My name is Shreyas Melanahalli, and I am a senior at BISV. I have added my senior project abstract below:
In company management, products need to be maximized and minimized in certain locations. This project addresses the importance of a net promoter score. A net promoter score is a scale – similar to a rating system that is typically out of 5 stars – used to indicate to a corporation which product should be maximized and where it ought to be implemented. With large corporations, they often lack the resources to conclude where to execute certain products, so they resort to their previous product. As time progresses, new products will inevitably set new trends. To help Cisco Systems Inc. – the place where I will be completing my project – I will be working with data scientists to efficiently calculate a net promoter score via python algorithms and effectively plot trends of these net promoter scores over a period of time. Through my project, I hope to further understand data science and help Cisco Systems with their Catalyst 3650 series – a networking switch which aids the travel of information from different parts of organizations to the desired location. This project is substantially important in keeping companies up to date with their products. We, as customers, do not want to constantly use old, outdated products when there are new products being manufactured. Alerting corporations of the demand for certain products in specific locations is vital for company growth and prosperity.
I chose this field to effectively categorize business data. Companies absorb enormous amounts of data daily, but this data is useless unless it is categorized into user-friendly terms. I am working with data scientists at Cisco Systems Inc. to, first, create a Net Promoter score and, second, categorize data efficiently. The company receives dozens upon dozens of data that, to even data scientists, look like gibberish. By categorizing the data for a particular product, scientists can give the product a rating from the net promoter score and make business decisions based on it – such as increasing productivity in specific regions, updating systems to make it better suited for customer needs, etc. This topic will allow me to explore the business and data science aspects of products that enter the market.
Working in an actual office has a more professional feel to it, and you are held accountable for every piece of data you work with. I hope to learn more about data science and how different simulations could affect the customer’s perspectives on products.
Thanks for reading!