R Programming

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R Programming

    Module 1: Introduction to Data Analytics
      Objectives:
      • This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
      • You can also learn how R can play an important role in solving complex analytical problems.
      • This module tells you what is R and how it is used by the giants like Google, Facebook etc.
      • Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

      Topics

      • Business Analytics, Data, Information
      • Understanding Business Analytics and R
      • Compare R with other software in analytics
      • Install R
      • Perform basic operations in R using command line
      • Learn the use of IDE R Studio
      • Use the ‘R help’ feature in R

      Module 2: Introduction to R programming

        Objectives:

        • This module starts from the basics of R programming like datatypes and functions.
        • In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
        • You will also learn how to apply the ‘join’ function in SQL.

        Topics

        • Variables in R
        • Scalars
        • Vectors
        • Matrices
        • List
        • Data frames
        • Using c, Cbind, Rbind, attach and detach functions in R
        • Factors

        Module 3: Data Manipulation in R

          Objectives:

          • In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
          • Thus using and exploring the popular functions required to clean data in R.

          Topics

          • Data sorting
          • Find and remove duplicates record
          • Cleaning data
          • Recoding data
          • Merging data
          • Slicing of Data
          • Merging Data
          • Apply functions

          Module 4: Data Import techniques in R

            Objectives:

            • This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
            • This module teaches you various data importing techniques in R.

            Topics

            • Reading Data
            • Writing Data
            • Basic SQL queries in R
            • Web Scraping

            Module 5: Exploratory Data Analysis

              Objectives:

              • In this module, you will learn that exploratory data analysis is an important step in the analysis.
              • EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

              Topics

              • Box plot
              • Histogram
              • Pareto charts
              • Pie graph
              • Line chart
              • Scatterplot
              • Developing graphs

              Module 6: Overview of Machine Learning techniques

                Objectives:

                • This module touches the base Statistics, Machine learning techniques used in the Industry and will cover case studies.

                Topics

                • Standard deviation
                • Outlier
                • Linear regression
                • Multiple regression
                • Logistic regressions
                • Correlation

                Module 7: Project Work

                • 1 Real-time project