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.
A Fishy Example of Cost-Sensitive Classification A 12-step Analytics Program in Healthcare and Medicine
ML and AI Making Big Moves in Marketing Analytics Exploratory Data Analysis: Successes, Insights, and Lessons
Data Literacy Exercises: Strengthening Your Data Science Abilities
Surprise Discovery in Regression Analysis
Neural Networks in Climate Modeling
ICA vs. PCA: The Cocktail Party Problem
Graph Mining: Connecting the Dots that Aren't Connected
Forecasting 2.0: Beyond Traditional Forecasting Clustering Analysis: Down to Earth, and Up to Space
Association Mining for Predictive Modeling
The Ways of Bayes: Classification, Markov Models, Missing Value Imputation, Causal Analysis
Precursor Analytics with Statistical Clustering The Internet of Context: Forecasting-as-a-Service Matching ML Algorithms to Business Analytics Problems The Keys to a Successful Data Science Career
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.