AI-Powered Data Analysis: A Practical Introduction

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AI-Powered Data Analysis: A Practical Introduction

Instructor: Tina Lasisi

4,584 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

23 reviews

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

23 reviews

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Learn principles of data analysis
Learn how to use generative AI to come up with data analysis ideas
Learn how to use generative AI to create functional data analysis code
Learn how to share data analysis projects on GitHub

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

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There are 3 modules in this course

As generative artificial intelligence (AI) reshapes our world, the ability to analyze data is quickly becoming as fundamental as reading and writing. “AI-Powered Data Analysis: A Practical Introduction” explores how AI tools like ChatGPT are revolutionizing our approach to data, making advanced analysis accessible to everyone. Whether you're a complete novice or looking to enhance your skills, you'll learn how to navigate this new terrain.

You'll learn to think critically about the context of data analysis, delve into the specifics of analyzing and visualizing data using AI, and consider broader factors that support but are not directly part of data analysis. This practical approach focuses on generative AI tools, ensuring you know how to ask the right questions to avoid common mistakes. Your final activity will allow you to set yourself up for continued learning with a prepared Python environment and data sets, which you can voluntarily showcase on GitHub—a code-sharing hub. By the end of this course, you'll be adept at using AI tools to analyze data effectively and seamlessly apply these skills to future projects.

This module teaches learners to understand and define data, utilize Generative AI for data acquisition, and effectively navigate various data formats and sources. Gain hands-on experience with real-world datasets.

What's included

4 videos6 readings1 assignment

4 videos Total 12 minutes

Welcome to Course 3 minutes
What Even is Data, Anyway? 3 minutes
Data Acquisition and GenAI 4 minutes
Data: Contents & Containers 1 minute

6 readings Total 41 minutes

How GenAI is Used in this Course 3 minutes
Generative AI Options 8 minutes
Course Syllabus 5 minutes
Help Us Learn About You! 5 minutes
Brainstorming and Searching with GenAI 10 minutes
Data Vocabulary 10 minutes

1 assignment Total 10 minutes

Module 1 Quiz 10 minutes

This module equips learners with fundamental data analysis skills, including selecting appropriate tools, cleaning and organizing data, performing statistical analyses, and creating visualizations, all while leveraging generative AI to enhance their capabilities.

What's included

6 videos5 readings1 assignment1 ungraded lab

6 videos Total 19 minutes

What is Data Analysis? 3 minutes
Tool Diversity 4 minutes
Data Types and Structures 4 minutes
Data Wrangling 1 minute
Data Analysis 2 minutes
Data Visualization 4 minutes

5 readings Total 50 minutes

Step 0 Context and Setup 10 minutes
Data Wrangling Examples with GenAI 10 minutes
Data Analysis Examples with GenAI 10 minutes
Data Visualization Examples with GenAI 10 minutes
Introduction to Jupyter Labs 10 minutes

1 assignment Total 10 minutes

Module 2 Quiz 10 minutes

1 ungraded lab Total 30 minutes

Module 2 Lab 30 minutes

This module equips learners with essential support skills such as version control, utilizing generative AI for technical assistance, and applying their data analysis knowledge to practical, domain-specific projects.

What's included

2 videos3 readings1 assignment1 ungraded lab

Instructor

Instructor ratings

(7 ratings)

Tina Lasisi

University of Michigan

1 Course 4,584 learners

Offered by

University of Michigan

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Reviewed on Oct 20, 2024

Reading content is a bit too much, instructor is sophisticated and veteran in data processing and ai manipulation.

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