Predictive Analytics on SAS

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SAS (Statistical Analysis System) introduced the SAS Business Intelligence & Analytics Solution for large enterprises to explore their large datasets in a visually appealing format. SAS analytics is a data analytics tool that is used increasingly in Data Science, Machine learning, and Business Intelligence applications. Not only it equips the organization with all necessary tools to monitor the key BI metrics but also produces powerful analytics and comprehensive reports for its decision makers to take well-informed decisions.

Main Features

  • Apply Ordinary Least Squares method to Create Linear Regressions
  • Use Training and Test data to build robust models
  • Assess the R-Squared and Adjusted R-Squared for all types of models
  • Create a Multiple Linear Regression Model (MLRM)
  • Derive new independent variables for modelling purposes
  • Understand the intuition of multicollinearity
  • Interpret coefficients of an MLRM to gain business insights
  • Intuitively understand a Logistic Regression
  • Understand the Odds Ratio and Dummy Variables
  • Read a Confusion Matrix
  • Operate with False Positives and False Negatives and know the difference
  • Transform independent variables for modelling purposes
  • Check for multicollinearity using VIF and the correlation matrix
  • Understand how to apply the Chi-Squared statistical test

Introduction to Analytics

1
Introduction to Excel
2
Conditional Formatting
3
Data Summarization techniques
4
Graphical summary using SAS/GRAPH: Introduction to Bar graph
5
Graphical summary using SAS/GRAPH: Introduction to Pie graph
6
Graphical summary using SAS/GRAPH introduction to Histogram, Box plots, Scatter diagram
7
Descriptive Statistics-Introduction to various measures of Central Tendency
8
Introduction to the measures of Dispersion, Range, Mean Deviation , Standard Deviation

Understanding Probability and Probability Distribution

1
Introduction to Probability theory
2
Types of probability distribution – Discrete Distribution and Continuous distribution
3
Understanding Probability Mass Function and Probability Density Function
4
Normal Distribution and Standard Normal Distribution
5
Normal plot using Proc GPLOT procedure in SAS
6
Application of Normal distribution in Analytics with real life examples
7
Binomial Distribution and Binomial plot using PROC GPLOT procedure in SAS
8
Poisson distribution and Poisson plot using Proc GPLOT procedure in SAS
9
Application of Binomial and Poisson distribution in Analytics with real life examples

Introduction to Sampling Theory and Estimation

1
Concept of Population and Sample
2
Use of PROC SURVEYSELECT procedure in SAS
3
Introduction to Some important terminologies
4
Parameter and Statistic
5
Properties of a good estimator
6
Standard Deviation and Standard Error
7
Point and Interval Estimation
8
Confidence level and level of Significance
9
Constructing Confidence Intervals
10
Formulation of Null and Alternative hypothesis
11
Performing simple test of Hypothesis

Statistical Significance of T-Tests Chi Square Tests and Analysis of Variance

1
Performing test of one sample mean using Proc ttest
2
Difference between two group means (independent sample) using Proc ttest
3
difference between two group means (Paired sample) using Proc ttest
4
Performing Chi-square tests: Test of Independence
5
Performing one-way ANOVA with PROC ANOVA and PROC GLM procedure
6
Performing post-hoc multiple comparisons tests in PROC
7
GLM using Tukey’s mean test

Introduction to Segmentation Techniques: Factor Analysis

1
Introduction to Factor Analysis and various techniques
2
Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA)
3
Application of Factor Analysis using Proc Factor procedure
4
KMO MSA test, Bartlett’s Test Sphericity
5
The Mineigen Criterion, Scree plot
6
Introduction to Factor Loading Matrix
7
Various rotation techniques like Varimax

Introduction to Segmentation Techniques: Cluster Analysis

1
Introduction to Cluster Analysis and various techniques
2
Hierarchical and Non – Hierarchical Clustering techniques
3
Using Hierarchical Clustering by Proc Tree procedure in SAS
4
Performing K – means Clustering in SAS
5
Divisive Clustering, Agglomerative Clustering
6
Application of Cluster Analysis in Analytics with profiling of the clusters and interpretation of the clusters

Correlation and Linear Regression

1
Introduction to Pearson’s Correlation coefficient using PROC CORR procedure
2
Correlation and Causation – Fitting a simple linear regression model with the Proc REG procedure
3
Understanding the concepts of Multiple Regression
4
Using automated model selection techniques in PROC REG to choose the best model
5
Interpretation of the model: overall fit of the model and finding out the influential variables
6
Linear Regression diagnostics
7
Examining Residual
8
Assessing Collinearity, Heteroskedasticity and Auto – Correlation

Introduction to Categorical Data Analysis and Logistic Regression

1
Comparison between Liner Regression and Logistic Regression
2
Performing Logistic regression using Proc Logistic Procedure in SAS
3
Performing Goodness of ft of the model
4
Introduction to Percent Concordant, AIC, SC, and Hosmer – Lemeshow
5
Receiver Operating Characteristics (ROC) Curve and Area under Curve (AUC)
6
Interpretation of the model: overall fit of the model and finding out the influential variables using Odds ratio criteria
7
Using automated model selection techniques in PROC Logistic to choose the best model using AIC criteria

Introduction to Time Series Analysis

1
What is Time series Analysis, Objectives and Assumptions of Time Series
2
Identifying pattern in Time series data: Decomposition of the time series data and general aspect of the analysis
3
Introduction to Various Smoothing techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing, Holt’s Linear Exponential Smoothing
4
Examples of Seasonality and detecting Seasonality in Time series data
5
Introduction to Proc Forecast to generate forecast for time series data
6
Autoregressive models and Stepwise Autoregression (STEPAR) procedure
7
Autoregressive and Moving Average models and Introduction to Box Jenkins Methodology
8
Introduction to Autoregressive Moving Average (ARMA) model
9
Autoregressive Integrated Moving Average (ARIMA) model
10
Building an ARIMA Model
11
Detection of Stationarity, Seasonality in ARIMA Model
12
Detecting the order of AR and MA of ARIMA model by Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)
13
Detecting the order by using AIC and BIC criterion
14
Estimation and forecast using Proc ARIMA in SAS
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Enrolled: 34 students
Duration: 96 hours
Lectures: 77
Video: 9 hours
Level: Advanced

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Working hours

Monday 9:30 am - 7.00 pm
Tuesday 9:30 am - 7.00 pm
Wednesday 9:30 am - 7.00 pm
Thursday Closed
Friday 9:30 am - 7.00 pm
Saturday 9:30 am - 7.00 pm
Sunday 9:30 am - 7.00 pm
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