Integrated program in Business Analytics

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Why this course ?

  • Business Analytics helps to explore data to find new patterns and relationships.
  • Business Analytics completes statistical analysis and quantitative analysis to explain why certain results occur.
  • Business Analytics helps to take Data driven decisions.
  • Business Analytics makes use of predictive modeling and predictive analytics to forecast future results

Integrated program in Business Analytics

Program Duration
and Fees

Duration

N/A

Price

N/A

Features

  • Benefits of Joining the training
    Join the hands-on based classroom or online training
    Join the hands-on based classroom or online training
    Led by an expert instructor who can look into the individual problems of the students

Description

An Overview of the SAS System
Getting Started With the SAS System
Getting familiar with SAS Data Sets
Producing List Report
Enhancing Output
Creating SAS Data Sets
DATA Step Programming
Combining SAS Data Sets
Producing Summary Reports
Introduction to Graphics
Introduction to Data Step Manipulation
Controlling Input and Output
Creating an Accumulating Variable
Reading & Writing Different Types of Data
Data Transformations
Debugging Techniques
Processing Data Iteratively
Combining SAS Data sets
Learning More
Introduction to SQL Procedure
Retrieving Data From a Single Table
Retrieving Data from Multiple Tables
Creating and Updating Tables and Views
Programming with the SQL Procedure
Practical Problem-Solving with PROC SQL
Types of Analytics
Properties of Measurements
Scales of Measurement
Types of Data
Measures of Central Tendency
Measures of Dispersion
Presentation of Data
Skewness and Kurtosis
Three Approaches towards Probability
Concept of a Random Variable
Probability Mass Function
Probability Density Function
Expectation of a Random Variable
Probability Distributions
Theory of Sampling
Concept of Estimation
Different types of Estimation
Concept of Hypothesis
Null Hypothesis
Alternative Hypothesis
Type-I error
Type-II error
Level of Significance
Confidence of Interval
Parametric Tests and Non Parametric Tests
One Sample T test
Two independent sample T test
Paired Sample T test
Chi Square Test for Independence of Attributes
Theory of Sampling
Concept of Estimation
Different types of Estimation
One Way Anova
Two Way Anova
Principal Component Analysis
Estimating the Initial Communalities
Eigen Values and Eigen Vectors
Correlation Matrix check and KMO-MSA check
Factor loading Matrix
Diagrammatic Representation of Factors
Problems of Factor Loadings and Solutions
Concept of Regression and features of linear line.
Assumptions of Classical Linear Regression Model
Method of Least Squares
Understanding the Goodness of fit
Test of Significance of the Estimated Parameters
Multiple Linear Regression and their Assumptions
Concept of Multicollinearity
Signs of Multi-collinearity
The Idea of Autocorrelation
Types of Clusters
Metric and Linkage
Ward’s Minimum Variance Criteria
Semi-Partial R-Square and R-Square
Diagrammatic Representation of clusters
Problems of Cluster Analysis
Concept and Applications of Logistic Regression
Principles Behind Logistic Regression
Comparison between Linear Probability Model and Logistic Regression
Mathematical Concepts Related to Logistic Regression
Concordant Pairs, Discordant Pairs and Tied Pairs
Classification Table
Graphical Representation Related to Logistic Regression
Concept of Time Series and its Applications
Assumptions of Time Series Analysis
Components of Time Series
Smoothening Techniques
ARIMA Modelling
Box-Jenkins Technology
Introduction to R
Basic Operations in R
Different Data Types and Data Structures In R
Subsetting In R
Additional Topics on Data Structures
Importing Data Sets in R
R Loops and Special Functions
Calculation of Commission and Simple Interest
Plots and Charts in R
Merging and Sorting Functions in R
Summarising Data
Calculations of the Measures of Central Tendency and Measures of Dispersion or Variability
Types of Analytics
Properties of Measurements
Scales of Measurement
Types of Data
Measures of Central Tendency
Measures of Dispersion
Presentation of Data
Skewness and Kurtosis
Principal Component Analysis
Estimating the Initial Communalities
Eigen Values and Eigen Vectors
Correlation Matrix check and KMO-MSA check
Factor loading Matrix
Diagrammatic Representation of Factors
Problems of Factor Loadings and Solutions
Three Approaches towards Probability
Concept of a Random Variable
Probability Mass Function
Probability Density Function
Expectation of a Random Variable
Probability Distributions
Theory of Sampling
Concept of Estimation
Different types of Estimation
One Way Anova
Two Way Anova
Concept of Hypothesis
Null Hypothesis
Alternative Hypothesis
Type-I error
Type-II error
Level of Significance
Confidence of Interval
Parametric Tests and Non Parametric Tests
One Sample T test
Two independent sample T test
Paired Sample T test
Chi Square Test for Independence of Attributes
Concept of Time Series and its Applications
Assumptions of Time Series Analysis
Components of Time Series
Smoothening Techniques
ARIMA Modelling
Box-Jenkins Technology
Support
Lift
Confidence
Decision Tree
MANOVA
Working with Formulae and Functions
Conditional Formatting in Excel
Data Sorting and Filtering, Excel Advanced Filter Options
Pivot Tables
VBA(Visual Basic Application)
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Course Name: Integrated program in Business Analytics