Build Your Data Analyst Portfolio with Expert Python EDA Skills π
Discover how to create a compelling Data Analyst Portfolio using Python Pandas for Exploratory Data Analysis. Join my free Skool community for exclusive resources, tools, and support in Data, Machine Learning, and AI Automation!
Ryan & Matt Data Science
93.7K views β’ Jul 10, 2023
About this video
π§ Donβt miss out! Get FREE access to my Skool community β packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! π https://www.skool.com/data-and-ai-automations-4579
In this video, we take a look at an Exploratory Data Analysis (EDA) portfolio project within Python Pandas. Everything is coded within Jupyter Notebook and the data is sourced from Kaggle.
Python Libraries needed: Pandas, Seaborn
Kaggle Data: https://www.kaggle.com/datasets/aiaiaidavid/the-big-dataset-of-ultra-marathon-running/discussion/420633
Code:
π Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/
π¨βπ» Mentorships: https://ryanandmattdatascience.com/mentorship/
π§ Email: ryannolandata@gmail.com
π Website & Blog: https://ryanandmattdatascience.com/
π₯οΈ Discord: https://discord.com/invite/F7dxbvHUhg
π *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan
π *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg
πΏ WATCH NEXT
Python Pandas Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4KvHRJ-awaxAPzFGdZ8yN6D
Python Data Cleaning: https://youtu.be/iaZQF8SLHJs
Python Pandas Groupby: https://youtu.be/L5kf4sQnVhI
Python Pandas Melt: https://youtu.be/XsYT6TD8nJc
OTHER SOCIALS:
Ryanβs LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/
Mattβs LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/
Twitter/X: https://x.com/RyanMattDS
Who is Ryan
Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF.
Who is Matt
Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One.
*This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.
In this video, we take a look at an Exploratory Data Analysis (EDA) portfolio project within Python Pandas. Everything is coded within Jupyter Notebook and the data is sourced from Kaggle.
Python Libraries needed: Pandas, Seaborn
Kaggle Data: https://www.kaggle.com/datasets/aiaiaidavid/the-big-dataset-of-ultra-marathon-running/discussion/420633
Code:
π Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/
π¨βπ» Mentorships: https://ryanandmattdatascience.com/mentorship/
π§ Email: ryannolandata@gmail.com
π Website & Blog: https://ryanandmattdatascience.com/
π₯οΈ Discord: https://discord.com/invite/F7dxbvHUhg
π *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan
π *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg
πΏ WATCH NEXT
Python Pandas Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4KvHRJ-awaxAPzFGdZ8yN6D
Python Data Cleaning: https://youtu.be/iaZQF8SLHJs
Python Pandas Groupby: https://youtu.be/L5kf4sQnVhI
Python Pandas Melt: https://youtu.be/XsYT6TD8nJc
OTHER SOCIALS:
Ryanβs LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/
Mattβs LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/
Twitter/X: https://x.com/RyanMattDS
Who is Ryan
Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF.
Who is Matt
Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One.
*This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.
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Video Information
Views
93.7K
Likes
3.2K
Duration
57:55
Published
Jul 10, 2023
User Reviews
4.7
(18)