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.
Course Overview Hi everyone. My name is Coach Culbertson, and welcome to my course, Communications Expectations to the Business. I am an author at Pluralsight. Demand for jobs in data science and machine learning is booming and will only increase. In this course, we are going to take on the challenge of finding data for a media insights generation project that will help a streaming video company license movies that will be released two to four years in the future. Some of the major topics that we will cover include the four major sources of data, the need for a subject matter expert in the domain, challenges that personally identifiable information present, and generating synthetic data from an origin source. By the end of the course, you'll have a solid grasp on how to communicate barriers to data access and how to adjust expectations for stakeholders so that they can have a realistic picture of what a solution needs and what they can expect from its results. From here, continue your learning by diving into data science with other courses on machine learning and Python. I hope you'll join me on this journey to Communicating Expectations to the Business, at Pluralsight.