Here are some of my Matplotlib and Seaborn examples. I usually use Tableau during the data exploration and reporting stages. However, Matplotlib and Seaborn are also very useful when I write Python code. I use Matplotlib and Seaborn to generate kernel density estimation plots, pie charts, box plots, and so on. While only generating correct plots is not enough. I do care about the style and color of my plots.
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A/B testing is a very popular topic among companies. As a data scientist, there are several things that I need to check before I conduct A/B testing. The first half of this project is the workflow I'd go over every time. There's also an example of A/B testing in the second half.
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If an A/B testing experiment has multiple treatment groups, the cost of testing each treatment group and the probability of false positives will increase. When there are multiple treatments groups, I usually use two types of reinforcement learning, Upper Confidence Bound (UCB) Algorithm and Thompson Sampling to quickly find out the best treatment group by using the minimum amount of time.
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I used AWS Redshift to investigate a drop in user engagement for Yammer. Yammer is an enterprise social networking service used for private communication within organizations. This is a relatively small dataset, with more than 34,000 rows of records and 5 tables, but I used this demo to show you my thinking process when I face a business problem.
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Natural Language Processing (NLP) is everywhere in our life. There are many types of NLP, such as classical NLP, deep learning NLP, seq2seq, and so on. This project is the first part of my NLP project which covers a classical NLP model, bag-of-words, for sentiment analysis.
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