Official SAS Curriculum Courses

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1 Certificate course in Predictive Business Analytics Official SAS Curriculum Courses SAS Programming Base SAS An overview of SAS foundation Working with SAS program syntax Examining SAS data sets Accessing SAS libraries Producing Detail Reports Sorting and grouping report data Enhancing reports Formatting Data Values Creating user-defined formats Reading SAS Data Sets Customizing a SAS data set Handling missing data Manipulating Data Combining SAS Data Sets Creating Summary Reports Controlling Input and Output Summarizing Data Reading Raw Data Files Data Transformations Debugging Techniques Using the PUTLOG statement Processing Data Iteratively Restructuring a Data Set Creating and Maintaining Permanent Formats SAS Global Certification SAS Certified Base Programmer Advance SAS SAS Macros Macro Variables Definitions Data Step and SQL Interfaces Macro Programs Optional SAS SQL Basic Queries Sub-Queries Joins (SQL) Operators Creating Tables and Views Additional Proc SQL Features Managing Tables Accessing RDBMS Advanced Techniques and Efficiencies Measuring Efficiencies Controlling I/O Processing and Memory Accessing Observation Using DATA Step Arrays Using DATA Step Hash and Hiter Objects Combining Data Horizontally Expert Programmer Techniques Optional SAS Global Certification SAS Certified Advanced Programmer Querying Microsoft SQL Server Microsoft Official Course Introduction to Microsoft SQL Server 2014 Introduction to T-SQL Querying Writing SELECT Queries Querying Multiple Tables Sorting and Filtering Data Working with SQL Server 2014 Data Types Using DML to Modify Data Using Built-In Functions Grouping and Aggregating Data Using Subqueries Mapped to exam Microsoft SQL Server: Using Table Expressions Using Set Operators Using Window Ranking, Offset, and Aggregate Functions Pivoting and Grouping Sets Executing Stored Procedures Programming with T-SQL Implementing Error Handling Implementing Transactions Improving Query Performance Querying SQL Server Metadata Banking/Financial Project SAS based fully functional financial project. Implement banking/financial analytics functions using Base SAS, SAS/SQL and SAS Macros. Programmatically translating the Underlying domain requirements to produce required reports.

2 Module A Analytics Theory Type I and Type II errors. Analytics Concepts: Descriptive Statistics Statistical Methods (Introduction) Introduction to Predictive Analytics Introduction to Statistics Descriptive Statistics Frequency Distributions Statistical graphs Central tendency & Dispersion Mean, Median and Mode Range, Interquartile Range and Variance / Standard Deviation Coefficient of variation Skewness & Kurtosis Module B Data Modeling Introduction to Probability Introduction to probability theory Axioms/Rules of Probability Random variable & Probability distribution functions Excepted value and Variance Advance Probability concepts Probability tree s Joint, Marginal and Conditional Probabilities Statistical Independence Probability Distributions Discrete Probability distributions o Binomial o Poisson Continuous Probability distributions o Normal Distribution Sampling & Estimation Introduction to sampling and types of sampling Parameter and Statistic Sampling distribution of a statistic Sampling distribution of a mean Point and Interval estimates Confidence Intervals and Central Limit Theorem. Hypothesis Testing: One Sample tests Hypothesis Testing: Two Sample tests Introduction Hypothesis testing for difference between means Paired t-test Pooled and Satterthwaite-Welch t-test Chi-Square Analysis Introduction Chi-square as a test of Independence Chi-square as a Goodness of fit Inference about the population variance ANOVA Introduction to ANOVA One-way ANOVA Post-HOC analysis Factorial ANOVA Correlation Correlation analysis and Interpretation Linear Regression Introduction Simple Regression and Estimating using the Regression Line Making inferences about the population parameters Multiple Regression and correlation Analysis Logistic Regression Introduction to logistic regression Concept of Odds Ration, Logistic function. Maximum likelihood function and parameter estimates Goodness of fit statistics Multiple Lo Introduction to Hypothesis testing Testing Hypothesis Hypothesis testing when population SD is known and Unknown Significance level, Critical Region and P-value

3 Predictive Analytics using SAS SAS Statistics: Introduction to ANOVA, Regression, and Logistic Regression Generate descriptive statistics and explore data with graphs Perform analysis of variance and apply multiple Comparison techniques Perform linear regression and assess the assumptions Use regression model selection techniques to aid in the choice of predictor variables in multiple regression Use diagnostic statistics to assess statistical Assumptions and identify potential outliers in multiple regression Use chi-square statistics to detect associations Among categorical variables Fit a multiple logistic regression model. Predictive Modelling Using Logistic Regression Use logistic regression to model an individual's behaviour as a function of known inputs Create effect plots and odds ratio plots using ODS Statistical Graphics Handle missing data values Tackle multicollinearity in predictors Assess model performance and compare models. SAS Global Certification: SAS Certified Statistical Business Analyst using SAS 9: Regression and Modeling Data Visualisation SAS Visual Analytics Getting Started with SAS Visual Analytics Exploring SAS Visual Analytics concepts Using the SAS Visual Analytics home page Discussing the course environment and scenario Administering the Environment and Managing Data Exploring SAS Visual Data Builder Exploring SAS Visual Analytics Administrator Using the SAS Visual Analytics Explorer Examining the Visual Analytics Explorer Selecting data and defining data item properties Creating visualizations Enhancing visualizations with analytics Interacting with visualizations and explorations Designing Reports with SAS Visual Analytics Examining the SAS Visual Analytics Designer interface Creating a simple report Creating data items and working with graphs Working with filters and report sections Establishing interactions, links, and alerts Working with gauges and display rules Working with tables Working with other objects Viewing SAS Visual Analytics Reports Viewing reports on the Web Viewing reports on a mobile device Viewing reports with SAS Office Analytics Case Study: Creating Analyses and Reports with SAS Visual Analytics Demonstration Exercises SAS Global Certification SAS Certified Visual Business Analyst: Exploration and Design Using SAS Visual Analytics (Optional)

4 Analytics Tools Microsoft Power BI Introduction to Self-Service BI Introduction to Power Pivot Importing Data Into Power Pivot Creating the Data Model Creating Calculated Columns with DAX Creating Calculated Measures with DAX Building Power Pivot Reports Introduction to Power View Creating Basic Power View Reports Optimizing Power Pivot Models for Power View Reporting Creating Interactive Reports with Power View Touring Data with Power Map Introduction to Power Query Importing Data Using Power Query to Transform Data Making Self-Service BI Scalable Using Power BI Q&A to Unleash your Data Microsoft Excel 2013 Microsoft Courses 55130AC/31/32 Creating a Microsoft Excel Workbook The Ribbon The Backstage View (The File Menu) The Quick Access Toolbar Entering Data in Microsoft Excel Worksheets Formatting Microsoft Excel Worksheets Using Formulas in Microsoft Excel Working with Rows and Columns Editing Worksheets Finalizing Microsoft Excel Worksheets Advanced Formulas Working with Lists Working with Illustrations Visualizing Your Data Working with Tables Advanced Formatting Using Pivot Tables Auditing Worksheets Data Tools Working with Others Recording and Using Macros Random Useful Item Leads to MOS: Microsoft Office Excel 2013 certification exam Microsoft Excel Power Programming with VBA John Walkenbach Understanding Excel VBA Starting With Excel VBA Procedures, Using Variables Functions In VBA Using Excel Objects Building Formular Control Structures Creating Custom Forms Programming User Forms Automatic Start-up R Programming R for Everyone: Jared P Lander Basics of R Programming (variables, data types, vectors, functions, etc.) Advanced R Programming (Data structures, Reading data, statistical graphics, control statements, loops, data reshaping) Probability Distributions in R (Normal, Binomial, Poisson) Basic Statistics in R (Summary statistics, correlation, covariance, t-tests, Anova) Linear Models in R (Simple Linear Regression, Multiple Regression) Model Diagnostics in R (Residuals, Comparing models, stepwise selection)

5 OPERATIONALISING ADVANCED ANALYICS I. Customer Lifecycle - Introduction Customer Lifecycle Analytics Analytical Solutions for Financial Services II. Product and Data Credit Products Glossary of Terms Data Application and Performance Data Credit Bureau III. Acquisition Acquisition Roadmap Acquisition Process and Underwriting Acquisition Channels IV. Usage Definition Activation Activation Case Study Spend Management Spend Management Case Study Balance Build Segmentation Case Study V. Cross-Sell Definition Benefits and Challenges Industry Exemplars Types of Offering Cross-Sell Process Targeting Strategy Targeting Models Case Study VI. Retention Introduction Types of Attrition Retention Strategies Targeting Strategy - Case Study About Trainer: Dr. Sandhya Kuruganti is a senior professional with 18+ years of experience in business analytics, mostly in financial services. She has been working as a management consultant since Her last full time role was with Citibank, India as SVP, managing the Centre of Excellence for Asia Pacific and Europe. Prior to this role, she was heading the Analytics function for the India consumer business of Citibank. She started her career with American Express, New York in the risk management group. She is also a co-author of Business Analytics: Applications to Consumer Marketing, published by McGraw Hill and released in April By training, she is an econometrician with a doctorate in Economics from a U.S. based university. Program Highlights: Dr. Sandhya Kuruganti will take two sessions over two Saturday mornings (4hours each) covering the topics above. This will be followed up by the case studies to done individually by the participants. Lodestone trainers will be at hand to assist participants navigate the case studies. Each case study is expected to take 20 hours effort by the participant. Participants are expected to prepare presentations for each case study to be submitted to Dr. Sandhya for her individual review. CASE STUDIES for HANDS ON ANALYTICS 1. Telecom Churn Prediction using Logistic Regression 2. Credit Card Usage Segmentation using Cluster Analysis 3. Personal Loan Risk Segmentation using Decision Tree

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