Machine Learning and data science have become the most outgrown sectors in the current phase, API plays a crucial role in data science so this article will comprehensively cover why you require API for data science.
Data science software allows data scientists to discover analytics, data mining, machine learning, and predictive models. To train all these models the developer requires API to fetch the data to model and create neural networks just like the human brain.
What is an API and Data Science?
API stands for application programming interface which acts as a bridge between database and interface which fetches data to the application and provides seamless data transfer for users.
On the other hand data science is the most talked about concept in the current phase due to the emergence of AI and Machine Learning.
Data science consists of various sectors such as data mining, machine learning, and data analysis. Basically, it is related to extracting meaningful insights from data for various purposes.
So now let us understand the usage of API for data science (Machine Learning).
Importance of API in Data Science and ML
The first step of developing a machine Learning model is to develop the model. Either we could create our own data or deploy the data from somewhere the process of creating the data and deploying manually would be more hectic and waste more time.
On the other hand, deploying the data from somewhere not only saves time but trains the model faster and becomes more natural.
The deployment of data is done by an API because API provides seamless data transfer and saves a lot of time scraping data manually. The data is extracted in JSON format which can easily be converted to dataframe () function.
How To Fetch Data From API For Data Science
Let’s take an example of NewsData.io News API and understand how you can fetch it to a machine learning model.
First, you need to have a personalized API key by signing up to NewsData.io and subscribing to one of the plans.
Then make your first request by using different parameters such as crypto, news sources, news, and news archive. To understand more briefly about the parameters you can visit the blogs.
So for an example let’s take up this query parameter
https://newsdata.io/api/1/news?apikey=YOUR_API_KEY
Which will fetch all the news articles of the past 48 hours. Once you retrieve the data you have to pre-process the data to filter out the unrelated data which can affect the output of the machine model.
After processing data into structured data the data is then inserted into the learning algorithm.
If we talk about BERT it uses the transformer package that learns the contextual relationship between words. Since the use of BERT is to generate a language model the use of an encoder becomes necessary.
Without any data to fetch the model it would just be a framework and providing quality and structured data is also necessary to get the maximum quality result.
Machine learning is just like training a child to speak and write so data quality matters a lot it is really important to use API for data science and the quality has to be ensured by data scientists.
Conclusion
API is a constantly evolving thing and saves a lot of time for developers to start from scratch nowadays it has a major impact on the field of data science and the usage of API for data science has increased.
The emergence of AI and Machine Learning has paved a new way for how we analyze and interact with data.
Frequently Asked Question
Q1. What is API in data science?
Ans. API plays a crucial role in data science, especially in machine learning. The models requires the textual data to understand the relationship of words and cause and effect. This data is derived through an API. So it is necessary for data scientists to use API for data science.
Q2. What is an API in ML?
Ans The API streamlines the process of learning and training the data for the Machine Learning framework. It provides the necessary data to build the Machine Learning model.
Q3. Is data science a coding?
Ans Yes coding is the essential part of data science with R and Python being the most prominent tools.
Greetings, I’m Akriti Gupta, a recent graduate from Delhi University. My pursuit in life revolves around an insatiable curiosity to explore and acquire new knowledge, fostering personal growth while nurturing a sense of compassion and goodness within me. Among my passions, painting, calligraphy, doodling, and singing stand as the cornerstones of my creative expression. These hobbies not only serve as outlets for my imagination but also as mediums through which I continually learn and evolve.
