Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision-making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too!
Join Elaine Eisenbeisz of Omega Statistics as she presents the rationale and risks of predictive modeling via supervised learning techniques. Elaine will also provide an overview of some of the many available modeling techniques including:
- K-Nearest Neighbors
- Logistic Regression
- Linear Discriminant Analysis
- Decision Trees
- Linear Regression
- Subset Selection
- Shrinkage Methods (Ridge Regression, Lasso Regression)
- Resampling Methods (Bootstrap)
- Ensemble Methods (Boosting, Bagging, Random Forests)
Demonstration of the models using R software will be performed, and sample code and datasets will be provided for attendees to practice with.
Who will Benefit:
- Data scientists
- Data managers
- Data processors
- Statisticians
- Professionals in pharmaceutical, medical device, clinical and biotechnology research who work with data collection and management
- Individual researchers in health and biotech fields.
- Person’s designated as “data people” in the organization
- Anyone who is interested in using data to gain insight and help in decision making
- Graduate students in the biological or other sciences, marketing and medical researchers.
In-Person Seminar going Virtual with increased learner satisfaction.
Yes, attend this seminar from anywhere. We are making it real and more interactive – Here's a sneak peek:Our enhanced delivery process and technology provides you an immersive experience and will allow you to access:
- The real-time and live presentation as in in-person events
- Private chat for company-specific conversation – the same as you would get in an in-person seminar
- Opportunities to connect with your peers to share knowledge at a different time and have group discussions
- Live workshop activities
- Live Q&A during the event and offline Q&A assistance after the event
- As usual more content, activities and case studies and now adding homework for a comprehensive understanding
- Certification
November 12th, 2020 (10:00 AM - 4:00 PM PST)
- Session 1 (90 Mins): The What and Why of Predictive Modeling
- 2 types of Modeling/Learning
- Supervised Learning
- Unsupervised Learning
- Data Science vs. Statistics
- Epicycles of Data Analysis in Data Science
- GIGO = Garbage In, Garbage Out. Why we need good practices in data management
- Goals of Predictive models
- Trade-offs in Predictive Modeling
- Assessing Model Accuracy
- 2 types of Modeling/Learning
- Session 2 (90 mins.: Classification Methods
- K-Nearest Neighbors
- Logistic Regression
- Linear Discriminant Analysis
- Tree Based Methods
- Session 3 (90 Mins): Regression Models
- Linear regression
- Subset Selection
- Ridge Regression
- Lasso Regression
- Session 4 (90 Mins.) Bootstrap and Ensemble Methods
- Bootstrapping techniques
- Bagging and Boosting
- Random Forests
Elaine Eisenbeisz
Owner, Omega Statistics
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.
Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.
Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.
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