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.

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