The Best Institute to Mastering SAS, Business Analytics, Predictive Modeling, Financial & Risk Modeling.
( Duration – 40 HRS )
Pass SASInstitute A00-240 Exam at Your First Attempt with our Practice Exam classes
This course also prepares the lerners for Global SAS Certification for Statistical Business Analyst Using SAS 9: Regression and Modeling Credential (A00-240), Mock Tests are also available at the end of the training.
SAS is the most recommended tool in the data industry. And what does SAS stands for?
It is Statistical Analysis system. Data crunching or data mining leads to analysis which is used in decision making. This has opened up a railroad to business analytics courses. In India there are many institutes which provide trainings in business analytics courses. Some go online others offline. But at the end of the course what do you get out of it is all that matters. You do a lot of research in finding the best analytics institute you get things delivered as you expected.
Most of the people make mistakes in understanding Analytics training as Predictive modeling while predictive modeling is just one module of analytics training. People who are passionate about data modeling, predictive modeling and business analytics, google a lot and do a lot of research in finding the best institute for a better delivery and some land up elsewhere losing the faith trust and passion indeed. Some training institutes do not have trained professionals others lack in ethics while some fail in infrastructure and then there are those who are proficient experienced well trained good mannered above all who take you through right direction building a stronger trust. We at Professional SAS tutors have a faculties who are trained, proficient and experienced in SAS, business analytics, predictive modeling, financial and risk modeling. We impart trainings with deep diving in data with an extra focus on the current industrial methodologies. We make sure that you do not lose the correct pace and keep you stay focused.
Our students make us proud by clearing the certification in respective area (SAS, business analytics, predictive modeling) where they look for their future or want to pursue something by their inner passion. The will to learn and a trained professional go hand in hand when it comes to mastering SAS, business analytics, predictive modeling, financial and risk modeling.
SAS Business analytics refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions
No one has the ability to capture and analyze data from the future. However, there is a way to predict the future using data from the past. It’s called predictive modeling, and these days organizations do it on a daily basis. To get a better understanding of predictive modeling let’s take an example, Customer Lifetime Value (CLTV) measure used by companies to predict the net profit attributed to the entire future relationship with a customer, it involves predictive modeling to determine how much a customer will buy from the company over time.
Do you have a “next best offer” or product recommendation capability? That’s an analytical prediction of the product or service that your customer is most likely to buy next. Have you ever made a forecast of next quarter’s sales? Used digital marketing models to determine what advertisements to place on what publisher’s site? All of these are forms of predictive modeling.
Predictive modeling is gaining popularity, no matter what designation you hold in your organization, whether you are a manager or an analyst, you really need to expertise in predictive modeling in order to interpret results and make better decisions. By understanding a few basics you will feel more comfortable working with and communicating with data scientists and all others in your organization about the results and recommendations from predictive modeling on other hand quantitative analysis is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
One of the reasons Business Analytics course has emerged as one of the most sought after training is that no academic background prepares you for a career in analytics. Almost all educational backgrounds are useful because they contribute towards what you need to know. So if you have a background in Mathematics, Statistics, Economics, Engineering, and MBA then you will find that what you know or what you have learnt as a part of formal education is useful but far from complete for a career in analytics. So no matter what your formal background is, you will need Business Analytics course / training to be able to learn other aspects of analytics that you were not taught as a part of your formal education system.
This Business Analytics course by Professional SAS tutors has been designed on the basis of inputs that we gathered from major employers in the analytics space. We went and asked them, what kind of skills they think are difficult to find and which skills would be in demand in future for which they would be paying high salaries. The hard part was to get the program developed by the people in analytics field and real time framework, and they are the ones who will train and prepare students to clear the certification in business analytics at Professional SAS Tutors. All the people coming from different educational backgrounds know the content but they do not know the application aspect of what they had been taught till now. We need to be taught the practical application in the analytics industry. You need Industry practitioners who have been doing this over years together to be able to teach those subjects because that really makes the difference between success and any other training program
Introduction to Statistics
Basic Statistical Concepts
Descriptive and Inferential Statistics, Populations and Samples, Parameters and Statistics, Use of variables dependent and Independent, Types of Variables Quantitative and Categorical, Scales of measurement Nominal ordinal interval ratio, Statistical Methods, Exploring your data
Describing your data, Measures of Location, Percentiles, Measures of Variability, Using descriptive statistics to answer data questions, Means procedure, Using Proc means to generate statistics
Picturing your data
Histogram, Normal Distribution, Assessing Normality, Measures of Shape Skewness, Measures of Shape Kurtosis, Normal probability plots, ,Box Plots, Comparing Distributions Summary, Assessing Normality with examples, Univariate Procedure, Statistical Graphics procedure in SAS, The SG Plot procedure in SAS, ODS Graphics output, Using SAS to picture your data
Point Estimators,Variability and Standard Error, Distribution of Sample Means, Interval Estimators, Confidence Intervals, Normality and Central Limit Theorem, Calculating confidence interval for the Mean, Using Proc Means to generate confidence interval, Calculating 95 percent confidence interval
Decision Making Process, Steps in hypothesis testing, Types of Errors and Power, The P value Effect Size Sample Size, Statistical Hypothesis Test, The T Statistic and the T Distribution, Comparing Sample and Hypothesized Means, Using Proc univariate to generate statistics, Using Proc Univariate to perform a hypothesis test
Analysis of Variance – Anova
Two sample T test
Assumptions for Two sample T Test, F test for Equality of Variance, Comparing Group Means, Identifying your data, The T test procedure, Running Proc T test in SAS, Examining the equal variance t- test and p- value, Examining the unequal variance t- test and p value, Interpreting two sample t test result, One Sided Test, Test for difference on one side, The T test procedure and Side option, Performing a one sided test.
One Way Anova-Introduction
Anova Overview, The ANOVA hypothesis, The ANOVA Model, Sum of Squares, Assumptions for Anova, Predicted and Residual Values, Comparing group means with one way Anova, Examining the descriptive statistics across means, The GLM procedure, using the GLM procedure.
Anova with data from a Randomized block design
Observational Studies Vs Controlled Experiments, Nuisance Factors, Including a blocking variable in the model, More Anova Assumptions, Creating a Randomized block design, Performing Anova with Blocking,
Anova -Post Hoc Test
Multiple Comparison Methods, Tuckey’s multiple comparison method, Dunnet’s multiple comparison method, determining which mean is different, Diffogram and Tuckey’s Method, Control Plots and Dunnet’s Method, Proc GLM with LS means, performing post hoc pairwise comparison
Two Way Anova with Interactions
N Way Anova, Interactions, Two way Anova Model, Using Two way Anova, Identifying your data, Applying the two way Anova Model, Examining your data with Proc Means, Examining your data with Proc SG plot, Performing two way Anova with Interactions, Performing Post Hoc Pairwise comparison
Exploratory data analysis
Using scatter plots to describe relationship between continuous variables, Using Correlation to measure relationship between two continuous variables, Hypothesis testing for a correlation, Avoiding common errors in interpreting correlations, Avoiding common errors Causal and Effect, Avoiding common Errors: Types of relationships, Avoiding common Errors: Outliers, Exploring data using correlation and scatter plots, Producing correlation statistics and scatter plots using Proc Corr, Using PROC CORR to produce correlation matrix and scatter plots, Examining correlations between predictor variables.
Simple Linear Regression
Objectives of simple linear regression, performing simple linear regression, The simple linear regression model, How SAS performs simple linear regression model, Measuring how well the model fits the data, Comparing regression model to a baseline model, Hypothesis testing for linear regression, Assumption of simple linear regression, The REG procedure: Performing Simple linear regression, Confidence and prediction intervals, Specifying Confidence and Prediction Intervals using SAS, Viewing and printing confidence intervals, The REG procedure producing predicted values, Producing predicted value of the response variable, Scoring predicted values using parameter estimates, Storing parameter estimates using Proc Reg and score using Proc score.
Multiple Linear Regression
Advantages and Disadvantages of Multiple Regression, Common Applications for Multiple linear regression, Picturing the model for Multiple Regression, Analysis versus prediction in multiple regression, Hypothesis testing for multiple regression, Assumptions for multiple regression, The Reg Procedure performing multiple linear regression
Model Building and interpretation
Approaches to selecting model, SAS and automated approaches to modeling, All possible regressions approach to model building, SAS and all possible regressions approach, Evaluating the model using Mallow’s Cp stat, Viewing Mallow’s Cp stat in Proc Reg, The REG procedure using all techniques, The REG procedure using automatic selection, The REG procedure Estimating and testing coefficients for selected models, The Stepwise selection approach to model building, Specifying Stepwise selection in SAS, The REG procedure performing stepwise regression, Using alternate significance criteria for stepwise models
Assumption for regression, the importance of plotting data and checking Assumptions, Verify Assumptions using residual plots, Detecting outlies using residual plots, The REG procedure producing default diagnostics, The REG procedure specific diagnostics
Identifying influential observations introduction
Using diagnostic statistic, Using Diagnostic Statistic STUDENT, Using Diagnostic Statistic COOK, Using Diagnostic Statistic RSTUDENT, Using Diagnostic Statistic DFFITS, Using Diagnostic Statistic DFBETAS, The REG procedure Requesting diagnostic plots, using diagnostic plots to identify influential observation, The REG procedure generating and saving diagnostic statistics, The REG procedure writing diagnostic statistics to an output dataset, Using cut off values for diagnostic criteria, Detecting influential observation programmatically, Handling influential
Understanding Collinearity, The REG procedure detecting Collinearity, Using Diagnostic statistic to detect Collinearity, The REG procedure Calculating diagnostics for Collinearity, The REG Procedure Dealing with Collinearity, Using an effective modeling cycle
Categorical data analysis
Describing categorical data
One Way Frequency tables, Association between categorical Variables, Cross Tabulation tables, Testing for Association and fitting a logistic model, The Tables statement in Proc Freq, Examining the distribution of categorical variables, Ordering the values of an Ordinal variable, Ordering a variable in cross tab table
Test of Association
The Pearson Chi Square Test, Cramer’s V Statistics, Odds Ratio, Performing Chi Square Test, The Mentel Haenszel Chi Square Test, The Spearman Correlation Statistic, Performing a Mentel Haenszel test for ordinal Association
Introduction to Logistic Regression
Logistic Regression, Modeling a binary response, The Logistic Procedure, Specifying a parameterized method in class statement, Effect Coding, Reference Cell coding, Fitting a binary logistic regression model, Interpreting odds ratio for a categorical predictor, Interpreting odds ratio for a continuous predictor, Comparing pairs to assess the fit of a model.
Multiple Logistic Regression
Multiple logistic regression, The backward elimination method for variable selection, Adjusted Odds ratio, Specifying the variable selection method in model statement, The Units Statement, Fitting a multiple logistic regression model, Comparing the binary and the logistic regression model, Specifying the formatted value as a reference, Interaction between variables, The backward elimination method with interactions, Specifying interaction in the model statement, Fitting a multiple logistic regression with interaction, The Odds ratio statement, Fitting a multiple logistic regression with all Odds ratio, Comparing multiple logistic regression models, Interaction plots
Professional SAS Tutors now also offers online Business Analytics certification courses & training in Delhi, India.
TAG: SAS Business Analytics Training in Delhi