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There are 5 modules in this course
This foundational course provides a comprehensive understanding of generative AI and its applications in data analysis. You'll explore key tools and platforms, learn to integrate AI into existing workflows, and master prompt engineering for various data analysis tasks.
Upon completion of this course, you'll be able to:
Define generative AI and its role in data analysis
Integrate generative AI tools into existing data analysis workflows
Develop prompt engineering skills for data analysis tasks
Evaluate ethical considerations in using generative AI for data analysis
Develop a foundational understanding of what Generative AI is, its current applications in data analysis, and the key tools and platforms available.
What's included
7 videos8 readings4 assignments
Show info about module content
7 videos•Total 39 minutes
Welcome to generative AI for data analysis certificate•4 minutes
What is generative AI?•6 minutes
The creative potential of generative AI•6 minutes
Applying generative AI in data analysis•6 minutes
The field of generative AI•6 minutes
Microsoft Copilot for data analysis: A walkthrough•6 minutes
Integrating Microsoft Fabric/PowerBI/365 into workflows•6 minutes
8 readings•Total 80 minutes
Generative AI for data analysts certificate syllabus overview•10 minutes
Coursera Community: Introduce yourself and say hi to your classmates•10 minutes
A paradigm shift in data analysis•10 minutes
Case studies: How generative AI is transforming data analysis•10 minutes
GenAI Tools for Data Analysis: A Practical Guide•10 minutes
Choosing the right generative AI tool: A guide for data analysts•10 minutes
Power BI and Generative AI: A powerful combination•10 minutes
Best practices for integrating Generative AI into your data analysis workflow•10 minutes
4 assignments•Total 90 minutes
Defining generative AI and its impact•15 minutes
Tools and platforms for data analysis•15 minutes
Project: Data analysis challenge•30 minutes
Introduction to Generative AI•30 minutes
Data exploration with GenAI
Module 2•4 hours to complete
Module details
Integrate generative AI tools into their existing data analysis workflows to streamline data exploration, uncover insights, and enhance decision-making processes.
What's included
9 videos6 readings6 assignments
Show info about module content
9 videos•Total 47 minutes
Data exploration essentials: Key terms and concepts for analysts•5 minutes
The language of data: Visualizing key exploration concepts •2 minutes
How to ask effective questions: A guide to data exploration•5 minutes
Streamlining data exploration with generative AI tools•6 minutes
Demo: Data exploration with Microsoft Copilot•6 minutes
GenAI for data exploration: Best practices and tips•6 minutes
How to craft effective queries for generative AI•6 minutes
Demo: Refining prompts for better insights•6 minutes
Evaluating insights from generative AI: Separating facts from claims•6 minutes
6 readings•Total 60 minutes
Data exploration: An introduction for data analysts•10 minutes
Asking the right questions: A guide to data exploration•10 minutes
Generative AI for data exploration: New possibilities•10 minutes
Case study: Data exploration with generative AI•10 minutes
Assessing the validity of generative AI insights: A framework for data analysts•10 minutes
Challenges and limitations of generative AI in data exploration•10 minutes
6 assignments•Total 105 minutes
Key terms and questions in data exploration•15 minutes
Leveraging Generative AI for data exploration•15 minutes
Activity: Data detective: The first look•15 minutes
Analyzing a dataset with GenAI•15 minutes
Activity: Data detective: Uncover the clues•15 minutes
Data exploration with generative AI•30 minutes
Data cleaning with GenAI
Module 3•3 hours to complete
Module details
Apply generative AI tools to investigate and summarize datasets, and identify patterns and relationships, and formulate hypotheses for further analysis.
What's included
7 videos8 readings6 assignments
Show info about module content
7 videos•Total 35 minutes
GenAI's role in uncovering inconsistencies•6 minutes
Demo: Automatic inconsistency flagging•6 minutes
Visualizing data inconsistency: A guided tour•2 minutes
Decoding GenAI reports: Understanding the impact of data inconsistencies•6 minutes
From report to action: Making informed decisions with GenAI insights•6 minutes
The outlier effect: How GenAI helps spot and address anomalies•3 minutes
Data transformation fundamentals: Why it matters for accurate analysis•6 minutes
8 readings•Total 80 minutes
The data cleaning dilemma: How GenAI offers solutions•10 minutes
Beyond the basics: Advanced data cleaning techniques with GenAI•10 minutes
Data quality is key: Using GenAI to prioritize cleaning efforts•10 minutes
The ethics of automated data cleaning: Balancing efficiency and responsibility•10 minutes
The art and science of missing data: GenAI as your assistant•10 minutes
Beyond removal: When outliers are insights, not errors•10 minutes
The data transformation toolkit•10 minutes
Feature engineering in the age of GenAI: A balanced approach•10 minutes
6 assignments•Total 90 minutes
Identifying and categorizing data inconsistencies•15 minutes
Evaluating impact and making decisions•15 minutes
Imputing missing values and handling outliers•15 minutes
Project: Unveiling Data Inconsistencies with GenAI•15 minutes
Project: Addressing data inconsistencies by cleaning your dataset with GenAI •15 minutes
Data Cleaning with GenAI•15 minutes
Prompt Engineering Fundamentals
Module 4•4 hours to complete
Module details
Develop the skills to create and optimize effective prompts for diverse data analysis tasks, enhancing the quality and efficiency of data cleaning, analysis, and visualization outcomes.
What's included
6 videos9 readings7 assignments
Show info about module content
6 videos•Total 28 minutes
The prompt makeover: From vague to valuable•3 minutes
Improving your prompts: Strategies for refinement•5 minutes
Sculpting your prompts: The art of iterative refinement•3 minutes
Learning paradigms in GenAI: Using examples effectively•5 minutes
Zero-shot, one-shot and few shot learning•7 minutes
Effective prompting: Using the RACE and PARE frameworks•5 minutes
9 readings•Total 90 minutes
The art of prompting: A guide for data analysts•10 minutes
Prompt gallery: Real-world examples for inspiration•10 minutes
Iterative prompt refinement: A data analyst's secret weapon•10 minutes
Prompt engineering cheat sheet: Quick tips for data analysts•10 minutes
Zero-shot, one-shot, and few-shot learning with generative AI•10 minutes
Practical applications of learning paradigms in data analysis•10 minutes
Discussion: Choosing the right learning paradigm•10 minutes
Prompting with purpose: The RACE framework for data analysis•10 minutes
Using the PARE framework to ensure quality insights•10 minutes
7 assignments•Total 115 minutes
Crafting effective prompts•15 minutes
Optimization techniques•15 minutes
Zero-shot, one-shot, and few-shot learning•15 minutes
Applying the RACE framework•15 minutes
Activity: Prompt refinement•15 minutes
Project: Evaluating with PARE•10 minutes
Prompt engineering fundamentals•30 minutes
Ethical and Safe Use of GenAI
Module 5•4 hours to complete
Module details
Evaluate and apply ethical, legal, and practical considerations in utilizing generative AI for data analysis, ensuring responsible application and risk mitigation.
What's included
7 videos7 readings6 assignments
Show info about module content
7 videos•Total 36 minutes
Addressing ethical issues in generative AI: Bias, privacy, and misuse•6 minutes
Garbage in, garbage out: How bias persists in GenAI•3 minutes
AI as a double-edged sword: Power, responsibility, and consequences•3 minutes
The role of an AI ethicist: Balancing innovation and responsibility•6 minutes
Ethical challenges: Addressing issues in generative AI•6 minutes
Built-in safeguards: Comparing GenAI tools for ethical use•6 minutes
Why your organization needs custom ethical AI guidelines•6 minutes
7 readings•Total 70 minutes
The ethical landscape of generative AI: A data analyst's perspective•10 minutes
Ethical dilemmas in action: Case studies from the data analysis field•10 minutes
Beyond compliance: Why responsible AI is a competitive advantage•10 minutes
Building an ethical AI framework: A guide for data teams•10 minutes
Before you deploy: Evaluating GenAI use cases for ethical risks•10 minutes
Implementing ethical GenAI: Best practices for data teams•10 minutes
The future of ethical AI: Challenges and opportunities for data analysts•10 minutes
6 assignments•Total 115 minutes
Identifying ethical concerns•15 minutes
The importance of responsible AI•15 minutes
Evaluating GenAI use in scenarios•15 minutes
Activity: Bias in generative AI•10 minutes
Project: Ensuring ethical use for a Data Analysis scenario•30 minutes
Ethical and Safe Use of GenAI Assessment•30 minutes
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