R Data Science Course, 100%MNC Job upto 50K, Free Python and Alteryx, SLA Institute, Delhi NCR

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

Starting and quitting R

Recording your work

• Basic features of R.
• Calculating with R
• Named storage
• Functions
• R is case-sensitive
• Listing the objects in the workspace
• Vectors
• Extracting elements from vectors
• Vector arithmetic
• Simple patterned vectors
• Missing values and other special values
• Character vectors Factors
• More on extracting elements from vectors
• Matrices and arrays
• Data frames
• Dates and times

NOTE:-Assignments with Datasets

Import and Export data in R

• Importing data in to R
• CSV File
• Excel File
• Import data from text table

Topics:

• Variables in R
• Scalars
• Vectors
• R Matrices
• List
• R – Data Frames
• Using c, Cbind, Rbind, attach and detach functions in R
• R – Factors
• R – CSV Files
• R – Excel File

NOTE:

• Assignments
• Business Scenerio/Group Discussion.

R Nuts and Bolts

• Entering Input. – Evaluation- R Objects- Numbers- Attributes- Creating Vectors- Mixing Objects-Explicit Coercion- Summary- Names- Data Frames.

• The dplyr Package
• Installing the dplyr package
• select()
• filter()
• arrange()
• rename()
• mutate()
• group_by()
• %>%

NOTE:

• Assignments
• Business Scenerio/Group Discussion.

• Looping on the Command Line
• lapply()
• sapply()
• tapply()
• apply()

NOTE:

• Assignments
• Business Scenerio/Group Discussion.

• 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
• Merging data

Statistical Plotting

• Bar charts and dot charts
• Pie charts
• Histograms
• Box plots
• Scatterplots
• QQ plots

Note:

• Assignments with Datasets

Objectives

• Control Structure Programming with R
• The for() loop
• The if() statement
• The while() loop
• The repeat loop, and the break and next statements
• Apply
• Sapply
• Lapply

Note:

• Assignments with Datasets

Factors

• Using Factors
• Manipulating Factors
• Numeric Factors
• Creating Factors from Continuous Variables
• Convert the variables in factors or in others.

Reshaping

• Data Modifying
• Data Frame Variables
• Recoding Variables
• The recode Function
• Reshaping Data Frames
• The reshape Package

Note:

• Assignments with Datasets

Explore many algorithms and models

• Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
• Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine!

• Machine Learning Languages, Types, and Examples
• Machine Learning vs Statistical Modelling
• Supervised vs Unsupervised Learning
• Supervised Learning Classification
• Unsupervised Learning

• K-Nearest Neighbors
• Decision Trees
• Random Forests
• Reliability of Random Forests
• Advantages and Disadvantages of Decision Trees

• Regression Algorithms
• Model Evaluation
• Model Evaluation: Overfitting and Underfitting
• Understanding Different Evaluation Mode

• K-Means Clustering plus Advantages and Disadvantages
• Hierarchical Clustering plus Advantages and Disadvantages
• Measuring the Distances Between Clusters – Single Linkage Clustering
• Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
• Density-Based Clustering

• Dimensionality Reduction: Feature Extraction and Selection
• Collaborative Filtering and Its Challenges

• An Overview of Classification.
• Why Not Linear Regression?
• Logistic Regression
• The Logistic Model
• Estimating the Regression Coefficients
• Making Predictions
• Logistic Regression for >2 Response Classes
• Lab: Logistic Regression.
• The Stock Market Data
• Logistic Regression

Note:

• Assignments with Different Datasets.
• Business Scenerio/Group Discussion.

• The Basics of Decision Trees
• Regression Trees
• Classification Trees
• Trees Versus Linear Models
• Advantages and Disadvantages of Trees
• Bagging, Random Forests, Boosting
• Bagging
• Random Forests
• Lab: Decision Trees
• Fitting Classification Trees
• Fitting Regression Trees

Note:

• Assignments with Different Datasets.
• Business Scenerio/Group Discussion.

• Time series
• Estimating and Eliminating the Deterministic Components if they are present in the Model.
• Estimating and Eliminating Seasonality if it is present in the Model
• Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
• Identify ‘order’ of the ARMA model
• Forecast’ or Predict for Future Values
• Practise on R

Note:

• Assignments with Different Datasets.
• Business Scenerio/Group Discussion.

• Understand when the Support Vector family of methods are an appropriate method of analysis.
• Understand what a hyperplane is and how they are used with the Support Vector methods.
• Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support
• Vector Machines.
• Know how each of the algorithms determines the best separating hyperplane.
• Distinguish between hard and soft margins and when each is to be used.
• Know how to extend the method for nonlinear cases.

Note:

• Assignments with Different Datasets.
• Business Scenerio/Group Discussion.

• Understand what principal components are and when principal component analysis is appropriate.
• Describe eigenvalues and eigenvectors and how they are used to calculate principal components.
• Understand loading and loading vectors.
• Know how to decide how many principal components to use in the analysis.
• Be able to use principal component analysis for regression.

Note:

• Assignments with Different Datasets.
• Business Scenerio/Group Discussion.