Data science With R Corparate Trainning

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Data science With R online classes

Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR..


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Data Science With R Training Course Curriculum

Module 1

Data Science Introduction

✅ What is data science?

✅ Who are data scientist?

✅ What data scientist do?

✅ What skills data scientist require?

✅ Data Science Goals

✅ Data Scientists and Data Analysts responsibilities.

✅ Different tools available for Data Science
Module 2

Introduction to R

✅ Installing R and R-Studio

✅ R packages and R Operators

✅ if statements and loops (for, while, repeat, break, next), switch case

 

 

 

 
Module 3

Data Visualization

✅ Bar Graph (Simple, Grouped, Stacked)

✅ Histogram

✅ Pie Chart, Line Chart, Box (Whisker) Plot, Scatter Plot

✅ Correlogram

 

 

 
Module 4

Introduction to Statistics

✅ Terminologies of Statistics

✅ Measures of Centers, Measures of Spread

✅ Probability

✅ Normal Distribution

✅ Binary Distribution

✅ Hypothesis Testing, Chi-Square Test

✅ ANOVA
Module 5

Predictive Modeling – Linear Regression

✅ Supervised Learning – Linear Regression, Bivariate Regression, Multiple Regression Analysis, Correlation ( Positive, negative and neutral)

✅ Industrial Case Study

✅ Machine Learning Use-Cases

✅ Machine Learning Process Flow

✅ Machine Learning Categories
Module 6

Predictive Modeling Logistic Regression

✅ Logistic Regression

✅ What are Classification and its use cases?

✅ What is Decision Tree?

✅ Algorithm for Decision Tree Induction

✅ Confusion Matrix

✅ Creating a Perfect Decision Tree

 
Module 7

Random Forest

✅ Random Forest

✅ What is Naive Bayes?

✅ What is Clustering & its Use Cases?

✅ What is K-means Clustering?

✅ What is Canopy Clustering?

✅ What is Hierarchical Clustering?

 
Module 8

Association Analysis and Recommendation engine

p>✅ Market Basket Analysis (MBA)

✅ Association Rules

✅ Apriori Algorithm for MBA

✅ Introduction of Recommendation Engine

✅ Types of Recommendation – User-Based and Item-Based

✅ Recommendation Use-case
Module 9

Sentiment Analysis

✅ Introduction to Text Mining

✅ Introduction to Sentiment

✅ Setting up API Bridge, between R and Twitter Account

✅ Extracting Tweet from Twitter Account

✅ Scoring the tweet

 

 
Module 10

Time Series

✅ What is Time Series data?

✅ Time Series variables

✅ Different components of Time Series data

✅ Visualize the data to identify Time Series Components

✅ Implement ARIMA model for forecasting

✅ Exponential smoothing models

✅ Identifying different time series scenario based on which different Exponential Smoothing model can be applied

✅ Implement respective ETS model for forecasting

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