop eco system      Big Data use cases     Data Science Overview     Role of Data Scientist     Hadoop Architecture
Module 2
Data Acquisition	(i)                  HDFS shell commands (ii)                Install & configure Hadoop Sqoop, Flume & PIG tools (iii)               Export and import data using Sqoop. Extract the data from web logs using Flume. (iv)              Extract, Transform and Load data from web logs and databases into Hadoop Cluster using Sqoop, PIG and flume.
Module 3
Data Evaluation & Transformation	(i)                  Working with various file formats including binary files, JSON, XML and csv (ii)                Evaluating data using various tools (iii)               Understanding of Data sets sampling and filtering (iv)              Writing Map only Hadoop jobs (v)                Joining data sets (vi)              Write records into new formats such as SequenceFileOutputFormat and AvroOutputFormat
Module 4
Statistics Level 1	Introduction to Statistics Samples & Populations  Statistics Basics Sampling Concepts Sample Selection methods Presenting Categorical variables in Chart using R Presenting Numerical variables in Chart using R Descriptive Statistics Measuring the Central Tendency using R Measuring Spread – quartiles and 5-number summary Measures of Position Measures of Variation
Statistics Level 2	Inferential Statistics Probability Rules of Probabilities, Assigning Probabilities Probability Distributions, Binomial and Poisson Probability Distributions Hypothesis Testing           Session 1           Session 2 & 3 Anova & Chi-Square testing              Session 1              Session 2              Session 3 & 4
Module  5
Introduction to R	R History Integrating R with Hadoop Basic Data Types Vectors Factors             Matrix             List             Data Frame Creating Data sets, transformation of data sets using R map-reduce programs Basic  data manipulation using R
Module 6
Data Science & Machine Learning – Level 1	Data Science fundamentals Data Science use cases Machine Learning fundamentals Types of Machine Learning algorithms Identify the algorithms appropriate to each model Building machine learning models using Apache Mahout & R Supervised machine learning        Fundamentals of Regression        Steps for training a set of data in order to        identify new data based on known data        Linear Regression  - Forecasting        Logistic Regression – Forecasting        Support Vector Machines
Machine Learning – Level 2	Unsupervised machine Learning         Market Basket Analysis using Association Rules         Clustering fundamentals and its use cases         Cluster Analysis         Decision Trees         Time Series Analysis         K-means clustering Recommendation Algorithms         Item based         User based Text mining
Module 7
Model Optimization	Bagging Boosting Random Forests
Case study	Insurance, Retail , Telecom, BFS domains