Data Science Using R

"Data Science Using R: Comprehensive Training in Data Analysis, Visualization, and Machine Learning Techniques .

What you'll learn

Proficient R Programming: Develop a solid foundation in R programming for data manipulation, analysis, and visualization.

Statistical Analysis Skills: Apply statistical methods and machine learning algorithms to derive meaningful insights from datasets.

Data Visualization Mastery: Create compelling visualizations using ggplot2 to effectively communicate data findings and trends.

Practical Application: Complete real-world projects that enhance problem-solving abilities and demonstrate proficiency in data science concepts.

Requirements

To enroll in the Data Science Using R course, participants should have a basic understanding of programming concepts, as familiarity with any programming language will facilitate the learning process. A foundational knowledge of statistics is also beneficial, as it will help students grasp essential data analysis techniques more effectively. Additionally, proficiency in general computer literacy and software applications is required to navigate R and its associated tools. Most importantly, a strong eagerness to learn and a curiosity about data science are crucial for success in this course. These prerequisites will ensure that all students are well-prepared to dive into the exciting world of data science.

Description

This course, "Data Science with R," is designed for aspiring data scientists and analysts seeking to harness the power of R for data manipulation, analysis, and visualization. Participants will begin by gaining a solid foundation in R programming, covering key concepts such as data types, structures, and essential functions.As the course progresses, students will delve into data wrangling techniques using packages like dplyr and tidyr, enabling them to clean and prepare datasets for analysis. The curriculum emphasizes statistical analysis, including hypothesis testing, regression models, and machine learning algorithms, empowering participants to draw meaningful insights from their data.Visualization is a key focus, with instruction on using ggplot2 to create informative and engaging graphics that communicate results effectively. Real-world case studies and hands-on projects will provide practical experience, allowing students to apply their skills to actual data challenges.By the end of the course, participants will have developed a comprehensive toolkit for data science, including proficiency in R, an understanding of statistical methodologies, and the ability to present their findings clearly. This course is perfect for those looking to kickstart a career in data science or enhance their analytical capabilities in any field and sorroundings.IIBM Institute of Business Management.

Overview

Section 1: R Introduction

Lecture 1 R Introduction

Section 2: R Implementation, R Data Structures, R Interfaces, R Interfaces

Lecture 2 R Implementation, R Data Structures, R Interfaces, R Interfaces

Section 3: Data Visualization Using R Software

Lecture 3 Introduction to Visualisation - Line Plots and Bar Charts - Pie Chart and Histog

Section 4: Predictive Customer Analytics using R - Linear Regression using R Software

Lecture 4 Predictive Customer Analytics using R - Linear Regression using R Software-Part1

Lecture 5 Predictive Customer Analytics using R - Linear Regression using R Software-Part2

Section 5: Bank Loan Modelling using R

Lecture 6 Logistic Function - Single Predictor Model.

Section 6: Sales Promotion Effectiveness -Dimension Reduction using R Software.

Lecture 7 Sales Promotion Effectiveness -Dimension Reduction using R Software - Part 1

Lecture 8 Sales Promotion Effectiveness -Dimension Reduction using R Software - Part 2

Section 7: Customer and Market Segmentation - Cluster Analysis using R

Lecture 9 Customer and Market Segmentation - Cluster Analysis using R Software-Part 1

Lecture 10 Customer and Market Segmentation - Cluster Analysis using R Software-Part 2

Section 8: Retail Analytics: Market Basket Analysis (MBA) - Association Rule using R.

Lecture 11 Association Rule Introduction - Apriori Algorithm - Multiple Association Rules

Section 9: Customer Loyalty Analytics- Naïve Bayes Classification using R Software.

Lecture 12 Naïve Bayes Introduction - Probabilistic Basics and Probabilistic Classification

Section 10: K – Nearest Neighbour (KNN) Using R Software

Lecture 13 K – Nearest Neighbour Introduction - K – Nearest Neighbour Algorithm.

Section 11: Decision Trees using R Software

Lecture 14 What is a Decision Tree? - How to create Decision Tree

Section 12: Random Forest using R Software

Lecture 15 Ensample of Decision Tree.

Section 13: Support Vector Machine - SVM

Lecture 16 Linear SVM using Hyperplane - Non-Linear Hyperplane using Kernal Trick.

Section 14: Real Time Project - Customer Loyalty Analytics and its Application.

Lecture 17 RFM Segmentation and Analysis - Propensity Modelling and its application.

Lecture 18 Real Time Project - Customer Loyalty Analytics and its Application

Section 15: Real Time Project - Finance Analytics and its Application using R

Lecture 19 Credit Risk Analytics using Logistic Regression.

Section 16: Course Complete Revision

Lecture 20 Course Complete

This course is for aspiring data scientists, analysts, and researchers seeking to enhance their R programming skills, gain insights from data, and apply analytical techniques in real-world scenarios.