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Towards Data Science 10/22/2020 22:07
How the U.S. blundered the war on COVID. Here in the U.S., we are seven months into the COVID-19 pandemic and two weeks out from the presidential election. We now have plenty of data to take stock and evaluate how countries have performed in combating the virus. The U.S. has employed a haphazard response to the pandemic, with and used by state and local leaders. This lack of leadership has led to a weak economy and incredibly high death rates. The chart below shows a high-level timeline of the pandemic to-date. Europe had the first significant outbreak with peak mortality occurring April 10, with the U.S. peak occurring two weeks later on April 24 and Canada, another two weeks later on May 7. Latin America experienced its peak much later in
Towards Data Science 10/22/2020 19:46
Tired of wordclouds? PictureText converts a list of short documents to an interactive tree map with minimal code. The amount of written text available and relevant for us is staggering and, like most things these days, increasing exponentially. Yet the tools we use to read have largely remained unchanged. When researching a topic, reading the news or trying to get an update on an event, we tend to follow a T-shaped process. We want to understand, from a body of documents:. What are they mostly about ? What is generally going on? What are the main themes ? After the broad strokes, we tend to quickly dive deep into something we find interesting. Ideally keep track of what we read, we try and stay on course. Pull up, dive again into a narrow top.
Towards Data Science 10/22/2020 19:31
Detecting COVID-19 Outbreaks Through Wastewater Monitoring. Source: Author. Governments attempting to monitor COVID-19 outbreaks usually have to make difficult tradeoffs. Testing individual members of the population at scale is expensive and logistically difficult: limited laboratory equipment and medical facilities make it almost impossible to test an entire country’s population.
Towards Data Science 10/22/2020 18:27
Image by author. Use linear regression and Newton’s method to maximize a company’s production output. Introduction. Economics and data science can look very similar at times. Many techniques that have been used by economists for decades are imperative to data science and machine learning. The intersection comes not only from the close relationship of both fields to statistics, but also from the mathematics that drives their modeling processes. In this post I will show the usefulness of applying economic methods to a data science-like problem. The technique used in this blog post for solving the problem is going to be much more complicated than necessary. However, a great way to learn a complicated process is to practice it on simple problems.
Towards Data Science 10/22/2020 18:24
Opinion. Image from. One of the hallmarks of developing NLP solutions for enterprise customers and brands is that more often than not, those customers serve consumers who don’t all speak the same language. While the majority of our customers are running VoC (voice of the customer), social listening and market research programs with a predominantly English-speaking consumer base, we do have some that serve consumers that span more than 20 languages, and many with a heavy presence in Nordic countries, Latin America and the Asia-Pacific region…not to mention other parts of Europe. The challenge in NLP in other languages is that, with nearly 300 million more English-speaking users than the next most prevalent language, Mandarin Chinese. Modern NL.
Towards Data Science 10/22/2020 18:20
Moving real-time analytics to the next level. Image by via under license to Outbrain. We have been running Real Time Analytics, based on Apache Kafka, Spark Streaming and Apache Druid for more than one year. After solving the problem of connecting these technologies, described in my recent blog post, we added many use-cases to our system. In general, each use-case was built according to the principle of: read raw events from one Kafka topic, process them in a Spark Streaming job, write into another Kafka topic, and ingest into Apache Druid. Image by Author. It has enabled us to obtain a wide range of insights from our Real Time dashboards. However, each type of events had its own flow and had its own visualizations, independent from other type.
Towards Data Science 10/22/2020 18:13
Analyzing the Google Play Store Apps Reviews based on sentiment reported by reviewers. About this article. This article talks about the results of a data visualization project in the context of the Google Play Store Apps Review. The is one of the most used digital distribution services in the world, with tons of apps and users. Because of it, there is a lot of data available regarding the apps and their users’ evaluations. The project was solicited as part of the course of Data Science I at the Instituto Metrópole Digital, part of the Universidade Federal do Rio Grande do Norte, in Natal, Brazil to practice the subjects learned in class. Methods. In this project, we will search for trends in the dataset available in . For this purpose, we will.
Towards Data Science 10/22/2020 17:08
Leveraging target encoding when your categorical variables have a hierarchical structure. Photo by on. At the very beginning of this millennium, when my hair was a lot darker, I wrote a little article with a pretty long name, entitled “”. It was a straightforward article, which I decided to write, driven by a very practical need for a method to deal with data types that were hard to plug into Machine Learning (ML) models. At the time, I was working on ML models to detect fraudulent e-commerce transactions. Therefore I was dealing with very “sparse” categorical variables, like ZIP codes, IP addresses, or SKUs. I could not find an easy way to “preprocess” such variables, except for the traditional , which didn’t scale well to situations where o.
Towards Data Science 10/22/2020 13:52
Pitfalls To Avoid while Interpreting Machine Learning-PDP/ICE case. Photo by on. Abstract. One can find numerous articles today on Explainable AI, some of which can be found . The most standard guide for Explainable AI will undoubtedly be by Christoph Molnar. When I came across the recent paper , I decided to write some blogs out of it. This is a take on one of the aspects presented in the paper. This article is focused on the pitfalls we need to avoid while interpreting Partial Dependence Plots(PDPs)/Individual Conditional Expectation(ICE) plots. These are post hoc techniques used to observe how the model takes a decision by keeping all exogenous variables fixed, except one(also two in case of PDPs) which is regarded as the feature of interes.

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