Learn how to manage your stakeholders' expectations for a solution as well as the ins and outs of procuring data for training a model. You'll also explore the idea of synthetic generation--it could save your project and your budget!
Discover critical skills in communicating barriers and solutions to data acquisition for model training. In this course, Communicating Expectations to the Business, you will learn foundational knowledge that will aid you in managing stakeholders' expectations of data science, machine learning, and augmented intelligence solutions. First, you will learn what is needed for a data science solution. Next, you will discover the four main sources of historical data that can be used to train models for a solution that will generate insights that will be used by a team, and what barriers you may encounter in acquiring that data. Then, you will examine an innovative solution, synthetic data generation, that will aid in transforming existing data while maintaining the data's character, personality, and richness. Finally, you will explore how to communicate solutions and expectations to stakeholders on data availability and formatting, and ask for a go/no-go decision. When you're finished with this course, you will have the skills and knowledge of communicating challenges around availability of data, and strategies needed to overcome barriers to bring needed historical data to your data science and machine learning solution.
Discover critical skills in communicating barriers and solutions to data acquisition for model training. In this course, Communicating Expectations to the Business, you will learn foundational knowledge that will aid you in managing stakeholders' expectations of data science, machine learning, and augmented intelligence solutions. First, you will learn what is needed for a data science solution. Next, you will discover the four main sources of historical data that can be used to train models for a solution that will generate insights that will be used by a team, and what barriers you may encounter in acquiring that data. Then, you will examine an innovative solution, synthetic data generation, that will aid in transforming existing data while maintaining the data's character, personality, and richness. Finally, you will explore how to communicate solutions and expectations to stakeholders on data availability and formatting, and ask for a go/no-go decision. When you're finished with this course, you will have the skills and knowledge of communicating challenges around availability of data, and strategies needed to overcome barriers to bring needed historical data to your data science and machine learning solution.
