# Data Science with R

### Course Insides

## About the course

Data science is the field of study that combines domain expertise, programming skills, and knowledge of math and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems that perform tasks which ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users translate into tangible business value.

## Who should attend ?

- This course is meant for people with at least some programming experience
- Data Analyst
- Software Engineer
- Business Analyst

## Benefits

- The course is absolutely practical and real-time based on theory material provided in advance.
- The sessions are interactive and interesting
- All the queries are answered along with guidance on certification
- Data Science is greatly in demand. Prospective job seekers have numerous opportunities. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.
- Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.
- Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines in order to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.

## Modules

- What is Data Science?
- Introduction to Business Analytics
- Introduction to Machine Learning
- How to switch your career into ML

- What is R?
- Setting up R
- R Programming – R Operator
- R Conditional Statement & Loop
- R Programming – R Function
- Practice, Questions and exercise

- R Data Structure – Vector
- Matrix, Array and Data Frame
- A Deep Drive to R Data Frame
- R Data Structure – Factor
- Codes – Factor
- R Data Structure – List
- Code – List
- Practice, Questions and exercise

- Import CSV Data in R
- Import Text Data in R
- Import Excel, Web Data in R
- Export Data in R – Text
- Export Data in R – CSV & Excel
- Practice, Questions and exercise

- If and Nested If condition
- Types of Error
- Date Functions
- Conditional Formatting
- Practice, Questions and exercise

- Apply Function
- Select
- Mutate
- Filter
- Arrange
- Pipe Operator
- Group by
- Date
- Practice, Questions and exercise

- Introduction to Data Visualization & Scatter Plot
- Mfrow
- Pch
- Line Chart
- Bar Plot
- Pie Chart
- Histogram
- Density Plot
- Box Plot
- Mosaic Plot and Heat Map
- 3D Plot
- Correlation Plot and Word Cloud
- ggplot2
- Practice, Questions and exercise

- Introduction To Statistic
- Descriptive Statistics
- Common charts used
- Skewness
- Inferential Statistics
- Variance, standard deviations
- Covariance,Coefficient,Correlation
- Probability
- Normal Distributions
- Central Limit Theorem
- inferential statistics
- Confidence intervals
- Hypothesis Testing
- Practice, Questions and exercise

- What is Machine learning?
- Types of ML
- Basic steps of ML
- ML algorithms
- Practice, Questions and exercise

- Linear Regression Intuition
- Simple Linear Regression
- Multiple Linear Regression
- Regression Case Study
- Model Evaluation
- Practice, Questions and exercise

- Logistics Regression Intuition
- R Code Implementation
- Telecom Churn Case Study
- K-NN Intuition
- R Code Implementation
- K-NN Case Study
- SVM – Intuition
- R Code Implementation
- SVM Case Study
- Naive Bayes – Intuition
- Naive Bayes – R Code Implementation
- Naive Bayes – Case Study
- Decision Tree Intuition
- Decision Tree -How it works
- Decision Tree – R Code Implementation
- Decision Tree Pruning
- Decision Tree Case Study
- Naive Bayes – Intuition
- Naive Bayes – R Code Implementation
- Naive Bayes – Case Study
- Random Forest – Intuition
- Random Forest -R Code Implementation
- Random Forest – Case Study
- Model Evaluation
- Practice, Questions and exercise

- K-Mean Clustering Intuition
- K-Mean Clustering R Code Implementation
- K-Mean Clustering Case Study
- Hierarchical Clustering Intuition
- Hierarchical Clustering R Code Implementation
- Hierarchical Clustering Case Study
- DBScan Clustering -Intuition and R Code
- DBScan Clustering – Case Study
- Model Evaluation
- Practice, Questions and exercise

- PCA – Intuition
- PCA – R Code Implementation
- PCA – Case Study
- Association Rule Mining -Introduction
- Association Rule Mining -R Code Implementation
- Association Rule Mining – Case Study
- Practice, Questions and exercise

- Model Deployment – Workflow
- Model Deployment – Pre Requisite
- Model Deployment – Steps To Follow
- Model Deployment – Azure ML DEMO

Titanic Survival

Big Mart Sell

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₹ 20,000
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