Adding value as a Data Scientist: Utilizing Docker, FASTAPI, and AWS Beanstalk to solve real world issues in Rwanda. Adding value through Data Science and Data Engineering.
For my final project at Lambda School I was put into a cross-functional team that would work directly with Bridges To Prosperity, a non-profit focused on building footbridges over impassable rivers in isolated communities to create access to essential health care, education and economic opportunities. The problem Bridges To Prosperity faced concerned their engineering department: the engineers spent an inordinate amount of time reviewing bridge sites, that in some cases need immediate attention, before being able to make a decision regarding whether to build at the site. Therefore, our goal was to build a product that could assist the engineers in the review stage.
We decided to build a web application that would allow all stakeholders including the engineers to evaluate the sites on an interactive map. Our team compromised of two front-end developers, two back-end developers, and three data scientists. The deliverables for Data Science were the following: create a classification model that can accurately identify engineering reviews that were false negatives, clean data sets for stakeholders, and provide interactive visualizations to better understand the data.
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
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In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines. In this post, we'll learn Data Science vs Big Data: Difference Between Data Science & Big Data.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Your Data Architecture: Simple Best Practices for Your Data Strategy. Don't miss this helpful article.