## 9007116752

## c3creativedomain@gmail.com

# Data Science with R Training

data science career.

2) R for data science can be used for statistical analysis

and other functions.

3) R allows users to explore ,model,visualize data.

4) R has various statistical and graphical capabilities.

## What is R Programming Language ?

R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.” The R Development Core Team currently develops R. It is also a software environment used to analyze statistical information, graphical representation, reporting, and data modeling. R is the implementation of the S programming language, which is combined with lexical scoping semantics.

## Why Learn R Programming Language ?

There are several tools available in the market to perform data analysis. Learning new languages is time taken. The data scientist can use two excellent tools, i.e., R and Python. We may not have time to learn them both at the time when we get started to learn data science. Learning statistical modeling and algorithm is more important than to learn a programming language. A programming language is used to compute and communicate our discovery.

The important task in data science is the way we deal with the data: clean, feature engineering, feature selection, and import. It should be our primary focus. Data scientist job is to understand the data, manipulate it, and expose the best approach. For machine learning, the best algorithms can be implemented with R. Keras and TensorFlow allow us to create high-end machine learning techniques. R has a package to perform Xgboost. Xgboost is one of the best algorithms for Kaggle competition.

R communicate with the other languages and possibly calls Python, Java, C++. The big data world is also accessible to R. We can connect R with different databases like Spark or Hadoop.

## Course Curriculum

## R Programming For Data Science For Beginners

- What is R?
- Why R?
- Installing R
- R environment
- How to get help in R
- R Studio Overview

- Environment setup
- Data Types
- Variables Vectors
- Lists
- Matrix
- Array
- Factors
- Data Frames
- Loops
- Packages
- Functions
- In-Built Data sets
- Class & Objects

- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to CSV file

- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques

- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Standard Deviation
- Variance
- Correlation

- Importance of Statistics with Respect to Data Science
- Levels of Measurement
- Measures of Central Tendency
- Measure of Dispersion
- Measures of Shape
- Covriance & Correlation

- Data Importing and Exporting
- Data Cleaning and Preprocessing
- Data Manipulation
- Exploratory Data Analysis (EDA)
- Handling Time Series Data
- Data Export

- Handling Missing Data
- Removing Duplicates
- Data Type Conversion
- Renaming Columns
- Filtering and Subsetting Data
- String Manipulation
- Handling Outliers
- Merging and Joining DataFrames
- Pivoting and Reshaping Data
- Grouping and Aggregation

**Scatter Plots****Line Plots****Bar Charts****Histograms****Box Plots****Heatmaps****Pie Charts****Area Plots****Bubble Charts**

## Quick Contact Form

## Minimum Eligibility 10 + 2 Pass

Course Duration 3 Months

Mode of Training Online & Offline

## Machine Learning with R programming

- What is R?
- Why R?
- Installing R
- R environment
- How to get help in R
- R Studio Overview

- Environment setup
- Data Types
- Variables Vectors
- Lists
- Matrix
- Array
- Factors
- Data Frames
- Loops
- Packages
- Functions
- In-Built Data sets
- Class & Objects

- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to CSV file

- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques

- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Standard Deviation
- Variance
- Correlation

- Machine Learning Fundamentals
- Need for Machine Learning
- Machine Learning with R

- Linear Regression
- Linear Equation
- Slope
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure
- ROC curve
- Bias Variance Tradeoff

- K-Means
- K-Means ++
- Hierarchical Clustering

- Linear Regression
- Logistic Regression
- K-Means
- K-Means++
- Hierarchical Clustering – Agglomerative
- CART
- 5.0
- Random forest
- Naïve Bayes