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SAS for Finance

  • Course Code: Data Science - SAS for Finance
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for those who wants to leverage the analytical power of SAS to perform financial analysis efficiently.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level SAS for Finance skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to leverage the analytical power of SAS to perform financial analysis efficiently. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our SAS for Finance expert instructor, students will learn about and explore: 

  • Leverage the power of SAS to analyze financial data with ease 
  • Find hidden patterns in your data, predict future trends, and optimize risk management 
  • Learn why leading banks and financial institutions rely on SAS for financial analysis 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand time series data and its relevance in the financial industry 
  • Build a time series forecasting model in SAS using advanced modeling theories 
  • Develop models in SAS and infer using regression and Markov chains 
  • Forecast inflation by building an econometric model in SAS for your financial planning 
  • Manage customer loyalty by creating a survival model in SAS using various groupings 
  • Understand similarity analysis and clustering in SAS using time series data 

Audience & Pre-Requisites 

This course is designed for developers wants to leverage the analytical power of SAS to perform financial analysis efficiently. 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Time Series Modeling in the Financial Industry 
  • Time Series Modeling in the Financial Industry 
  • 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 
  1. Forecasting Stock Prices and Portfolio Decisions using Time Series 
  • Forecasting Stock Prices and Portfolio Decisions using Time Series 
  • 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 
  1. Credit Risk Management 
  • Credit Risk Management 
  • Risk types 
  • Basel norms 
  • Credit risk key metrics 
  • Aspects of credit risk management 
  • PD model build 
  1. Budget and Demand Forecasting 
  • Budget and Demand Forecasting 
  • The need for the Markov model 
  • Business problem 
  • Markovian model approach 
  • ARIMA model approach 
  • Markov method for imputation 
  1. Inflation Forecasting for Financial Planning 
  • Inflation Forecasting for Financial Planning 
  • What is inflation? 
  • Business case for forecasting inflation 
  • Modeling methodology 
  1. Managing Customer Loyalty Using Time Series Data 
  • Managing Customer Loyalty Using Time Series Data 
  • Advantages of survival modeling 
  • Key aspects of survival analysis 
  • Business problem 
  1. Transforming Time Series – Market Basket and Clustering 
  • Transforming Time Series – Market Basket and Clustering 
  • 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 
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