SAS, Financial modeling Excel Hot Skills - Part 1

Hot Skills:

   • Enterprise Guide – GUI point-and-click front-end application

   • Hash Programming Techniques – “Fast” table lookups, sorts, merges, and joins

   • Create RTF, PDF, HTML, XML, and Excel Spreadsheets with Output Delivery System (ODS)

   • Queries, Tables, Views, Case Expression Logic, inner and outer Joins – PROC SQL

   • Detail and Summary Reporting – PROC REPORT

   • Construct Reusable Code and Tools – Macro Language

   • Access SAS Environment – Dictionary Tables and SASHELP Views

   • Business Intelligence

SAS Model Developers 

• SAS Modelling and development 
• Able to create reusable objects and job templates Schedule, compile, and run SAS jobs efficiently
• skills in database and data warehouse design principles - SQL Coding and querying skills
• skills of UNIX
• Data mapping experience Creates clear and concise documentation, produces source to target maps, understands and validates metrics
• work well with cross border technical team members.
• Must also be able to interface strongly with the business.
• Risk, fraud, market campaign background knowledge
  Experience/people/networking

Advanced statistical analysis

regression modeling, conjoint analysis, audience segmentation, and cluster analysis; also must show experience with behavioral tracking data; multiple linear regression, hierarchical linear modeling, psychometrics (including item response theory)

Multivariate modeling experience such as linear and nonlinear regression models, limited dependent variable models, cluster analysis

Segmentation/classification methods, EM algorithm, hierarchical Bayesian models and MCMC simulation methods; Specific knowledge of latent class models, linear discriminant analysis, conjoint and discrete choice models and etc. in market research is a plus

ANOVA, Regression, Generalized Linear Models and Multivariate Methods

Different types of data mining tasks in relation to various business concerns, including classification, prediction, cluster analysis and segmentation, and association analysis and market basket analysis.  

Critically review and appreciate the strengths and weaknesses of the different data mining techniques, models, and tools. 

Apply appropriate data mining techniques for a given real-world problem. 

Evaluate various models built from a data mining process. 

Undertake a data mining project with clear business focus, in particular, in relation to CRM analysis, RFM modelling, and credit risk scoring

Financial modeling in excel, data mining excel sql Advanced Business Intelligence Solutions Using Microsoft Excel Excel Services