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Data Science Trainers

Data Science

Course Overview:

In this training program experience world-class Data Science training by an industry leader on the most in-demand Data Science and Machine learning skills. Gain hands-on exposure to key technologies including R, Python, Tableau, Hadoop, and Spark. Become an expert.Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.

Course Content:

Introduction to R

♦ What is R?

♦Why R?

♦ Installing R

♦ R environment

♦ How to get help in R

♦ R Studio Overview

Understanding R data structure

♦ Variables in R

♦ Scalars

♦ Vectors

♦ Matrices

♦ List

♦ Data frames

♦ Cbind,Rbind, attach and detach functions in R

♦ Factors

♦ Getting a subset of Data

♦ Missing values

♦ Converting between vector types

Importing data

♦ Reading Tabular Data files

♦ Reading CSV files

♦ Importing data from excel

♦ Loading and storing data with clipboard

♦ Accessing database

♦ Saving in R data

♦ Loading R data objects

♦ Writing data to file

♦ Writing text and output from analyses to file

Manipulating Data

♦ Selecting rows/observations

♦ Rounding Number

♦ Creating string from variable

♦ Search and Replace a string or Number

♦ Selecting columns/fields

♦ Merging data

♦ Relabeling the column names

♦ Data sorting

♦ Data aggregation

♦ Finding and removing duplicate records

Using functions in R

♦ Apply Function Family

♦ Commonly used Mathematical Functions

♦ Commonly used Summary Functions

♦ Commonly used String Functions

♦ User defined functions

♦ local and global variable

♦ Working with dates

R Programming

♦ While loop

♦ If loop

♦ For loop

♦ Arithmetic operations

Charts and Plots

♦ Box plot

♦ Histogram

♦ Pie graph

♦ Line chart

♦ Scatterplot

♦ Developing graphs

♦ Cover all the current trending packages for Graphs

Machine Learning Algorithm

♦ Sentiment analysis with Machine learning

♦ C 5.0

♦ Support vector Machines

♦ K Means

♦ Random Forest

♦ Naïve Bayes algorithm

Statistics

♦ Correlation

♦ Linear Regression

♦ Non Linear Regression

♦ Predictive time series forecasting

♦ K means clustering

♦ P value

♦ Find outlier

♦ Neural Network

♦ Error Measure

Leading Topics

♦ Overture of R Shiny

♦ What is Hadoop

♦ Integration of Hadoop in R

♦ Data Mining using R

♦ Clinical research preface in R

♦ API in R (Twitter and Facebook)

♦ Word Cloud in R.

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