Introduction
To present the results of Machine Learning experiments to users and customers, data scientists must quickly develop a Data Science App.
With the help of the Python Pandas library, we extracted data from the stock exchange using the InvestPy Python Library. We then processed the data into interactive candlesticks using Python's Plotly library; we processed this data into interactive candlesticks.
As a result, the Data Scientist can customise the Data Science App for new business models based on this article.
The purpose of this article is to serve as a guide for the Junior Data Scientist to implement a Data Science App in an agile manner, although it can be extended to a more complex app in the future.
Front-end application using traditional techniques
In the browser, any application using web front-end technology runs scripts based on JavaScript, HTML, and CSS.
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JavaScript controls the logic and user flow of a web page. CSS is responsible for styling information, while HTML is responsible for its content.
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Due to the lack of mastery of these front-end technologies by Data Scientists, alternative frameworks to Python are emerging for implementing a Data Science App front-end without learning JavaScript, HTML, or CSS.
Data Science Frontend App for StreamLit
This experiment is intended for Data Scientists to see the potential of Streamlit to demonstrate the feasibility of a Data Science application.
The first step is to extract data from the stock exchange through the Python InvestPy library, where we can select the US or Brazilian stock exchange and the asset to search for, such as Google, Apple, or Facebook.
In addition, we indicate the start and end dates of the data analysis and the frequency of the analysis, which can be daily, weekly, or monthly. Monthly. By presenting information on the median, minimum, maximum, and 25% and 75% quartiles, candlesticks help visualise the data based on its frequency. Visit the Data science course in Delhi for more information.
Financial Market Library InvestsPy
InvestPy is a Python library created by the investing.com portal that retrieves real-time data from approximately 40,000 shares traded on various stock exchanges and 80,000 investment funds, indices, bonds, commodities, and cryptocurrencies.
Thanks to this Python library, the Data Scientist has access to a vast collection of the world's major markets.
Frontend Data Science App Shares Stock Exchange
The Python code below is based on the StreamLit Framework, and when we run the App, we have an efficient web application available.
StreamLit.io Alternatives for Data Science App
The Data Scientist can choose from the following Data Science Apps:
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Streamlit.io is a simple implementation that is not very scalable.
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Django
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Complex implementation that is highly scalable
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APIs with strong access control and authentication
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IoT Support
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Flask
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Complex implementation that is highly scalable
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APIs with strong access control and authentication
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IoT Support
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Voilà
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Easy to implement but not very scalable
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Jupyter Notebook integration
Conclusion
The Data Scientist will be able to select the best alternative for a Data Science Web application after reading this article. If you’re passionate about data science and want to make a career shift, check out the Data science certification course in Delhi, and become a data scientist in MAANG Firms.
