Data Science with R Syllabus

    Module 1: Introduction to Data Science (Duration-1hr)

    • What is Data Science?
    • What is Machine Learning?
    • What is Deep Learning?
    • What is AI?
    • Data Analytics & it’s types
    • Module 2: Introduction to R (Duration-1hr)

      • What is R?
      • Why R?
      • Installing R
      • R environment
      • How to get help in R
      • R Studio Overview
      • Module 3: R Basics (Duration-5hrs)

        • Environment setup
        • Data Types
        • Variables Vectors
        • Lists
        • Matrix
        • Array
        • Factors
        • Data Frames
        • Loops
        • Packages
        • Functions
        • In-Built Data sets
        • Module 4: R Packages (Duration-2hrs)

          • DMwR
          • Dplyr/plyr
          • Caret
          • Lubridate
          • E1071
          • Cluster/fpc
          • Data.table
          • Stats/utils
          • Ggplot/ggplot2
          • Glmnet
          • Module 5: Importing Data (Duration-1hr)

            • Reading CSV files
            • Saving in Python data
            • Loading Python data objects
            • Writing data to csv file
            • Module 6: Manipulating Data (Duration-1hr)

              • Selecting rows/observations
              • Rounding Number
              • Selecting columns/fields
              • Merging data
              • Data aggregation
              • Data munging techniques
              • Module 7: Statistics Basics (Duration-11hrs)

                  Central Tendency

                • Mean
                • Median
                • Mode
                • Skewness
                • Normal Distribution
                • Probability Basics

                • What does mean by probability?
                • Types of Probability
                • ODDS Ratio?
                • Standard Deviation

                • Data deviation & distribution
                • Variance
                • Bias variance Trade off

                • Underfitting
                • Overfitting
                • Distance metrics

                • Euclidean Distance
                • Manhattan Distance
                • Outlier analysis

                • What is an Outlier?
                • Inter Quartile Range
                • Box & whisker plot
                • Upper Whisker
                • Lower Whisker
                • Scatter plot
                • Cook’s Distance
                • Missing Value treatments

                • What is a NA?
                • Central Imputation
                • KNN imputation
                • Dummification
                • Correlation

                • Pearson correlation
                • Positive & Negative correlation
                • Module 8: Error Metrics (Duration-3hrs)

                  Classification

                • Confusion Matrix
                • Precision
                • Recall
                • Specificity
                • F1 Score
                • Regression

                • MSE
                • RMSE
                • MAPE
                • Module 9: Machine Learning

                    Module 10: Supervised Learning (Duration-6hrs)

                      Linear Regression

                    • Linear Equation
                    • Slope
                    • Intercept
                    • R square value
                    • Logistic regression

                    • ODDS ratio
                    • Probability of success
                    • Probability of failure
                    • ROC curve
                    • Bias Variance Tradeoff
                    • Module 11: Unsupervised Learning (Duration-4hrs)

                      • K-Means
                      • K-Means ++
                      • Hierarchical Clustering
                      • Module 12: Machine Learning using R (Duration-10hrs)

                        • Linear Regression
                        • Logistic Regression
                        • K-Means
                        • K-Means++
                        • Hierarchical Clustering – Agglomerative
                        • CART
                        • 5.0
                        • Random forest
                        • Naïve Bayes

                        Thanks for the best ever Training. I did my Android Course here. Trainer is very good in knowledge. They provides the best ever Support to me.

                        Kaviya