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Introduction to Data Analytics:

  • Understand Business Analytics and R

  • Knowledge on the R language

  • community and ecosystem

  • Understand the use of 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 pne

  • Learn the use of IDE R Studio and Various GUI

  • Use the feature in R

  • Knowledge about the worldwide R community collaboration.

Introduction to R Programming:

  • The various kinds of data types in R and its appropriate uses

  • The built-in functions in R pke: seq(), cbind (), rbind(), merge(), Knowledge on the various Subsetting methods, Summarize data by using functions pke: str(), class(), length(), nrow(), ncol(), Use of functions pke head(), tail(), for inspecting data, Indulge in a class activity to summarize data.

Data Manipulation in R:

  • The various steps involved in Data Cleaning

  • Functions used in Data Inspection

  • Tracking the problems faced during Data Cleaning

  • Uses of the functions pke grepl(), grep(), sub(), Coerce the data, Uses of the apply() functions.

Data Import Techniques in R:

  • Import data from spreadsheets and text files into R

  • Import data from other statistical formats pke 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

Exploratory Data Analysis:

  • The Exploratory Data Analysis(EDA)

  • Implementation of EDA on various datasets

  • Boxplots

  • Understanding the cor() in R

  • EDA functions pke summarize(), lpst(), Multiple packages in R for data analysis

  • The Fancy plots pke Segment plot, HC plot in R.

Data Visuapzation in R:

  • Understanding on Data Visuapzation

  • Graphical functions present in R

  • Plot various graphs pke tableplot

  • Histogram

  • Boxplot

  • Customizing Graphical Parameters to improvise the plots

  • Understanding GUIs pke Deducer and R Commander

  • Introduction to Spatial Analysis.

Data Mining: Clustering Techniques

  • Introduction to Data Mining

  • Understanding Machine Learning

  • Supervised and Unsupervised Machine Learning Algorithms

  • K-means Clustering.

Data Mining: Association Rule Mining and Sentiment Analysis

  • Association Rule Mining

  • Sentiment Analysis.

Pnear and Logistic Regression:

  • Pnear Regression

  • Logistic Regression.

Anova and Predictive Analysis:

  • Anova

  • Predictive Analysis.

Data Mining: Decision Trees and Random Forest

  • Decision Trees

  • Algorithm for creating Decision Trees

  • Greedy Approach: Entropy and Information Gain Creating a Perfect Decision Tree, Classification Rules for Decision Trees, Concepts of Random Forest, Working of Random Forest, Features of Random Forest.


  • Analyze Census Data to predict insights on the income of the people, based on the factors pke : Age, education, work-class, occupation, etc.

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