This course focuses on advanced methods for data cleaning, preparation, and optimization using AI-assisted tools. You'll learn to generate synthetic data, address privacy concerns and data limitations in your projects. Discover how to leverage AI to identify and resolve complex data quality issues, ensuring your datasets are primed for analysis.
Data Processing and Optimization with Generative AI
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Data Processing and Optimization with Generative AI
This course is part of Microsoft Generative AI for Data Analysis Professional Certificate
Instructor: Microsoft
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
October 2024
Assessments
28 assignments
Taught in English
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This course is part of the Microsoft Generative AI for Data Analysis Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
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There are 5 modules in this course
Explain the process of generating synthetic data using generative AI, identifying its applications and potential benefits in addressing data limitation
What's included
10 videos8 readings6 assignments
Apply generative AI tools to identify and resolve complex data quality issues, such as outliers, inconsistencies, and errors,ensuring data integrity for accurate analysis.
What's included
7 videos6 readings6 assignments
Analyze the impact of data preparation on subsequent analysis and utilize generative AI tools to automate and optimize preprocessing steps, ensuring data readiness for analysis
What's included
6 videos6 readings6 assignments
Describe the key components of a well-structured dataset and the role of generative AI in enhancing data quality for analysis
What's included
7 videos7 readings6 assignments
Evaluate the ethical implications of data processing and synthetic data generation, developing strategies to mitigate biases and ensure responsible and transparent data practices.
What's included
10 videos6 readings4 assignments
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