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Data Processing and Optimization with Generative AI

Microsoft via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

5 weeks, 3 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English, German, Hindi, Pashto, Russian, French, Chinese, Hungarian, Ukrainian, Indonesian, Kazakh, Swedish, Korean, Portuguese, Greek, Italian, Spanish, Arabic, Vietnamese, Thai, Japanese, Azerbaijani, Polish, Dutch, Turkish

Found in

Part of

Microsoft Generative AI for Data Analysis

Overview

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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. Upon completion of this course, you'll be able to: Generate synthetic data using generative AI models Implement advanced data cleaning techniques with AI assistance Optimize datasets for improved analysis efficiency Apply ethical considerations in data processing and synthetic data generation

Syllabus

Synthetic data generation
- In this module, you will explore how generative AI can support synthetic data creation when real data is limited, sensitive, or incomplete. You will examine approaches such as Copilot-supported generation, Python-based generation, GANs, and VAEs. You will also consider benefits, limitations, bias concerns, and ways to compare synthetic data with real data.
Advanced data cleaning techniques
- In this module, you will explore how generative AI can help identify and address complex data quality issues. You will learn about issues such as inconsistent formats, missing values, outliers, hidden errors, and complex data structures. You will also consider how techniques such as anomaly detection, data imputation, and format conversion support cleaner and more reliable datasets.
Dataset preparation
- In this module, you will examine how dataset preparation affects the quality and reliability of generative AI workflows. You will explore preprocessing concepts such as cleaning, transformation, normalization, feature engineering, and data wrangling. You will also compare synthetic, real-world, and hybrid datasets and consider when each type may be appropriate.
Datasets optimization
- In this module, you will explore the key components of a well-structured dataset. You will consider how generative AI can help improve dataset relevance, accessibility, structure, and quality. You will also examine how optimized datasets can be compared with original datasets to evaluate changes.
Ethical considerations in data processing
- In this module, you will examine ethical considerations related to data processing and synthetic data generation. You will explore issues such as bias, fairness, privacy, transparency, and potential misuse of data. You will also consider how responsible practices and ethics guidelines can support better decisions when working with generative AI and data.