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Data Scientist Certification(R, SAS & Excel) Training is a comprehensive package for aspiring analytics professionals to gain expertise in SAS software and essential statistical techniques to decode extensive data. Participants will be competent in data analytics methods like reporting, clustering, predictive modeling, and optimization to manage voluminous data.

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By the end of this training you will be able to:

Master the concepts of statistical analysis like linear & logistic regression, cluster analysis and forecasting
Get certification and be proficient in using R,SAS and Excel to model data and predict solutions to business problems
Gain fundamental knowledge on Analytics and how it assists in data-driven decision-making
Visualize and optimize data effectively using the built-in tools in R , SAS and Excel
Master SAS codes on SAS platform and work with ease on R language
Work on real-life industry based projects using R,SAS, Excel


Course Contents

Day 1

Introduction to Business Analytics

Need of Business Analytics Preview
Business Decisions
Introduction to Business Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Supply Chain Analytics
Health Care Analytics
Marketing Analytics
Human Resource Analytics
Web Analytics
Application of Business Analytics - Case Study
Business Intelligence (BI)
Data Science
Big Data
Analytical Tools 

Introduction to R

Comprehensive R Archive Network (CRAN)
Installing R on Various Operating Systems
Installing R on Windows from CRAN Website
Install R
IDEs for R
Installing RStudio on Various Operating Systems
Install RStudio 
Steps in R Initiation 
Benefits of R Workspace 
Setting the Workplace 
Functions and Help in R 
Access the Help Document
R Packages
Installing an R Package 
Install and Load a Package 

Day 2

R Programming

Operators in R
Running an R Script
Running a Batch Script
R Functions
Use Commonly Used Functions 

R Data Structure

Types of Data Structures in R
Vectors
Scalars
Colon Operator
Matrices
Arrays
Elements of Data Frames
Factors
Lists
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 from R 

Apply Functions 

Types of Apply Functions
Apply() Function
Lapply() Function
Sapply() Function
Tapply() Function
Vapply() Function
Mapply() Function
Dplyr Package - An Overview
Installing the Dplyr Package
Functions of the Dplyr Package
Use Arrange Function
Functions of Dplyr Package - Mutate()
Functions of Dply Package - Summarise()

Day 3

Data Visualization

Graphics in R
Create a Stacked Bar Plot and Grouped Bar Plot
Pie Charts
Histograms
Creating a Histogram
Kernel Density Plots
Create Histograms and a Density Plot
Line Charts
Box Plots
Create Line Graphs and a Box Plot
Heat Maps
Word Clouds
File Formats for Graphic Outputs
Save Graphics to a File
Exporting Graphs in RStudio
Exporting Graphs as PDFs in RStudio
Save Graphics Using RStudio 

Introduction to Statistics

Qualitative vs. Quantitative Analysis
Types of Measurements in Order
Nominal Measurement
Ordinal Measurement
Interval Measurement
Ratio Measurement
Statistical Investigation
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
Dist() Function in R
Perform the Distance Matrix Computations

Day 4

Hypothesis Testing I

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
Upper Tail Test
Test Statistic
Factors Affecting Test Statistic
Critical Value Using Normal Probability Table

Hypothesis Testing II

Parametric Tests
Z-Test
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
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
One-Way ANOVA Test - Case Study
54 Perform ANOVA

Day 5

Regression Analysis

Introduction to Regression Analysis
Use of Regression Analysis - Examples
Simple Regression Analysis
Multiple Regression Models
Simple Linear Regression Model
Simple Linear Regression Model Explained
Perform Simple Linear Regression
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

Classification vs. Prediction
Classification System
Classification Process
Classification Process - Model Construction
Classification Process - Model Usage in Prediction
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
Naive Bayes 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

Day 6

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
Hierarchical Clustering - Case Study
Perform Hierarchical Clustering
DBSCAN Clustering
Concepts of DBSCAN
DBSCAN Clustering Algorithm
DBSCAN in R
DBSCAN Clustering - Case Study 

Association

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
Apriori Algorithm - Example
Applying Aprior Algorithm
Step 1 - Mine All Frequent Item Sets
Algorithm to Find Frequent Item Set
Finding Frequent Item Set - Example
Ordering Items
Candidate Generation
Candidate Generation - Example
Step 2 - Generate Rules from Frequent Item Sets
Generate Rules from Frequent Item Sets - Example
Perform Association Using the Apriori Algorithm
Perform Visualization on Associated Rules
Problems with Association Mining

Day 7

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 Series Analysis

Enroll

 
 
 
 
 



Training Hours

Time: 12:00 NOON GMT | 07:00AM EST | 4:00AM PST | 6:00AM CST | 5:00AM MST | 5:30PM IST  | 01:00PM GMT+1

Audience

 

Software professionals looking for a career switch in the field of analytics
Professionals working in field of Data and Business Analytics
Graduates looking to build a career in Analytics and Data Science
Anyone with a genuine interest in the field of Data Science


 Predictive Modeling Techniques - Using SAS and Excel

Predictive Modelling Techniques

Linear Regression

Logistic Regression

Cluster Analysis

 Time Series Analysis


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