R Analytics Programming Training Syllabus In Bangalore

Duration : 40 Hours
Course Fees : 20000/- + GST
Batch Size Minimum : 05

CHAPTER 1 : What is Data Analytics

1.   R tools and their uses in Business Analytics
2. Objectives
3. Analytics
4. Where is analytics applied?
5. Responsibilities of a data scientist 
6. Problem definition
7. Summarizing data
   Data collection    
        

CHAPTER 2 : About R

1. Difference between R and other analytical languages
2. Different data types in R
3. Built in functions of R: seq(), cbind (), rbind(), merge()
4. Subsetting methods
5. Use of functions like str(), class(), length(), nrow(), ncol(),head(), tail()
   
   

CHAPTER 3 : Data manipulation in R

1. Steps involved in data cleaning
2. Problems and solutions for Data cleaning
3. Data inspection
4. Use of functions grepl(), grep(), sub()
5. Use of apply() function
6. Coerce the data       
        

CHAPTER 4 : Data Import techniques

1.   How R handles data in a variety of formats
2. Importing data from csv files, spreadsheets and text files
3. Import data from other statistical formats like sas7bdat and sps
4. Packages installation used for database import
5. Connect to RDBMS from R using ODBC and basic SQL queries in R
6. Basics of Web Scraping 
   
   

CHAPTER 5 : Exploratory Data analysis

1. Understanding the Exploratory Data Analysis(EDA)
2. Implementation of EDA on various datasets
3. Boxplots
4. Understanding the cor() in R
5. EDA functions like summarize()
6. llist()
7. Multiple packages in R for data analysis
8. Segment plot HC plot in R     
      

CHAPTER 6 : Data Visualization in R

1. Understanding on Data Visualization
2. Graphical functions present in R
3. Plot various graphs like tableplot
4. Histogram
5. Box Plot
6. Customizing Graphical Parameters to improvise the plots
7. Understanding GUIs like Deducer and R Commander
8. Introduction to Spatial Analysis  
   
   

CHAPTER 7 : Data Mining: Clustering Techniques

1. Introduction to Data Mining
2. Understanding Machine Learning
3. Supervised and Unsupervised Machine Learning Algorithms
4. K-means Clustering 
   
   

CHAPTER 8 : Data Mining: Association Rule Mining and Sentiment Analysis

1. Association Rule Mining
2. Sentiment Analysis
   
   

CHAPTER 9 : Linear and Logistic Regression

1. Linear Regression
2. Logistic Regression
3. CHAPTER 10: Anova
4. Anova
5. CHAPTER 11: Predictive Analysis
6. Predictive Analysis    
   
   

CHAPTER 12 : More on Data Mining

1. Decision Trees
2.   Algorithm for creating Decision Trees
3. Greedy Approach: Entropy and Information Gain
4. Creating a Perfect Decision Tree
5. Classification Rules for Decision Trees
6. Concepts of Random Forest
7. Working of Random Forest
8. Features