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
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