Watson Studio Methodology – eLearning – W7067G WBT

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Details


Course Code: W7067G

Brand: DS&BA – Watson Studio Local

Category: Analytics

Skill Level: Basic

Duration: 6.0H

Modality: WBT

 

 

Audience


Data scientists, data engineer, business analyst

 

Prerequisites


None

 

Short Summary


This course explores data preparation, data modeling, data visualization, and data cataloging.

 

Overview


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.

 

Topic


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 
 

 

Objectives


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