Data Analysis Projects with Python

From logging into your Facebook account to buying a new iPhone, it’s all backed up somewhere. We have so much data today that it’s used everywhere today, for example, to help a business understand a client’s needs or to help a dating app find the perfect fit for you. So when we analyze a large amount of data to find relationships between different characteristics, it is nothing more than data analysis. In this article, I will introduce you to some of the best data analysis projects with Python, that you can try as a beginner.

Data Analysis Projects with Python

WhatsApp Chat Analysis:

WhatsApp is one of the most used messenger applications today with more than 2 Billion users worldwide. It was found that more than 65 billion messages are sent on WhatsApp daily so we can use WhatsApp chats for analyzing our chat with a friend, customer, or a group of people. You can use your WhatsApp data for many data science tasks like sentiment analysis, keyword extraction, named entity recognition, text analysis and several other natural language processing tasks. 

It also depends on who you are analyzing your WhatsApp messages with because you can find a lot of information from your WhatsApp messages which can also help you to solve business problems. You can find a data analysis project on WhatsApp chat analysis with Python from here.

Covid-19 Vaccines Analysis:

There was a time when Covid-19 got out of hand. Even after the lockdown, this still resulted in a rapid increase in cases as in some countries cases were brought under control but the economy was sacrificed. In such a situation, only vaccines are seen as the only tool that can help the world fight covid-19. 

Many vaccines have been introduced so far to fight covid-19. No vaccine has guaranteed 100% accuracy so far, but most manufacturing companies claim their vaccine is not 100% accurate, but still, it will save your life by giving you immunity. Thus, each country tries to vaccinate a large part of its population so as not to depend on a single vaccine. That’s what I’m going to analyze in this data analysis project, which is how many vaccines each country is using to fight covid-19. You can find this project here.

Uber Trips Analysis:

Uber has been a major source of travel for people living in urban areas. Some people don’t have their vehicles while some don’t drive their vehicles intentionally because of their busy schedule. So different kinds of people are using services of Uber and other taxi services.

By analyzing Uber trips, we can draw many patterns like which day has the highest and the lowest trips or the busiest hour for Uber and many other patterns. The dataset I’m using here is based on Uber trips from New York, a city with a very complex transportation system with a large residential community. You can find this data analysis project here.

Google Search Analysis:

Approximately 3.5 billion searches are performed on Google daily, which means that approximately 40,000 searches are performed every second on Google. So Google search is a great use case for analyzing data based on search queries. Google doesn’t give much access to the data about daily search queries, but another application of google known as Google Trends can be used for the task of Google search analysis. 

Google Trends provides an API that can be used to analyze the daily searches on Google. This API is known as pytrends, you can easily install it in your systems by using the pip command; pip install pytrends. You can find this data analysis project here.


So these were some of the best data analysis projects with Python that you can try as a beginner. After working on these projects, if your next goal is to get your hands on data science and machine learning, you can find over 200+ projects here. Hope you liked this article on data analysis projects with Python. Please feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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