Data Science using SAS & R Online Training Course Content


Introduction to Business Analytics

  • Introduction
  • Objectives
  • Need of Business Analytics
  • Business Decisions
  • Introduction to Business Analytics
  • Features ofBusiness Analytics
  • Types of Business Analytics
  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Supply Chain Analytics
  • Health Care Analytics
  • Marketing Analytics
  • Human Resource Analytics
  • Web Analytics
  • Business Decisions
  • Business Intelligence (BI)
  • Data Science
  • Importance of Data Science
  • Data Science as a Strategic Asset
  • Big Data
  • Analytical Tools

Introduction to R

  • Introduction
  • Objectives
  • An Introduction to R
  • Comprehensive R Archive Network (CRAN)
  • Cons of R
  • Companies Using R
  • Understanding R
  • Installing R on Various Operating Systems
  • Installing R on Windows from CRAN Website
  • Install R
  • Introduction to R
  • Introduction
  • Objectives
  • An Introduction to R
  • Comprehensive R Archive Network (CRAN)
  • Cons of R
  • Companies Using R
  • Understanding R
  • Installing R on Various Operating Systems
  • Installing R on Windows from CRAN Website
  • Install R

R Programming

  • Introduction
  • Objectives
  • Operators in R
  • Arithmetic Operators
  • Use Arithmetic Operations
  • Relational Operators
  • Use Relational Operators
  • Logical Operators
  • Use Logical Operators
  • Assignment Operators
  • Use Assignment Operator
  • Conditional Statements in R
  • If else() Function
  • Use Conditional Statements
  • Use Switch Function
  • Loops in R
  • Break Statement
  • Next Statement
  • Use Loops
  • Scan() Function
  • Running an R Script
  • Running a Batch Script
  • R Functions
  • Use Commonly Used Functions
  • Use Commonly-USed String Functions

R Data Structure

  • Introduction
  • Objectives
  • Types of Data Structures in R
  • Vectors
  • Create a Vector
  • Scalars
  • Colon Operator
  • Accessing Vector Elements
  • Matrices
  • Accessing Matrix Elements
  • Create a Matrix
  • Arrays
  • Accessing Array Elements
  • Create an Array
  • Data Frames
  • Elements of Data Frames
  • Create a Data Frame
  • Factors
  • Create a Factor
  • Lists
  • Create a List
  • Importing Files in R
  • Importing an Excel File
  • Importing a Minitab File
  • Importing a Table File
  • Importing a CSV File
  • Read Data from a File
  • Exporting Files fromR

Apply Functions

  • Introduction
  • Objectives
  • Types of Apply Functions
  • Apply() Function
  • Use Apply Function
  • Lapply() Function
  • Use Lapply Function
  • Sapply() Function
  • Use Sapply Function
  • Tapply() Function
  • Use Tapply Function
  • Vapply() Function
  • Use Vapply Function
  • Mapply() Function
  • Dplyr Package-An Overview
  • Dplyr Package-The Five Verbs
  • Installing the Dplyr Package
  • Functions of the Dplyr Package
  • Functions of the Dplyr Package-Select()
  • Use the Select Function
  • Functions of Dplyr-Package-Filter()
  • Use Select Function
  • Functions of Dplyr Package-Arrange()
  • Use Arrange Function
  • Functions of Dplyr Package-Mutate()
  • Functions of Dply Package-Summarise()
  • Use Summarise Function

Data Visualization

  • Introduction
  • Objectives
  • Graphics in R
  • Types of Graphics
  • Bar Charts
  • Creating Simple Bar Charts
  • Editing a Simple Bar Chart
  • Create a Bar Chart
  • Create a Stacked Bar Plot and Grouped Bar Plot
  • Pie Charts
  • Editing a Pie Chart
  • Create a Pie Chart
  • Histograms
  • Creating a Histogram
  • Kernel Density Plots
  • Creating a Kernel Density Plot
  • Create Histograms and a Density Plot
  • Line Charts
  • Creating a Line Chart
  • Box Plots
  • Creating a Box Plot
  • Create Line Graphs and a Box Plot
  • Heat Maps
  • Creating a Heat Map
  • Create a Heatmap
  • Word Clouds
  • Creating a Word Cloud
  • Create a Word Cloud
  • File Formats for Graphic Outputs
  • Saving a Graphic Output as a File
  • Save Graphics to a File
  • Exporting Graphs in RStudio
  • Exporting Graphs as PDFs in RStudio
  • Save Graphics Using RStudio

Introduction to Statistics

  • Introduction
  • Objectives
  • Basics of Statistics
  • Types of Data
  • Qualitative vs. Quantitative Analysis
  • Types of Measurements in Order
  • Nominal Measurement
  • Ordinal Measurement
  • Interval Measurement
  • Ratio Measurement
  • Statistical Investigation
  • Statistical Investigation Steps
  • Normal Distribution
  • Example of Normal Distribution
  • Importance of Normal Distribution in Statistics
  • Use of the Symmetry Property of Normal Distribution
  • Standard Normal Distribution
  • Use Probability Distribution Functions
  • Distance Measures
  • Distance Measures-A Comparison
  • Euclidean Distance
  • Example of Euclidean Distance
  • Manhattan Distance
  • Minkowski Distance
  • Mahalanobis Distance
  • Cosine Similarity
  • Correlation
  • Correlation Measures Explained
  • Pearson Product Moment Correlation (PPMC)
  • Dist() Function in R
  • Perform the Distance Matrix Computations

Hypothesis Testing

  • Introduction
  • Objectives
  • Hypothesis
  • Need of Hypothesis Testing in Businesses
  • Null Hypothesis
  • Alternate Hypothesis
  • Null vs. Alternate Hypothesis
  • Chances of Errors in Sampling
  • Types of Errors
  • Contingency Table
  • Decision Making
  • Critical Region
  • Level of Significance
  • Confidence Coefficient
  • Bita Risk
  • Power of Test
  • Factors Affecting the Power of Test
  • Types of Statistical Hypothesis Tests
  • An Example of Statistical Hypothesis Tests
  • Upper Tail Test
  • Test Statistic
  • Factors Affecting Test Statistic
  • Critical Value Using Normal ProbabilityTable

Hypothesis Testing II

  • Introduction
  • Objectives
  • Parametric Tests
  • Z-Test
  • T-Test
  • Use Normal and Student Probability Distribution Functions
  • Testing Null Hypothesis
  • Objectives of Null Hypothesis Test
  • Three Types of Hypothesis Tests
  • Hypothesis Tests About Population Means
  • Decision Rules
  • Hypothesis Tests About Population Proportions
  • Chi-Square Test
  • Steps ofChi-Square Test
  • Degree of Freedom
  • Chi-Square Test for Independence
  • Chi-Square Test for Goodness of Fit
  • Use Chi-Squared Test Statistics
  • Introduction to ANOVA Test
  • One-Way ANOVA Test
  • The F-Distribution and F-Ratio
  • F-Ratio Test
  • Perform ANOVA

Regression Analysis

  • Introduction
  • Objectives
  • Introduction to Regression Analysis
  • Types Regression Analysis
  • Simple Regression Analysis
  • Multiple Regression Models
  • Simple Linear Regression Model
  • Simple Linear Regression Model Explained
  • Perform SimpleLinear Regression
  • Correlation
  • Correlation Between X and Y
  • Find Correlation
  • Method of Least Squares Regression Model
  • Coefficient of Multiple Determination Regression Model
  • Standard Error of the Estimate Regression Model
  • Dummy Variable Regression Model
  • Interaction Regression Model
  • Non-Linear Regression
  • Non-Linear Regression Models
  • Perform Regression Analysis with Multiple Variables
  • Non-Linear Models to Linear Models
  • Algorithms for Complex Non-Linear Models

Classification

  • Introduction
  • Objectives
  • Introduction to Classification
  • Examples of Classification
  • Classification vs. Prediction
  • Classification System
  • Classification Process
  • Classification Process-Model Construction
  • Classification Process-Model Usage inPrediction
  • Issues Regarding Classification and Prediction
  • Data Preparation Issues
  • Evaluating Classification Methods Issues
  • Decision Tree
  • Decision Tree-Dataset
  • Classification Rules of Trees
  • Overfitting in Classification
  • Tips to Find the Final Tree Size
  • Basic Algorithm for a Decision Tree
  • Statistical Measure-Information Gain
  • Calculating Information Gain for Continuous-Value Attributes
  • Enhancing a Basic Tree
  • Decision Trees in Data Mining
  • Model a Decision Tree
  • NaiveBayes Classifier Model
  • Features of Naive Bayes Classifier Model
  • Bayesian Theorem
  • Naive Bayes Classifier
  • Applying Naive Bayes Classifier-Example
  • Naive Bayes Classifier-Advantages and Disadvantages
  • Perform Classification Using the Naive Bayes Method
  • Nearest Neighbor Classifiers
  • Computing Distance and Determining Class
  • Choosing the Value of K
  • Scaling Issues in Nearest Neighbor Classification
  • Support Vector Machines
  • Advantages of Support Vector Machines
  • Geometric Margin in SVMs
  • Linear SVMs
  • Non-Linear SVMs
  • Support a Vector Machine

Clustering

  • Introduction
  • Objectives
  • Introduction to Clustering
  • Clustering vs. Classification
  • Use Cases of Clustering
  • Clustering Models
  • K-means Clustering
  • K-means Clustering Algorithm
  • Pseudo Code of K-means
  • K-means Clustering Using R
  • Perform Clustering Using Kmeans
  • Hierarchical Clustering
  • Hierarchical Clustering Algorithms
  • Requirements of Hierarchical Clustering Algorithms
  • Agglomerative Clustering Process
  • Perform Hierarchical Clustering
  • DBSCAN Clustering
  • Concepts of DBSCAN
  • DBSCAN Clustering Algorithm
  • DBSCAN in R

Association

  • Introduction
  • Objectives
  • Association Rule Mining
  • Application Areas of Association Rule Mining
  • Parameters of Interesting Relationships
  • Association Rules
  • Association Rule Strength Measures
  • Limitations of Support and Confidence
  • Apriori Algorithm
  • Applying Aprior Algorithm
  • Step 1-Mine All Frequent Item Sets
  • Algorithm to Find Frequent Item Set
  • Ordering Items
  • Candidate Generation
  • Step 2-Generate Rules from Frequent Item Sets
  • Perform Association Using the Apriori Algorithm
  • Perform Visualization on Associated Rules
  • Problems with Association Mining

Basic Analytic Techniques-Using SAS and Excel

  • Basic Analytic Techniques-Using SAS
  • Data Exploration
  • Data Visualization
  • Diagnostic Analytics

Predictive Modeling Techniques-Using SAS and Excel

  • Predictive Modelling Techniques
  • Linear Regression
  • Logistic Regression
  • Cluster Analysis
  • Time SeriesAnalysis


We are providing Data Science using SAS & R Online Training in Ameerpet Hyderabad. We are one of best Institute to provide Best High Quality Data Science using SAS & R online training all over India. The IT Professionals and Students from India and abroad who are unable to attend regular classes can attend our Data Science using SAS & R online training from their home in their convenient timings. For more details on Data Science using SAS & R Online Training please call to 9290971883, / 9247461324, or drop a mail to revanthonlinetraining@gmail.com

Data Science using SAS & R online training institute address : B1, 3rd Floor, Eureka Court, Near Image Hospital, Ameerpet, Hyderabad, India


Enquiry Form

Security-Code reload security code

Other Related Courses

Chef Online Training in Hyderabad India

Chef Online Training in Hyderabad India

Read More
DevOps Online Training in Hyderabad India

DevOps Online Training in Hyderabad India

Read More
Jenkins Online Training in Hyderabad India

Jenkins Online Training in Hyderabad India

Read More
Puppet Online Training in Hyderabad India

Puppet Online Training in Hyderabad India

Read More