In this course, you will explore data preparation, data modeling, data visualization, and data cataloging using Watson Studio, Watson Knowledge Catalog, and Watson Machine Learning.

• Data science and AI
• Watson Studio
• Watson Machine Learning
• Watson Knowledge Catalog
• Data refinement
• Data modeling
• Data science with notebooks
• Model deployment

Data scientists, data engineer, business analyst

None

Data science and AI • Describe the value of artificial intelligence • Explain the AI ladder approach and AI lifecycle • Identify the roles for working with data and AI Watson Studio • Summarize the benefits of Watson Studio • Outline the integration of Watson Studio and Watson Machine Learning • List and explain the tools available in Watson Studio • Sign up for a free IBM Watson account Watson Machine Learning • Describe machine learning methods and how they fit with AI • Create a Watson Studio project for learning models Watson Knowledge Catalog • Explain the features of Watson Knowledge Catalog • Identify the role of data policies to govern data assets • List and describe the data files used in this course • Create a catalog, add assets to a catalog, and add catalog assets to a project Data refinement • List the steps to successful data mining • Describe the typical customer churn business problem • Identify the steps in the data refinement process • Shape a data set using the Data Refinery according to specific observations Data modeling • Differentiate the Watson Studio tools to create models • Create a Watson Machine Learning model using AutoAI • Create a Machine Learning model using SPSS Modeler • Build a model using SparkML Modeler Flow Data science with notebooks • Experiment with Jupyter notebooks • Load from a file and run a Jupyter notebook with Watson Studio Model deployment • Identify the model repository • List model deployment and test options • Deploy a model • Test a deployed model