R Programming Certification TOC

Course Outline

Module 1: Introduction to Business Analytics

  • Introduction to terms like Business Intelligence, Business Analytics, Data, Information.
  • How information hierarchy can be improved/introduced.
  • Understanding Business Analytics and R.
  • Knowledge about the R language, its community and ecosystem.
  • Understand the use of 'R' in the industry.
  • Compare R with other software in analytics.
  • Install R and the packages useful for the course.
  • Perform basic operations in R using command line.
  • Learn the use of IDE R Studio and Various GUI.
  • Use the ‘R help’ feature in R.
  • Knowledge about the worldwide R community collaboration.

  • Module 2: Introduction to R Programming :

  • The various kinds of data types in R and its appropriate uses.
  • The built-in functions in R like: seq(), cbind (), rbind(), merge().
  • Knowledge on the various subsetting methods.
  • Summarize data by using functions like: str(), class(), length(), nrow(), ncol().
  • Use of functions like head(), tail(), for inspecting data.
  • Indulge in a class activity to summarize data.
  • dplyr package to perform SQL join in R

  • Module 3: Data Manipulation in R :

  • The various steps involved in Data Cleaning.
  • Functions used in Data Inspection.
  • Tackling the problems faced during Data Cleaning.
  • Uses of the functions like grepl(), grep(), sub().
  • Coerce the data, uses of the apply() functions.

  • Module 4: Data Import Techniques in R :

  • Import data from spreadsheets and text files into R.
  • Import data from other statistical formats like sas7bdat and spss.
  • Packages installation used for database import.
  • Connect to RDBMS from R using ODBC and basic SQL queries in R.
  • Basics of Web Scraping.

  • Module 5: Exploratory Data Analysis :

  • Understanding the Exploratory Data Analysis(EDA).
  • Implementation of EDA on various datasets.
  • Boxplots, whiskers of Boxplots.
  • Understanding the cor() in R.
  • EDA functions like summarize(), llist()
  • Multiple packages in R for data analysis
  • Fancy plots like the Segment plot, HC plot in R.

  • Module 6: Data Visualization in R :

  • Understanding on Data Visualization.
  • Graphical functions present in R

  • R Programming Certification TOC :

  • Plot various graphs like tableplot, histogram, Boxplot.
  • Customizing Graphical Parameters to improvise plots.
  • Understanding GUIs like Deducer and R Commander
  • Introduction to Spatial Analysis.

  • Module 7: Data mining: clustering techniques :

  • Introduction to Data Mining
  • Concept of Machine Learning
  • Understanding Supervised Machine Learning algorithms.
  • Understanding unsupervised Machine Learning Algorithms
  • K-means Clustering.
  • Hierarchical Clustering

  • Module 8: Data Mining: Association rule mining and Sentiment analysis :

  • Association Rule Mining.
  • User Based Collaborative Filtering (UBCF)
  • Item Based Collaborative Filtering (IBCF)
  • Sentiment Analysis

  • Module 9: Linear and Logistic Regression :

  • What is Regression.
  • Linear Regression
  • Logistic Regression.

  • Module 10: Annova and Predictive Analysis and Data Mining :

  • Decision Trees and Random forest
  • Anova
  • Sentiment Analysis
  • Decision Tree, the 3 elements for classification of a Decision Tree
  • Entropy
  • Gini Index
  • Pruning and Information Gain.
  • Bagging of Regression and Classification Trees
  • Concepts of Random Forest
  • Working of Random Forest, features of Random Forest, among others