Identify modeling opportunities and categorize them (Detect, Discover, Predict, Optimize)
Select appropriate modeling components and ML algorithms for specific use cases
Design your own analytics solutions
Solve a broad range of problems
Generate value from the data assets in your organization
Communicate to your stakeholders the importance and meaning of models in data-intensive environments
Attendees will also work on data literacy exercises and study exploratory data analysis use cases that broaden and deepen one’s understanding and abilities in insights discovery and value creation from data.
Dr. Kirk Borne
Principal Data Scientist And Executive Advisor, Booz Allen Hamilton
Training Overview and Data Science Preliminaries
Introduction to Modeling Concepts
Supervised vs. Unsupervised Modeling
Insights Discovery and Generalization
Supervised Learning Concepts
Predictive vs. Prescriptive Modeling
What does Cognitive have to do with it?
The Two Most Important Things in Data Science
Optimization and Feedback Loops in Modeling
Cold-Start Modeling: When the Data Becomes the Model (Unsupervised ML)
Machine Learning vs. Deep Learning
Common Business Modeling Examples
The OODA Loop in Decision Science and Data Science
When Predictive Modeling Fails
Enriching Your Models with Smart Data (Semantic Tags, Labels, Annotations)
Exploiting High-Variety Data to Achieve Better Model Outcomes
Steps to Data Analytics Mastery
Data scientists, data analysts, business intelligence practitioners, data users, and other analytics-related professionals are the target audience for this training. Generally, this training is for anyone:
Who seeks to understand how machine learning works and how ML models can deliver actionable insights, decision support, and value to their organization.
Who wants to become more knowledgeable and proficient in identifying machine learning opportunities and in contributing to ML modeling applications.
Who seeks to learn the power of machine learning models in thought and action, in order to progress in your own career journey (e.g., from data analyst to data scientist).
Some experience with machine learning will make this workshop easier to follow, but all that is required is basic knowledge of the concepts.