DATA ANALYTICS R Online Training

 >>  DATA ANALYTICS R Online Training

DATA ANALYTICS R Online Training


 (4.9) | 1500 Ratings


Introduction


DATA ANALYTICS R Online Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 30 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Data Analytics R Online Training Details

Module 1: Overview Of Business Analytics

Topics: Understand Business Analytics and R, Understand the Use of ‘R’ In The Itndustry, Correlate R With Other Software in Analytics, Knowledge on the R Language Ecosystem and Community, understand the use of IDE R Studio Perform Basic Activities in R Using Command Line, Learn Various GUI, use the ‘R Help’ Feature in R, Understanding about the Worldwide R Community Collaboration, Install R and the Entities useful for the Course

Module 2: Introduction To R Programming

Topics: Learn and Implement the Various Data Types in R, Appropriate use of Built in Function, Knowledge on the Various Subsetting Methods, use of Summarizing Data Functions, use of Inspecting Function, Indulge in a Class Activity to Summarize Data

Module 3: Data Manipulation In R

Topics: Operations used in Data Inspection, Coerce the Data, uses of The Apply Functions, Procedure for Data Cleaning, use of Manipulation Functions

Module 4: Data Import Techniques In R

Topics: Understand the Versatility or Robustness of R, Import Data From Various Statistical Formats, Package Installation for Database Import, Basics of Web Scraping, Connect to RDBMS from R Using Basic SQL Queries and ODBC in R, Import Data from Text Files or Spreadsheets into R

Module 5: Exploratory Data Analysis

Topics: Understanding the Exploratory Data Analysis, Boxplots, EDA Functions Like to Summarize(), use Of Multiple Packages in R for Data Analysis, HC Plot in R, Implementation of EDA on Different Datasets, Understanding the Cor Function in R, Llist Function, use of Fancy Plots

Module 6: Data Visualization In R

Topics: Learning Data Visualization, Plots Various Graphs Like Table Plot, Box Plot, Understanding Guis, Understanding Graphical Functions Present In R, Histogram, Improving Plots By Using Graphical Parameters, Introduction To Spatial Analysis

Module 7: Data Mining

Topics: Understanding Machine Learning, Introduction to Data Mining, K-Means Clustering, Various Machine Learning Algorithms, Sentiment Analysis, Association Rule Mining, Logistic and Linear Regression

Module 8: Anova And Predictive Analysis

Topics: Introduction to Anova, Predictive Analysis

Module 9: Data Mining: Random Forest And Decision Trees

Topics: Information and Entropy Gain, Classification Rules for Decision Trees, Creating Decision Trees Using Algorithm, Building a Perfect Decision Tree, Features and Operation of Random Forest, Concepts of Random Forest

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Course Curriculum

Data Analytics R Online Training Details

Module 1: Overview Of Business Analytics

Topics: Understand Business Analytics and R, Understand the Use of ‘R’ In The Itndustry, Correlate R With Other Software in Analytics, Knowledge on the R Language Ecosystem and Community, understand the use of IDE R Studio Perform Basic Activities in R Using Command Line, Learn Various GUI, use the ‘R Help’ Feature in R, Understanding about the Worldwide R Community Collaboration, Install R and the Entities useful for the Course

Module 2: Introduction To R Programming

Topics: Learn and Implement the Various Data Types in R, Appropriate use of Built in Function, Knowledge on the Various Subsetting Methods, use of Summarizing Data Functions, use of Inspecting Function, Indulge in a Class Activity to Summarize Data

Module 3: Data Manipulation In R

Topics: Operations used in Data Inspection, Coerce the Data, uses of The Apply Functions, Procedure for Data Cleaning, use of Manipulation Functions

Module 4: Data Import Techniques In R

Topics: Understand the Versatility or Robustness of R, Import Data From Various Statistical Formats, Package Installation for Database Import, Basics of Web Scraping, Connect to RDBMS from R Using Basic SQL Queries and ODBC in R, Import Data from Text Files or Spreadsheets into R

Module 5: Exploratory Data Analysis

Topics: Understanding the Exploratory Data Analysis, Boxplots, EDA Functions Like to Summarize(), use Of Multiple Packages in R for Data Analysis, HC Plot in R, Implementation of EDA on Different Datasets, Understanding the Cor Function in R, Llist Function, use of Fancy Plots

Module 6: Data Visualization In R

Topics: Learning Data Visualization, Plots Various Graphs Like Table Plot, Box Plot, Understanding Guis, Understanding Graphical Functions Present In R, Histogram, Improving Plots By Using Graphical Parameters, Introduction To Spatial Analysis

Module 7: Data Mining

Topics: Understanding Machine Learning, Introduction to Data Mining, K-Means Clustering, Various Machine Learning Algorithms, Sentiment Analysis, Association Rule Mining, Logistic and Linear Regression

Module 8: Anova And Predictive Analysis

Topics: Introduction to Anova, Predictive Analysis

Module 9: Data Mining: Random Forest And Decision Trees

Topics: Information and Entropy Gain, Classification Rules for Decision Trees, Creating Decision Trees Using Algorithm, Building a Perfect Decision Tree, Features and Operation of Random Forest, Concepts of Random Forest

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