**Dynamic Programming for Data Scientists**

Algorithms and data structures are an integral part of data science. While most of us data scientists don’t take a proper algorithms course while studying, they are important all the same. Many companies ask data structures and algorithms as part of their interview process for hiring data scientists. Now the question that many people ask here is what is the use of asking a data scientist such questions. The way I like to describe it is that a data structure question may be thought of as a coding aptitude test. We all have given aptitude tests at various stages of our life, and while they are not a perfect proxy to judge someone, almost nothing ever really is. So, why not a standard algorithm test to judge people’s coding ability. But let’s not kid ourselves, they will require the same zeal to crack as your Data Science interviews, and thus, you might want to give some time for the study of algorithms and Data structure and algorithms questions. This post is about fast-tracking this study and explaining Dynamic Programming concepts for the data scientists in an easy to understand way.

**Why You Are Using t-SNE Wrong**

t-SNE has become a very popular technique for visualizing high dimensional data. It’s extremely common to take the features from an inner layer of a deep learning model and plot them in 2-dimensions using t-SNE to reduce the dimensionality. Unfortunately, most people just use scikit-learn’s implementation without actually understanding the results and misinterpreting what they mean.

**Sentiment Analysis using ALBERT**

Every researcher or NLP practitioner is well aware of BERT which came in 2018. Since then the NLP industry has transformed by a much larger extent. Albert which is A Lite BERT was made in focus to make it as light as possible by reducing parameter size.

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