Comprehensive course analysis
Who should attend
This is an advanced level analytics course, suitable for professionals with 2-3 years of experience, with an interest or requirement to develop advanced predictive models and provide input to improve service quality and apply predictive modeling in health space.
It is applicable for the following professionals who are engaged in the planning/forecasting and service innovation area:
About the course
This course has been designed to equip analytics professionals and managers with an understanding of how to solve complex prediction problems beyond what can be done using preliminary methodologies. The course covers a few diverse topics to take care of some of the real-world problems which require non-standard methodologies.
At the end of the course, the participants will be able to:
- Develop and implement advanced time series forecasting solution for any domain which can’t be solved using standard time series forecasting techniques
- While solving the above it will have all necessary discipline of
- Evaluating predictive modelling objective
- Design the predictive analytics process
- Assess and select the appropriate testing methods to validate the models
- Analyse the results and communicate the decision to the senior management and facilitate deployment to support the end-users
- Develop & implement TTE (Time to Event) modelling solution using censored/truncated data
- Develop Conjoint solution a type of discrete choice modelling that has many industry applications
- Understand the domain-specific variation needed in each of the topics
What Will Be Covered
- Module 1: Introduction to Advanced Predictive Modelling
- Module 2: Revisit Time Series Methods (ACF/PACF Functions, AR/MA)
- Module 3: ARIMA & Seasonal ARIMA Methods
- Module 4: Workshop 1: Forecasting using ARIMA/SARIMA methods based on relevant practical case study
- Module 5: Extending Univariate to Multivariate Time Series – Transfer Functions
- Module 6: Introduction to ARCH & GARCH Modelling
- Module 7: Workshop 2: Time Series Forecasting case study using Transfer Functions
- Quiz 1
- Module 8: Introduction To Conjoint Analysis
- Module 9: Traditional Conjoint
- Module 10: Adaptive Conjoint Analysis (ACA)
- Module 11: Workshop 3: Case study: Traditional Conjoint Models development to Solve an Industry Problem
- Module 12: Choice-Based Conjoint (CBC)
- Quiz 2
- Module 13: Predictive modelling Using Survival Analysis
- Module 14: Workshop 4: Case Study & Workshop using CBC & ACA to Solve an Industry Problem
- Module 15: Survival Analysis continued
- Module 16: Case Study and Workshop on Survival Analysis Modelling
- Quiz 3
Rita as a seasoned analytics professional has 25+ years of experience in Financial Services, Insurance and Market Research specializing in Scoring (operational & regulatory), Risk Management, Marketing Analytics, Analytics Strategy Development, Analytics Infrastructure, Analytics team managem...
David Hufton graduated from Kings College, Cambridge in 1975, and went on to complete an MSc in Statistics at Imperial College, London. Since then he has worked for a wide spectrum of companies during his forty years in Industry, including Aerospace companies, IT companies Government Departments ...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
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