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[DataFramed Careers Series #2] What Makes a Great Data Science Portfolio

Today marks the second episode in our DataFramed Careers Series. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of landing a data role in 2022.
mai. de 2022

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Guest
Nick Singh

Nick Singh started his career as a Software Engineer on Facebooks' Growth Team, and most recently, worked at SafeGraph, a location analytics startup. He graduated from the University of Virginia with a degree in Systems Engineering, and a minor in Computer Science and Applied Math. In college, he interned at Microsoft and on the Data Infrastructure team at Google's Nest Labs. He is the co-author of Ace the Data Science Interview.


Photo of Adel Nehme
Host
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Takeaways

1

Great portfolio projects are driven by genuine interest, value simplicity over complexity, and make a quantifiable impact.

2

Provable experience is needed even for entry-level jobs in data science, making portfolio projects vital for aspiring data scientists.

3

Quantifying your portfolio project’s impact is the key to getting noticed by recruiters and hiring managers.

Key Quotes

The key to standing out is to show your project made an impact and show that other people cared. Why are we in data? We're trying to find insights that will drive business that actually impacts a business, or we're trying to find insights that will actually shape society, or create something novel. We're trying to improve profitability or improve people's lives using and analyzing data, so if you don’t somehow quantify the impact, then you are lacking impact.

I've coached so many people and I've just seen so many smart people say, ‘I'm going to do deep learning on this topic.’ But, if you can even just get the data set on your computer and store it in a database, that would already be pretty impressive. For bigger data sets, you already have to write Cron jobs and you have to write some basic SQL to analyze a dataset, so forget about deep learning. Often when people look at what they think is cool, they over-complicate things. Instead, make it simple and easy, and almost make it a little stupid. Let's be honest, if all you did was make a cool data set and upload it on Kaggle, that could get traction on its own.

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