Guided Project: Analyzing Startup Fundraising Deals from Crunchbase

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About this Webinar
In this hands-on Project Lab, Dataquest’s Senior Content Developer, Anna Strahl, 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.
Key Skills Covered in This Project:
Optimizing pandas memory usage
Processing large datasets with chunking
Loading and querying data with SQLite
Filtering and aggregating data with pandas and SQL
Extracting insights from startup fundraising data
Note: This is a premium project that has been made available to all webinar participants at no cost from May 30 to June 6.
To get the most out of the session, we recommend you review our Processing Large Datasets in pandas course beforehand.
Before You Start: Pre-Instruction
To make the most of this project walkthrough, follow these preparatory steps:
1. Review the Project 
 Access the project and familiarize yourself with the goals and structure: 
Start the project here
2. Access the Solution Notebook:
You can view and download it here to see what we’ll be covering:
Solution Notebook
Helpful Tips
New to Markdown? We recommend learning the basics to format headers and add context to your Jupyter notebook: Markdown Guide.
For file sharing and project uploads, it is important that you create a GitHub account ahead of the webinar: Sign Up on GitHub.
Want to work offline?
1. Set Up Your Workspace
We'll work with a basics.ipynb file, which can be rendered in the following tools:
Jupyter Notebook (local installation required)
Google Colab (browser-based, no installation needed)
2. Download the Resource Files The dataset of startup investments we'll be exploring is from October 2013 and can be downloaded from GitHub here.