This week we return to Coronavirus data to look at new analyses that use mobile phone data to estimate the effects of social distancing restrictions, a vital question now are we see the world falling into “lockdown recession.” Speaking of economic matters, our course spotlight is:
June 12 – July 10: Financial Risk Modeling
See you in class!
– Peter Bruce
P.S. Our new course, Analyzing and Modeling Coronavirus Data, also starts June 12
Founder, Author, and Senior Scientist
Tracking Your Wanderings, for the Public Good
Seven weeks ago, Google released a trove of location data that it collects from users via its Maps app, and other apps and software. This is the same data, in a more general form, that it uses to drive its search and advertising business, repackaged as a public service to assist with […]
Word of the Week
As the Coronavirus continues to spread, so will research on its behavior. Models that rely mainly on time-series data will expand to cover relevant other predictors (covariates), and one such predictor will be gregariousness. How to measure it? […]
Paul Olszlyn, Senior Data Scientist at NovoDynamics
Paul Olsztyn designs and implements databases at NovoDynamics, a company that creates and deploys large scale data systems for corporations. As his company responded to customer needs for more predictive analytics by building greater capacity in this area, Paul decided to move beyond his own IT and database background. He enrolled in Statistics.com’s Programming for Data Science certificate program, and began taking classes in his spare time. There he learned how to apply Python’s scikit-learn for building and assessing predictive models, skills that he now uses as NovoDynamics continues to develop its capacities in data science.
The world is facing the deepest economic crash since the great depression, so, naturally, our spotlight this week is on
Financial Risk Modeling (June 12 – July 10)
In this course, you will learn how to
- Set up financial model simulations using appropriate probability distributions
- Use time-series models, and characterize the different components of a time series (trend, seasonality, autocorrelation, volatility, mean reversion)
- Use various technique to include correlations within simulation models
- Fit appropriate probability distributions to historical data, and assess the fit (AIC, etc.)
- Interpret results from simulation models, including expected Net Present Value (eNPV), Value at Risk (VaR), and the probability of negative NPV
Your instructor is Dr. Huybert Groenendaal, Managing Partner at EpiX Analytics, which specializes in risk analysis and modeling techniques for clients around the world. He has extensive experience in risk modeling and analysis for business development, financial valuation, R&D portfolios and portfolio evaluations in pharmaceuticals and medical devices.
Analyzing and Modeling Covid-19 Data (June 12 to July 10)
We’ll cover analysis of Covid data broadly, and focus on the epidemiological and statistical models used to forecast the spread of the pandemic. In this seminar-style course for statistically-literate* researchers, you will
- Explore key rates and features of the Coronavirus data
- Learn how to specify epidemiological models
- Learn how to fit statistical models
- The strengths and weaknesses of each type of model
The instructors are
- James Hardin, Epidemiology and Biostatistics Associate Professor and Associate Dean for Faculty Affairs and Curriculum for the Arnold School of Public Health, University of South Carolina.
- Wayne Folta a Lead Data Scientist at Elder Research, Inc. where he develops and deploys models for clients. His current work involves the design and implementation of text mining and deep learning models at the U.S. Dept. of Health and Human Services; he has also been active within Elder Research in exploring and assessing epidemiological models in R.
See you in class!
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