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Analytics Traning

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Analytics using SAS

Analytics using SAS

SAS Analytics provide an integrated environment for predictive and descriptive modeling, data mining, text analytics, forecasting, optimization, simulation, experimental design and more. From dynamic visualization to predictive modeling, model deployment and process optimization, SAS provides a range of techniques and processes for the collection, classification, analysis and interpretation of data to reveal patterns, anomalies, key variables and relationships, leading ultimately to new insights and better answers faster.


Module-1 (Introduction to Business Analytics)
• Relevance in industry and need of the hour
• Types of analytics – Marketing, Risk, Operations, etc
• Future of analytics and critical requirement

Module-2 (Fundamental of Statistics)
• Basic statistics; descriptive and summary
• Inferential statistics
• Statistical tests

Module-3 (Data Prep & Reduction techniques )
• Need for data preparation
• Outlier treatment
• Flat-liners treatment
• Missing values treatment
• Factor Analysis

Module-4 (Basic Analytics)
• Statistics Basics Introduction to Data Analytics and Statistical Techniques
• Types of Variables, measures of central tendency and dispersion
• Variable Distributions and Probability Distributions
• Normal Distribution and Properties
• Central Limit Theorem and Application
• Hypothesis Testing Null/Alternative Hypothesis formulation
• One Sample, two sample (Paired and Independent) T/Z Tes
• P Value Interpretation
• Analysis of Variance (ANOVA)
• Chi Square Test
• Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)
• Correlation

Module -5 (Customer Segmentation)
• Basics clustering
• Deciles analysis
• Cluster analysis (K-means and Hierarchical)
• Cluster evaluation and profiling
• Interpretation of results

Module-6 (Regression Modeling)
• Basics of regression analysis
• Linear regression
• Logistic regression
• Interpretation of results
• Multivariate Regression modeling

Online Sas Training
Analytics using SPSS

Analytics using SPSS

SPSS Statistics is a software package used for statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009, and current versions are officially named IBM SPSS Statistics. SPSS is among the most widely used programs for statistical analysis in social science. It is used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, and others.


Module-1 (Introduction to SPSS)
• Windows, Menu, Status bar
• Dialogue box, sub-dialogue box
• Variable names, variable labels
• Basic steps in data analysis

Module-2 (Data files and Editor)
• Opening a SPSS and external data file and options
• Saving a files and options
• Data view, Variable view, entering data, editing data
• Go to case, Case selection

Module-3 (Data preparation, transformations and file handling, )
• Defining variables and its properties
• Computing variables, functions, missing values, recoding values
• Sort, transport, merge, aggregate data, split file, select cases, weight cases
• Output viewer, output export, saving output

Module-4 (Working with command syntax)
• Syntax rules
• Editing syntax
• Multiple execute commands

Module -5 (Frequencies, descriptive, Explore, Crosstabs)
• Frequencies statistics, charts and formats
• Descriptive and options
• Explore statistics, plots and options
• Crosstabs layers, statistics and format
• Summarize options and statistics

Module-6 (Statistical analysis in SPSS)
• Means and options
• Factor analysis
• Cluster analysis
• T- tests, paired t-tests, one sample t-test
• Bivariate and partial correlations
• Linear regression (simple and multiple)


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