Data Analyst interviews will typically entail both technical and behavioral components

Source: Photo by Tobias Fischer on Unsplash

I was presented with a SQL challenge that was relatively straight forward, but included a comment that the columns in the challenge’s hypothetical tables were Slowly Changing Dimensions Type 2. I had no idea what these were but fortunately it was a take home challenge so I had the opportunity to research it and be successful on the challenge and the interview process. Beyond knowing the impact on my coding challenge, I was also curious as to why this would be important to the role of Data Analyst.

When I made my career transition by attending a Data Science bootcamp…

It is common for data scientists to spend a majority of their time exploring and cleaning data, but approaching this as an opportunity to invest in your model (instead of viewing it as just another chore on your to-do list) will yield big dividends later on in the data science process.

Performing thorough exploratory data analysis (EDA) and cleaning the dataset are not only essential steps, but also a great opportunity to lay the foundation for a strong machine learning model.

I used the Ames housing data set to predict home sale prices given a diverse range of features. In…

My interest in “data science” dates back to graduate school at NYU, before I knew what data science was, much less that it was a thriving industry with a multitude of opportunities.

I received my Masters in Public Administration with and focus in finance from NYU with my goal when I started the program to work in investment banking for public entities or as a credit analyst at one of the big ratings firms.During graduate school, a sizable portion of the course work was in statistics and my favorite course was “Multiple Regression and Econometrics.” …

Nick Vega

Data Scientist. Problem Solver. Solutions Advocate. Finance Nerd.

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