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For this project, you’ll work for a data science content company. Using SQL and Python, you’ll analyze Data Science Stack Exchange data to identify the most popular topics and guide the creation of in-demand content.

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4.8 (359 reviews)

366,013 learners enrolled in this project

Intermediate
Jupyter project
11 steps
Python

“The projects were really helpful because they provided a way to see how all the things I learned work.”

Alejandro Giraldo Riveros

Environment Engineer & Data Analyst

Project overview

In this project, you’ll take on the role of a data analyst at a company that creates data science educational content. To decide what material to produce, you’ll analyze data from Data Science Stack Exchange to determine the most popular and in-demand topics.

You’ll query the Data Science Stack Exchange database using SQL to extract relevant data. Then you’ll use Python to clean, analyze and visualize the data to identify topic trends. Through this process, you’ll develop valuable real-world data analysis skills as you work with messy data, explore insights and make data-driven recommendations on what content the company should prioritize creating to meet demand. You’ll strengthen your SQL querying skills, enhance your Python data analysis and visualization abilities, and gain experience in leveraging data to inform content strategy.

Objective: Analyze Data Science Stack Exchange data to identify the most popular data science topics and guide the creation of high-demand educational content.

Key skill required

To complete this project, it's recommended to build these foundational skills in Python

Resolving ambiguous language to clarify analysis requests
Communicating data analysis results to non-technical business audiences
Defining and evaluating business metrics to track performance
Identifying how data science fits into the overall business context and workflow

Projects steps

Step 1: Introduction

Step 2: Stack Exchange

Step 3: Stack Exchange Data Explorer

Step 4: Getting the Data

Step 5: Exploring the Data

Step 6: Cleaning the Data

Step 7: Most Used and Most Viewed

Step 8: Relations Between Tags

Step 9: Enter Domain Knowledge

Step 10: Just a Fad?

Step 11: Next Steps

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Alejandro Giraldo Riveros