So far, I have covered various topics involving data collection, data mining, data processing and machine learning. While all of these capabilities are important in the role of a data scientist, it is important to know how to present your findings to others in a clear, user-friendly manner. …

Between all of my technical-oriented posts, I thought it would be beneficial to speak a little about myself and my choice for pursuing the Data Science Program at Flatiron School.

I have always been an analytical-oriented person. I began my studies at Brandeis University, focusing on business and economics. My…

Oftentimes in Machine Learning, you are working towards solving a classification problem. For example, will someone default on their credit card payment, or pay it on time? Or, based on specific factors, will the flight be classified as delayed or non-delayed? In order to solve these problems, datasets will need…

SQL is a powerful tool that is used amongst data analysts and scientists in the tech industry. Building a repertoire that includes both SQL and Python knowledge can really help give an aspiring data scientist a leg up in their job interviews, as well as on the job itself! …

Netflix, Spotify, Facebook and many other platforms have powerful algorithms and tools that target specific advertising to their users. What makes these advertisements so powerful is their ability to cater their ads based on users existing preferences. These giants have comprehensive data that have been used to build sophisticated recommendation…

In statistics, a common tool is to build a linear regression model. As a data scientist, it is easy to get caught up in the technical improvements of a model: improving the R-squared, reducing the RMSEs, and removing features with high p-values. However, it is important not to lose sight…

Adina Steinman

Data Science Student at Flatiron School

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