Startups, as well as enterprises, simultaneously face the challenge of scaling their data science teams. More products and features for existing or new customers need to be rolled out in no time. And a large part of this challenge rides on the availability of appropriate data sources in APIs form.
In any industry, technology is always changing and the latest tool for analysis. Data scientist is no exception. There are many useful tools for data scientists and one of them is APIs. A number of companies provide APIs (like Twitter or Facebook) and it’s important to know how to use these APIs when you are building data products or doing Data Science.
In this post, we will look at the top 10 most popular and commonly used APIs for data scientists. This will help them get more authenticated about technology and their domain of work!
List of APIs for Data Scientists:
- FMP API – Advance tool for financial data collection
How do you find reliable data to help make informed decisions when trading stocks, or investing in the stock market? For this, you need an API that provides accurate and up-to-date data. The FMP API, for example, is one of the greatest APIs available, since it allows you to get to the heart of financial data. It provides you with a comprehensive and enriched catalog of options that facilitate your smart research on stocks, real-time stock rate, and more. For the best financial data analysis, this API works effectively in all programming languages. You can use it for free and experience a premium time fetching financial data.
- Reddit API – Go with social world trends
One of the top APIs that any data scientist should know is the Reddit API. Reddit has created an API in a very friendly way that allows you to be able to retrieve data that you may need to run machine learning algorithms on, as well as provide a way to build your datasets.
Reddit, known as the “front page of the internet,” is an online forum that hosts user-generated content. A Reddit API lets developers easily tap into all of the data on Reddit. If you have a project or ideas that involve creating summaries and finding trends in unstructured text data, Reddit API is a great choice for building a dataset.
- Zillow API – Real estate data master
If you’re a data scientist and are looking for some data to be manipulated and analyzed, Zillow has an API that lets you pull housing information on millions of homes across the country. It’s a fun API to use and learn from if you want to get into real estate development or play with some housing data.
The API offers a variety of data feeds and datasets with unique data, particularly on residential property values and sales.
Including this and much more, Zillow API is quite popular among the data science community.
- Google Maps API – Calculate distance, routes, and locations
Whether you’re looking for addresses and calculating distances between locations, or you want to find the shortest route between two destinations, it’s important to know some of the best APIs from Google Maps.
- Spotify API – Gather songs related customer data
Spotify is a great API not just because it’s easy to use, but also because it allows you to do a lot of tasks.
The Spotify API provides you with the tools needed to gather all this valuable data and more. It’s super simple to query the API and there are a ton of examples available to help you learn how to manipulate data.
- Amazon Machine Learning API – Best for customer awareness
Are you developing a data science tool or a project which requires strong customer analysis? Then Amazon Machine Learning API is one of the best APIs you can use.
Amazon’s Machine Learning API is a good option for those who are starting out. It’s easy to implement and has a minimal monthly fee.
It is a cloud-based machine learning service that makes it easy for developers of all skill levels to create powerful, efficient machine learning platforms.
- IBM Watson Discovery API – Convert text into speech
The IBM Watson Discovery API is one of the best APIs on this list. Using this API, you can convert text into speech, determine how a message resonated with a particular audience, model users based on specific social characteristics, and answer frequently asked questions in real-time.
All of these are done by analyzing natural language input.
If you have ever wanted to convert text into speech and make your own voice assistant then the IBM Watson Discovery API is for you.
- Yummly API – Foods and recipes collector
When your company seeks to be different and innovative in its field, there is one aspect most people will always look to: data. A data scientist is an individual that can take an idea and move it from the idea phase into something more viable.
The Yummly API is so awesome, I’m going to give it two top ten slots. Yummly collects recipes from around the web and compiles them into their API. This API allows you to find new recipes, analyze them, and create new ones.
- New York Times API – Latest and trendy news data
The New York Times API is a great starting point if you’re looking for data to use in your next data science project. It covers the latest news as well as historical information. This can be useful for your personal blog or start-up idea.
Not only does it have a good amount of data on offer, but you can search through the archives and filter with facets. The possibilities are endless. There’s also a ton of information – more than 600,000 articles to be exact – which can keep you occupied for a while.
- Crunchbase API – Window to the world of startups
The Crunchbase API is a tool that helps check data consistency, validate the existence of data, and get related data using a simple REST interface.
Originally created in 2001 to power their own data analysis tools, Crunchbase has become a comprehensive database of startups, providing industry-specific information on hundreds of thousands of companies within 60 industries.
Using this API allows you to access a wealth of public and private company data from all around the world.
With more and more startups focused on data science problems and attacking large market opportunities, the need for a strong partnership between the two entities is now greater than ever. No business will succeed in the data-driven future if it does not have a powerful, analytical backbone. And most of these companies are trying to get their hands into this lucrative new wave of products and services. As a result, APIs to sources of high-quality data (social, transactional, media, or other) become just as important as having well-defined business goals.