Advanced Analytics Methodologies
Advanced Analytics Methodologies (2 days)
In this course you will get an introduction to the most important methodologies, algorithms and ideas in the data scientist’s toolbox. You will learn to use advanced methodologies to uncover underlying patterns or concepts contained in large datasets, automatically group objects into meaningful clusters, classify objects into predefined categories, and refine your features to enrich predictive modeling endeavors. This course covers the main algorithms of supervised and unsupervised learning an introduction into modern deep learning modeling approaches will be covered as well. The course will cover modern thinking on model evaluation, model selection, and novel ideas of model deployment. Exercises are offered for KNIME, R, Python, SAS and Spark.
Course Outline
- Introduction to Data Science and Challenges
- Key concepts of Advanced Analytics
- Introduction to Feature Engineering
- Supervised Learning (Decision Tree, Regression, Neural Network, Naïve Bayes, Support Vector Machines, Ensemble Modeling)
- Unsupervised Learning (Cluster Analysis, Association Analysis)
- Introduction to Deep Learning
- Model Assessment & Model Comparison
- Model Deployment
Who should attend
Data Scientists, statisticians, business analysts, market researchers, and information technology professionals who need to get started with Advanced Analytics and want to make better use of their data. Managers and Executives who want to embed analytics into their operating model and maximize data-driven business results at scale.
Did not find the training you are looking for? Please feel free to ask for any other Advanced Analytics training.