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    Business Analytics with R Training

    BY
    Mindmajix Technologies

    Familiarize concepts of business analytics with R programming through the online training course by Mindmajix.

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    Mode

    Online

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based
    Frequency of Classes Weekdays, Weekends

    Course overview

    Business Analytics with R Training Course is a training programme on Business Analytics using R that is developed and offered by Mindmajix, the online educational platform that provides various training courses. Through this online training, the students will understand deeply the aspects of the business analytics with R such as R for the analysis, Functions used in R for Data Inspection, Introduction to Spatial Analysis in R, Sub-setting methods in R, R Classification Rules for Decision Trees, advanced analytics, data analysis with R and many more.

     Students who plan to enroll in the Business Analytics with R Training Online Course can attend the training in two methods provided by Mindmajix, namely, the live mode through Google meet or Zoom with the support of the instructor or mode of self-paced videos. The learners will be able to explore the practical knowledge and experiences of the real-time application of Business Analytics using R Programming.  After the completion of Business Analytics with R Training certification, the participants will be given the opportunity to take the benefits of the premium features of Mindmajix including the Custom Resume Builder Access, Privileged Community Support, Mock Interviews, Life-Time Self-Paced Videos Access, etc.

    The highlights

    • Complete online course
    • Offered by Mindmajix
    • Flexible Schedule
    • Online Live and Self-paced Training Options
    • FREE Demo on Request
    • 24/7 Lifetime Support
    • Life-Time Self-Paced Videos Access
    • One-on-One Doubt Clearing
    • Certification Oriented Curriculum

    Program offerings

    • One-on-one doubt clearing sessions
    • Certification oriented curriculum
    • Real-time project use cases
    • 20 hours of labs
    • Free demo on request
    • 24/7 lifetime support
    • 30 hours of sessions
    • Online live and self-paced training options

    Course and certificate fees

    certificate availability

    Yes

    certificate providing authority

    Mindmajix Technologies

    What you will learn

    Business analytics knowledge R programming Knowledge of data visualization Knowledge of data mining

    By the end of the Business Analytics with R Training online certification, the learners will develop a deep understanding of the concepts of business analytics with R and will build the knowledge enough to clear Business Analytics with the R certification exam.

    The syllabus

    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
    • Package 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

    Project

    • 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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