Optimize Large Datasets with Pandas: Full Project Walkthrough in Python & SQL

In this video, we will guide you through how to analyze startup investment data using Python and SQLite to uncover key fundraising trends.

You’ll step into the role of a data analyst working with Crunchbase data, where you’ll learn how to efficiently work with medium-sized datasets by optimizing memory usage, processing data in chunks, and loading it into a SQLite database for analysis. By the end of the session, you’ll be able to extract insights into which startups get funded and which investors are most active. [This project is ideal for learners with basic Python and SQL experience who are looking to build confidence working with larger datasets in real-world scenarios.]

What You'll Learn:
- Data Optimization: Choose the right data types and process large files in manageable chunks.
- SQLite Integration: Store and query large datasets efficiently using SQLite.
- Real-World Data Handling: Clean and prepare investment data for analysis.
- Fundraising Trend Analysis: Uncover patterns in startup deals and investor activity.
- pandas-SQLite Workflow: Combine tools to analyze data at scale.

Recommended Prerequisites:

Video Chapters:
Project brief - 1:24
Reading and exploring the dataset - 3:02
Cleaning and dropping columns - 17:08
Optimizing Memory Usage with Data Types - 20:24
Loading Data into SQLite - 33:14
Analyzing and Visualizing with SQL - 35:40
Audience Q&A - 42:04

#PythonProject #Pandas #DataEngineering #SQL #DataAnalysis

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