BUSINESS ANALYTICS WITH R ONLINE TRAINING
Business analytics with R Introduction
Business analytics with R course will introduce you to the concept of data mining, business analytics, data manipulation, data visualization, exploratory data analysis, sentiment analysis and the concepts using open source R – Programming language & many other. R is a providing excellent capability to the analytics professionals open source programming environment. It much easier to pick up as compared to java like languages, it is high level programming language making.
- You don’t need to have a programming back ground to do
- Basic knowledge on R – Programming from scratch.
- It’s good to have basic understanding of statistics.
BUSINESS ANALYTICS WITH R ONLINE COURSE CONTENT
1. INTRODUCTION OF BUSINESS ANALYTICS AND R
- Introduction to Business Analytics with Applications
- Career opportunities
- Installation Procedure of RCmdr & RStudio interface
2. R PROGRAMMING
- Basic Math operations.
- Advanced Control Statements, Data Structures,& Loop
- Built in Function.
- Statistical Measures.
- Probability Distribution and its types.
- Creating Summary statistics.
4. IMPORTING DATA FROM DIFFERENT SOURCES
- CSV, Text, Excel, MySQL Data base
- Statistical Tools
- Web Scraping
5. DATA MANIPULATION IN R
- Apply Family, DPLYR Package.
- String Manipulation.
- Data Reshaping
6. DATA VISUALIZATION IN R
- Data Visualization Introduction
- Difference between Multivariate graphs & Bivariate , Univariate
- Different types of Graphs
7. DATA EXPLORATION AND PREPARATION
- EDA (Exploratory Data Analysis)
- Cross tabulation.
- Anomaly detection & Graphical analysis
8. DATA MINING WITH MACHINE LEARNING
- Introduction to Machine Learning, Data Mining.
- Clustering & its Application.
- Clustering Techniques.
9. ASSOCIATION RULE MINING AND SENTIMENT ANALYSIS
- Association Rule Mining & its Algorithm.
- Sentiment Analysis.
- Mining data from Twitter.
10. PREDICTIVE MODELLING TECHNIQUES
- Hypothesis Testing.
- T-Test , Z-Test
- Chi Square Test
11. REGRESSION MODELLING IN R
- Regression Introduction
- Linear Regression
- Logistic Regression
12. DECISION TREE AND RANDOM FOREST
- Decision Tree & its terminology
- Building Decision Trees
- Decision Tree splitting method
- Random Forest Introduction
- Party package
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