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Advanced R Programming for Data Analysis and Machine Learning

Programming
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Created by

Last Update

September 13, 2023

Created On

July 06, 2023

Description

R is a programming language designed for statistical computing, data analysis, and visualization. It offers a wide range of functions and packages for manipulating and analyzing data

Overview

The course in R programming offers students the prospects of gaining skills in data analysis, visualization, data preparation, statistics, and machine learning. It opens doors to career opportunities in data-driven fields such as data analysis, research, data science, and business intelligence.

Features

  • Application of commonly employed Machine Learning algorithms.
  • Comprehensive coverage, spanning from fundamental concepts to advanced topics.
  • Access to downloadable learning materials.
  • Completion certificate

What you'll learn

  • Fundamentals of R
  • Matrix and Array Operations in R
  • Working with Data Frames in R
  • Data Import and Export in R
  • R Operators and Conditionals
  • Advanced R Techniques
  • Data Cleaning and Preprocessing in R
  • Overview of Statistical Analysis in R
  • Hypothesis Testing with R
  • Visualizing Data in R
  • R for Machine Learning

Prerequisites

Curriculum

  • 13 modules

R Fundamentals

Getting Started with R

Mathematical Operations in R

R Variable and Data Type Basics

Understanding Vectors in R

Manipulating Vectors in R

Accessing and Extracting Elements from Vectors in R

Comparing Values in R

Getting Started with Matrices and Arrays in R

Constructing Matrices in R

Performing Arithmetic Operations on Matrices in R

Applying Operations on Matrices in R

Accessing and Manipulating Matrix Elements in R

Working with Categorical Data in Matrices in R

Generating Multidimensional Arrays in R

Accessing Elements in Multidimensional Arrays in R

Understanding Data Frames in R

Fundamentals of Data Frames in R

Accessing and Selecting Data in Data Frames

Manipulating and Transforming Data Frames

Exploring Lists in R

Working with Strings in R

Displaying Strings in R

Combining Strings in R

Basic String Manipulation in R

Advanced String Manipulation in R

Introduction to Regular Expressions in R

Advanced Regular Expressions in R

Introduction to data input and output

CSV files in R

Excel files in R

Working with Logical Operators in R Conditional Statements in R Iteration with While Loops in R Iteration with For Loops in R Creating and Using Functions in R

Utilizing R's Built-in Features

Applying Functions to List Elements in R (lapply)

Simplifying Function Output in R(sapply)

Validating and Applying Functions in

Performing Mathematical Operations in R

Handling Dates and Timestamps in R

Data Manipulation with dplyr

Streamlining Data Manipulation with the Pipe Operator

Data Reshaping with tidyr

Handling Missing Data in R

Imputing Missing Data

• Introduction to Statistics • Mean, Median, and Mode • Variance, standard deviation, and coefficient of variability • Covariance and correlation

Introduction to Hypothesis testing

Standard errors and Confidence intervals

Hypothesis testing

Type-1 and type-2 error.

P-value

Exploring Data Visualization with ggplot

Visualizing Data Distributions with Histograms

Representing Relationships with Scatterplots

Displaying Categorical Data with Barplots

Examining Data Distribution and Outliers with Boxplots

Customizing Plot Layout and Arrangement

Styling and Customizing Visualizations with Themes

Creating Interactive Visualizations with Plotly

• Introduction to Machine Learning

• Linear Regression

• Logistic Regression

• K-Nearest Neighbours

• Decision Trees

• Random Forests

• Support Vector Machines

• K-Means Clustering

• Using the customer churn dataset, we will classify whether the customer will purchase Internet services based on various parameters.

• Project: Customer Churn Classification

• Dataset: Customer Churn Dataset

• Classification Task: Predicting Customer Internet Service Purchase

• Parameter Analysis: Exploring Various Parameters for Customer Churn Classification

• Model Building: Developing Classification Models for Customer Churn Prediction

• Evaluation Metrics: Assessing Model Performance for Customer Churn Classification

• Feature Importance: Identifying Key Parameters Influencing Customer Churn

• Predictive Insights: Drawing Conclusions and Recommendations based on Customer Churn Classification.

Instructors

Admin
Admin

Skoliko Faculty

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₹3500.00
  • Modules
    13 Modules
  • Duration
    7 Hours
  • Category
    Programming

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