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

3,532 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

28 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

28 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

28 assignments

Taught in English

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Build your Data Analysis expertise

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
Earn a shareable career certificate from Microsoft

There are 5 modules in this course

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

What's included

10 videos8 readings6 assignments

10 videos Total 51 minutes

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

8 readings Total 75 minutes

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

6 assignments Total 100 minutes

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

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

7 videos Total 33 minutes

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

6 readings Total 60 minutes

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

6 assignments Total 115 minutes

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

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

6 videos Total 36 minutes

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

6 readings Total 60 minutes

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

6 assignments Total 95 minutes

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

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

7 videos Total 29 minutes

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

7 readings Total 70 minutes

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

6 assignments Total 90 minutes

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

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

10 videos Total 48 minutes

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

6 readings Total 60 minutes

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

4 assignments Total 60 minutes

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

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Instructor

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(6 ratings)

Microsoft

287 Courses 2,320,610 learners

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Microsoft

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Reviewed on Mar 20, 2025

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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