Business Analytics with R Online Training
R is the flexible programming language for statistical computing and data environment which comprises of customizable graphics and animations. Commercial packages can be created to adequate intellectual protection for developers. R is a powerful programming in which data can be read and stored as a list, matrix or data. frame. It consists of interfaces from analytical software and data-visualization packages. Business analytics with R online training teach the insight into analytics and acquire skills in methodologies and techniques.
Unit 1: Introduction to Business Analytics Topics - 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.
Unit 2: Introduction to R Programming Topics - 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. Unit 3: Data Manipulation in R Topics - 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.
Unit 4: Data Import Techniques in R Topics - 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. Unit 5: Exploratory Data Analysis Topics - Understanding the Exploratory Data Analysis(EDA), Implementation of EDA on various datasets, Boxplots, Understanding the cor() in R, EDA functions like summarize(), llist(), Multiple packages in R for data analysis, The Fancy plots like Segment plot, HC plot in R. Unit 6: Data Visualization in R Topics - Understanding on Data Visualization, Graphical functions present in R, Plot various graphs like tableplot, histogram, boxplot, Customizing Graphical Parameters to improvise the plots, Understanding GUIs like Deducer and R Commander, Introduction to Spatial Analysis.
Unit 7: Data Mining: Clustering Techniques Topics - Introduction to Data Mining, Understanding Machine Learning, Supervised and Unsupervised Machine Learning Algorithms, K-means Clustering. Unit 8: Data Mining: Association Rule Mining and Sentiment Analysis Topics - Association Rule Mining, Sentiment Analysis. Unit 9: Linear and Logistic Regression Topics - Linear Regression, Logistic Regression.
Unit 10: Anova and Predictive Analysis Topics - Anova, Predictive Analysis.
Unit 11: Data Mining: Decision Trees and Random Forest Topics - 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. Unit 12: Project Topics - Analyze 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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