Data Processing and Optimization with Generative AI

本课程是 Microsoft Generative AI for Data Analysis 专业证书的一部分

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该课程共有5个模块

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.

Explain the process of generating synthetic data using generative AI, identifying its applications and potential benefits in addressing data limitation

涵盖的内容

10个视频8篇阅读材料6个作业

10个视频总计51分钟

The data dilemma: When real data isn't enough 2分钟
Introducing synthetic data: The artificial twin 8分钟
Synthetic data in real-world applications 7分钟
The synthetic data factory: How GenAI makes it happen 3分钟
Demo: The magic behind synthetic data: Generative AI in action 8分钟
The synthetic data superhero: Saving the day with fake data 2分钟
Demo: Hands-on synthetic data creation: A guided tour 7分钟
Synthetic data in Python: A practical tutorial 7分钟
The data detective evaluates synthetic data: Real or fake? 2分钟
Comparative analysis: Synthetic vs. real data 6分钟

8篇阅读材料总计75分钟

Data Processing and Optimization with Generative AI syllabus 5分钟
GenAI Tools for Data Analysis: A Practical Guide 10分钟
Synthetic data: Unlocking new possibilities 10分钟
The ethical landscape of synthetic data: Navigating privacy and fairness concerns 10分钟
The art of synthetic data generation: GANs vs. VAEs 10分钟
Best practices for synthetic data creation: Dos and don'ts 10分钟
Synthetic data in action 10分钟
Measuring synthetic data quality: Metrics and benchmarks 10分钟

6个作业总计100分钟

Activity: Assessing synthetic data scenarios 15分钟
Activity: Synthetic data generation 15分钟
Evaluating and measuring synthetic data quality 30分钟
Indentifying synthetic data use cases 10分钟
Understanding synthetic data generation 15分钟
Generating synthetic data with GenAI tools 15分钟

Apply generative AI tools to identify and resolve complex data quality issues, such as outliers, inconsistencies, and errors,ensuring data integrity for accurate analysis.

涵盖的内容

7个视频6篇阅读材料6个作业

7个视频总计33分钟

The data detective: Beyond the basics 2分钟
Real talk: A data cleaning story time 6分钟
GenAI to the rescue: The superhero of data cleaning 2分钟
GenAI data cleaning toolkit: A deep dive 6分钟
Expert insights: A data engineer's perspective on GenAI for data cleaning 7分钟
Beyond the basics: Advanced techniques in GenAI data cleaning tools 6分钟
The clean data transformation: Before and after GenAI 2分钟

6篇阅读材料总计60分钟

The anatomy of bad data 10分钟
How Generative AI tackles advanced data cleaning 10分钟
GenAI in action: Case studies of complex data cleaning 10分钟
Evaluating data cleaning success: Metrics and best practice 10分钟
Beyond cleaning: Leveraging Generative AI for data enrichment and augmentation 10分钟
The impact of GenAI cleaning with dashboards 10分钟

6个作业总计115分钟

Activity: Spotting advanced data quality issues 30分钟
Project: GenAI tools in action: Fixing the mess 15分钟
Data cleaning with GenAI 30分钟
Identifying advanced data quality issues 15分钟
Advanced data cleaning 15分钟
Using GenAI for data cleaning 10分钟

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

涵盖的内容

6个视频6篇阅读材料6个作业

6个视频总计36分钟

The data spectrum: A data scientist's guide to choosing the right dataset 7分钟
Exploring synthetic data generation tools 6分钟
Demo: Preprocessing data in Python: A step-by-step guide 6分钟
Feature engineering in action 7分钟
Bias in data: The silent threat to fairness 2分钟
Navigating the ethical landscape of generative AI 7分钟

6篇阅读材料总计60分钟

Hybrid datasets: The best of both worlds? 10分钟
The data preprocessing pipeline: A journey from raw data to model input 10分钟
Lessons learned 10分钟
The art and science of feature engineering: A practical guide 10分钟
Ethical considerations in generative AI 10分钟
Real-world examples of bias in generative AI 10分钟

6个作业总计95分钟

Activity: Choosing the right dataset type 15分钟
Activity: Feature engineering challenge 15分钟
Biases and ethical implications 30分钟
The data trio 15分钟
Data preprocessing techniques 10分钟
Feature engineering for generative AI 10分钟

Describe the key components of a well-structured dataset and the role of generative AI in enhancing data quality for analysis

涵盖的内容

7个视频7篇阅读材料6个作业

7个视频总计29分钟

The anatomy of a dataset 5分钟
The data makeover: GenAI's transformation magic 2分钟
How GenAI transformed workflows 6分钟
Hands-on dataset optimization: A step-by-step guide 6分钟
OpenRefine/ Microsoft Azure Data Factory: Tips and tricks 5分钟
The optimized dataset: A powerful tool for analysis 2分钟
Introducing evaluating effectiveness 2分钟

7篇阅读材料总计70分钟

The data landscape: A guide to different types of datasets 10分钟
Data relevance and accessibility: The dynamic duo of analysis 10分钟
The ethical implications of AI in data optimization 10分钟
Beyond the basics: Advanced GenAI techniques for dataset optimization 10分钟
Case studies: Successful dataset optimizations with GenAI 10分钟
The ROI of dataset optimization: Measuring impact 10分钟
Discussion: The impact of AI on dataset optimization 10分钟

6个作业总计90分钟

Activity: Dataset components check-up 15分钟
Activity: Identifying techniques for dataset issues 15分钟
Dataset optimization with generative AI 30分钟
Well-structured dataset 10分钟
Enhance dataset structure 10分钟
Using GenAI to optimize a given dataset 10分钟

Evaluate the ethical implications of data processing and synthetic data generation, developing strategies to mitigate biases and ensure responsible and transparent data practices.

涵盖的内容

10个视频6篇阅读材料4个作业

10个视频总计48分钟

The ethical minefield of data 3分钟
The ethics of data collection 6分钟
Anonymization and pseudonymization techniques 7分钟
Bias and fairness in data 2分钟
Real-world examples of bias in AI 6分钟
The ethical AI assistant: Navigating the moral maze 2分钟
Generative AI and manipulated media: The ethical challenges 7分钟
Demo: Explainability and transparency in GenAI models 7分钟
The ethical compass of GenAI 2分钟
Creating a code of ethics 6分钟

6篇阅读材料总计60分钟

Data ownership and control 10分钟
Data privacy regulations: A global perspective 10分钟
Algorithmic Discrimination: Unmasking Bias in AI Systems 10分钟
Mitigating bias and promoting fairness: Best practices 10分钟
The ethical implications of synthetic data 10分钟
A framework for ethical data processing 10分钟

4个作业总计60分钟

Developing a code of ethics 30分钟
Identifying ethical concerns in data collection and privacy 10分钟
Impact of bias in data and algorithms 10分钟
Responsible use of generative AI 10分钟

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已于 Mar 20, 2025审阅

Please include more practical on synthetic data generation, data cleaning and feature engineering. It was hell lot of theories especially from Ethical considerations of GenAI.

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