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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with basic Python experience

Who should attend & recommended skills

  • This course is geared for developers who want to leverage the analytical power of SAS to perform financial analysis efficiently.
  • Skill-level: Foundation-level SAS for Finance skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

SAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data. SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues. This lesson shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs. By the end of this lesson, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.

Skills acquired & topics covered

  • Leveraging the power of SAS to analyze financial data with ease
  • Finding hidden patterns in your data, predict future trends, and optimize risk management
  • Why leading banks and financial institutions rely on SAS for financial analysis
  • Understanding time series data and its relevance in the financial industry
  • Building a time series forecasting model in SAS using advanced modeling theories
  • Developing models in SAS and infer using regression and Markov chains
  • Forecasting inflation by building an econometric model in SAS for your financial planning
  • Managing customer loyalty by creating a survival model in SAS using various groupings
  • Understanding similarity analysis and clustering in SAS using time series data

Course breakdown / modules

  • Time series illustration
  • The importance of time series
  • Forecasting across industries
  • Characteristics of time series data
  • Challenges in data
  • Good versus bad forecasts
  • Use of time series in the financial industry

  • Portfolio forecasting
  • A portfolio demands decisions
  • Forecasting process
  • Visualization of time series data
  • Dealing with multicollinearity
  • Role of autocorrelation
  • Scoring based on PROC REG
  • Recap of key terms

  • Risk types
  • Basel norms
  • Credit risk key metrics
  • Aspects of credit risk management
  • PD model build

  • The need for the Markov model
  • Business problem
  • Markovian model approach
  • ARIMA model approach
  • Markov method for imputation

  • What is inflation?
  • Business case for forecasting inflation
  • Modeling methodology

  • Advantages of survival modeling
  • Key aspects of survival analysis
  • Business problem

  • Market basket analysis
  • Segmentation and clustering
  • MBA business problem
  • Data preparation for MBA
  • Assumptions for MBA
  • Analysis of a set size of two
  • A segmentation business problem
  • Segmentation overview
  • Clustering methodologies
  • Segmentation suitability in the current scenario
  • Segmentation modeling