Description

The Data Science with R Certification course enables you to take your data science skills into a variety of companies, helping them analyze data and make more informed business decisions. The course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about various data structures in R, various statistical concepts, cluster analysis, Regression and classification. In this curriculum we will have in-depth mathematical understanding of the algorithms from the basics. 

Course Objectives

  • Install R, Rstudio, and learn about the various R packages

  • Gain an in-depth understanding of data structure used in R and learn to import/export data in R

  • Define, understand and use the various functions in R

  • Learn to do data visualization using ggplot2 packages

  • Gain a basic understanding of various statistical concepts

  • Understand and use the hypothesis testing method to drive business decisions

  • Understand and use linear and non-linear regression models, and classification techniques for data analysis

  • Learn and use the various association rules with the Apriori algorithm

  • Learn and use clustering methods including k-means, DBSCAN, and hierarchical clustering

    • Target Audience

    This course is meant for all those students and professionals who are interested in using the R’s powerful ecosystem

    Basic Understanding

    There are no prerequisites

    Course Content

    No sessions available.

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    Simpliv LLC
    39658 Mission Boulevard,
    Fremont, CA 94539, USA

    3 Days Live Virtual Training on Data Science with R Certification

    Session 1: Introduction to R

    1. Introduction to R

      Various datatypes in R

      Vectors

      Matrices

      Data Frames

    2. Core programming concepts

      While Loops

      For Loops

      If Else statements

    3. Visualizations in R
    4. Packages in R

      ggplot

      dfply

      e1071

    5. Matrix operations
    6. Dataframes

      Joins and manipulations in Dataframes

    Session 2: Machine Learning

    1. Data Pre-processing

      Missing Data

      Categorical Data

      Feature Scaling

      Data Split (Test and Training Set)

    2. Regression

      Simple Linear Regression

      Multiple Linear Regression

    3. Classification

      Logistic Regression

      K Nearest Neighbours (K-NN)

      Support Vector Machine (SVM)

      Navie Bayes

      Decision Tree Classification

      Random Forest Classification

      XGBoost

      Regularization in Logistic Regression

      Understanding different hyper parameters

      Accuracy measures 

    4. Clustering

      K Means

      Hierarchical Clustering

      DBScan

    5. Association Rule Mining

      Apriori 

    6. Model selection and Boosting

      K Fold Technique

      Grid Search

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