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Business Analytics with R Training Curriculum
Introduction To Business Analytics
Understand Business Analytics and R, Knowledge on the R language 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.
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 Sub-setting methods in R, Summarize data by using functions like: str(), class(), length(), nrow(), ncol() in R, Use of functions like head(), tail() for inspecting data, Indulge in a class activity to summarize data in R
Data Manipulation In R
The various steps involved in R for Data Cleaning, Functions used in R for Data Inspection, Tackling the problems faced during Data Cleaning, Uses of the functions like grep(),sub(), Coerce the data in R, 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 like sas7bdat and spss into R, Packages installation used for database import, Connect to RDBMS from R using ODBC and basic SQL queries in R, Basics of Web Scraping in R
R Exploratory Data Analysis
Understanding the Exploratory Data Analysis(EDA), Implementation of EDA on various datasets in R, Box plots, Understanding the cor() in R, EDA functions like summarize(), llist(), Multiple packages in R for data analysis, The Fancy plots like Segment plot and HC plot in R.
Data Visualization In R
Understanding on Data Visualization, Graphical functions present in R, Plot various graphs in R like table plot,histogram,box plot, Customizing Graphical Parameters to improvise the plots, Understanding GUIs like Deducer and R Commander, Introduction to Spatial Analysis in R
Data Mining: Clustering Techniques
Introduction to Data Mining in R, Understanding Machine Learning, Supervised and, Unsupervised Machine Learning Algorithms in R, K-means Clustering.
Data Mining: Association Rule Mining And Sentiment Analysis
Association Rule Mining in R, Sentiment Analysis.
Linear And Logistic Regression In R
Linear Regression in R, Logistic Regression in R
Anova And Predictive Analysis In R
Anova, Predictive Analysis.
R Data Mining: Decision Trees And Random Forest
Decision Trees in R, Algorithm for creating Decision Trees, Greedy Approach: Entropy and Information Gain, Creating a Perfect Decision Tree in R, R Classification Rules for Decision Trees, Concepts of Random Forest in R, Working of Random Forest in R, Features of Random Forest in R
Analyse Census Data, To predict insights on the income of the people, Based on the factors like : Age, education, work-class, occupation, etc.
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