Business Analytics with R

Course Overview

Business Analytics with R Training is a course designed to teach individuals how to use the programming language R to analyze and visualize data for business applications. The course covers topics such as statistical analysis, data manipulation, and data visualization, and aims to provide learners with the skills to extract insights and make data-driven decisions in a business setting.

At the end of the training, participants will be able to:

  1. Apply your in depth understanding of Business Analytics, Business Intelligence and Data Mining to projects
  2. Leverage your understanding of the core concepts of R Programming, Data Cleaning in R, Data Import in R
  3. Use R Analytics, Predictive Analytics, Data Visualisation to derive useful actionable insights
  4. Gain expertise in Data Import, Exploration & Manipulation in R
  5. Master modelling in R, Exploratory Data Analysis(EDA) & implementation of EDA on various datasets
  6. Master concepts of Data Mining Techniques, Statistical Modelling, Linear and Logistic Regression
 

Pre-requisite

A basic understanding of R programming and statistics is good to have. Knowledge of business analytics is not required.

 

Duration

3 days

Course Outline

  1. Understanding Data
  2. Introduction to Data Analytics
  3. Introduction to Business Analytics, Business Intelligence and Data Mining
  4. Analytical Decision Making
  5. Future of Business Analytics
  6. Big Data Analytics
  7. Social Media Analytics
  8. Basic Statistical Concepts
  9. Type of Data
  10. Sampling Techniques
  1. Introduction to R Programming
  1. Data Cleaning in R
  1. Data Import in R
  1. Exploratory Data Analysis(EDA) & Implementation
  • Data Visualization
  1. Basics of Machine Learning
  2. K : means clustering
  3. Cluster a dataset in MS Excel using k-Means
  4. Cluster a dataset in R using k-Means
  1. Association Rule Mining
  2. Collaborative Filtering
  1. Define a Statistical Model
  2. Understand and build Linear Models
  3. Compute Regression Statistics
  4. Determine ANOVA
  5. Variance (Anova) Technique
  6. Sentiment Analysis
  1. Understand the concept of Multiple Regression
  2. Create a Multiple Regression Model in MS Excel
  3. Create a Multiple Regression Model in R
  4. Understand the concept of Logistic Regression
  5. Create a Logistic Regression Model in MS Excel

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