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Python Data Analysis E– commerce Sales & Portfolio Project

Name: Python Data Analysis E– commerce Sales &amp; Portfolio Project
Author: Baturi

Master data cleaning, manipulation, and visualization to solve a real e-commerce business problem in 1.5 hours.
What you'll learn
Load and inspect real-world e-commerce sales datasets into Pandas DataFrames
Identify and handle common data quality issues like missing values and duplicate records
Manipulate and filter sales data to select specific insights and prepare for analysis
Perform powerful data aggregation using Pandas to summarize sales trends and product performance
Extract key business insights by analyzing customer orders, product popularity, and revenue drivers
Create fundamental data visualizations using Matplotlib and Seaborn to effectively communicate findings
Build a practical, portfolio-ready project by applying data analysis skills to solve a real business problem
Requirements
Basic understanding of Python (variables, data types, python libraries).
Description
Want to bridge the gap between Python basics and real-world data work?This hands-on course is your fast track to solving business problems with data—quickly, practically, and professionally.This course is designed for maximum efficiency, delivering a complete data analysis project in just 1.5 hours. We've trimmed the fat to give you a highly focused, hands-on learning experience that respects your time. You'll walk away with a portfolio-ready project that demonstrates not just what you know—but what you can do.Fast-Track Your Skills: Learn to clean, analyze, and visualize a real-world e-commerce dataset with Python in a highly streamlined format. Get straight to the core concepts without any unnecessary filler.Build Your Portfolio: Each section includes a practical assignment with a comprehensive solution. We highly recommend you complete these, especially the final portfolio project, which challenges you with a variety of advanced business analysis problems.Test Your Knowledge: Reinforce your learning with two practical tests that are highly recommended for every student. These are not just quizzes; they're an excellent way to refresh, reboot, and solidify the knowledge and coding skills you learn in the lectures.Get Your Questions Answered: Our teaching team is committed to your success. We encourage you to engage with the Q&A section, and we will respond to your questions as quickly as possible to keep your learning on track.What You'll LearnLoad & inspect real-world data using PandasClean messy datasets: fix missing values, duplicates, data typesManipulate & aggregate dаta: filter, group, summarize like a proVisualize insights with Matplotlib and SeabornSolve a business problem using data storytellingNo fluff—just applied, end-to-end analysis you can showcase to employers or clients.Why This Course?Unlike theory-heavy classes, this course goes narrow and deep—solving one problem from start to finish. It's designed to build practical skills, not just passive knowledge.Whether you're switching careers or leveling up, you'll leave with a real project that proves your ability to turn raw data into business value.Are there any course requirements?Basic Python skills (variables, loops, functions)Ability to install packages (pip, Jupyter, etc.)No prior experience with Pandas or visualization tools neededJoin now and start building your data portfolio today!
Who this course is for
Aspiring Data Analysts and Data Scientists: If you have basic Python knowledge and are eager to apply your skills to real-world data, this course is your practical launchpad into data analysis.
Students & Career Changers: If you're looking to build a compelling portfolio for entry-level data roles, this project-driven course will equip you with essential, in-demand skills in data manipulation and visualization.
Business Professionals & Researchers: Anyone who regularly works with data and wants to move beyond spreadsheets to perform more powerful, automated, and insightful data analysis using Python.
Anyone looking to make data-driven decisions: Learn to transform raw data into clear, actionable insights and compelling visual reports that can influence strategy and drive results.
Python Users Ready for the Next Step: If you've completed a "Python for Beginners" course and are now ready to learn how to effectively use Python's powerful data science libraries (Pandas, Matplotlib, Seaborn) to extract meaningful insights.
Homepage
https://www.udemy.com/course/python-data-analysis-e-commerce-sales-portfolio-project/

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Python Data Analysis E– commerce Sales & Portfolio Project

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