**Course Overview / Description**:

The Data Analytics and statistics using R Training is a “15 hour Hands-On Training”. This Training is intended for students interested in making a career in Data Analytics. This training covers understanding the basics of Statistics, R programming and Data Cleansing which is a first step in any Analytics project. This training is made basic to understand concepts of statistics that Machine Learning Algorithms work on. It also hand in hand will help the participant familiarise with hands on R programming.

**Core Idea**: Get a complete understanding of Data cleansing and hands on practise helps practical understanding of the challenges to data.

**Intent**: To help and enhance skills that are fast creating employment in the market

**Prerequisites / Eligibility**: Passion to learn.

**Detailed Course Content / Training Schedule/ Curriculum **:

**Data Wrangling**a. Data collection and Data types b. DATA Treatment c. Issues that affect data d. Different ways to cleanse data e. dplyr Package f. Data.table package h. Reshape2 package i. Tidyr package**Data Transformation**a. Normalisation of data b. Linear transformation c. Logarithm transformers**Data Processing****Introduction to R**a. Data collection and Data types b. Data types in R c.BASIC AND Metamodel commands in R d. Subsetting data in R e. Installing packages**Descriptive statistics**a. EDA - Univariate data b. Measures of Central Tendencies c. Measures of Dispersion d. Scope of Data Analysed - EDA(Fleet Industry) e. basic commands in R**Bivariate Analysis**a. Correlation Analysis b. types of correlation c. Pearsons correlation d. Spearmans rank correlation e. Kendall rank correlation f. Phi coefficient g. Tetra choric correlation h. Point biserial correlation i. Cross-tabs and Associations**Exploratory Data Analysis****Variance Influence factor****Maulticollinearity**

**Why learn / Advantages**:

This Training is intended for students/professionals interested in making a career in the Analytics domain as this training would help them to understand key concepts of statistics, R programming and Data cleansing which leads them to the next stage of Machine learning and model building a much easier exercise.

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