Startup Insights: Uncovering Trends in the World of Entrepreneurship through SQL Analysis

INTRODUCTION

In today's fast-paced world, startups are growing at an unprecedented rate, and their contributions to the economy are becoming increasingly significant. However, managing information on startups is a complicated task due to the enormous amount of data generated by these organizations. Fortunately, tools such as SQL are highly effective at parsing through and analyzing large datasets, making it easier to analyze and compare data from a broad range of sources.

In this SQL project, I will be analyzing CrunchBase Startup profile data, a database consisting of information on thousands of startups worldwide. With the help of SQL queries, we will explore various questions such as;

• What are the top industries by funding amount, and how have they changed over time?
• Which country has the most funding for startups?
• What is the correlation between the amount of funding received and the number of employees?

By answering these questions, we will gain invaluable insights into the trends and patterns that exist within the startup ecosystem.

DESCRIPTION OF DATASET AND TOOLS USED

The dataset is available on the CrunchBase website and it contains 32 columns and 21,529 rows. An overview of the dataset is shown below;

The analysis of the dataset was carried out using PGAdmin 4 tool in the PostgreSQL package and Jupypter notebook.

DATA PREPARATION

The data preparation phase is where we attempt to understand the data. It might require cleaning, transformation, and integration. Data Analysis is not magic and it only works when a problem which needs to be solved, is represented as accurately as it can be from the real world. One of the most important and vital tasks of Data Analysis is cleaning and preparing the data.

I tried to maintain the integrity of the data as much as I could, but I removed columns that were irrelevant to my analysis. After plenty deliberation I deemed only about 19 column necessary in my analysis. After I am satisfied with the quality of data I have, I proceeded to create a table in my database using PGAdmin 4.

EXPLORATORY DATA ANALYSIS

Exploratory data analysis (EDA) is used by data scientists to analyse and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.

1.   To get an initial understanding of the startup profiles in the database, I displayed the first ten rows of the table. This gave me a glimpse of the data available and helped me to identify any potential issues or patterns.
   

2. Now that I had a grasp of the data, I decided to count the number of small companies in the dataset. I defined small companies as those with estimated revenues less than $1M.

3. To gain insights into the most recent funding rounds of the startup profiles present in the database, I retrieved the five organizations with the highest total funding amount. This helped me identify the most recently and heavily funded startups in the dataset.

4. To find the top 5 organizations with the highest number of founders in the dataset, I used this query. It helped me identify which startups had the largest founding teams, and analyse how the size of a founding team relates to a startup's success and growth potential.

5. To understand the distribution of funding amounts across different funding statuses, I ran a query to group the total funding amounts by funding status. This information helped me understand which funding statuses are the most popular among startups in our dataset, and how much funding they typically receive.

6. To identify the organizations with the highest number of acquisitions, I ran a query that filtered out null values for the number of acquisitions column, and ordered the data by the number of acquisitions in descending order. This information helped me understand which organizations are most active in acquiring other companies and which industries are likely to be targeted by these acquirers.

Conclusion

This project demonstrates the power of SQL in organizing and extracting valuable insights from large datasets, providing entrepreneurs, investors, and analysts alike with the tools needed to make informed decisions.

Here is the link to the full project