Although Data Science has been around us ever since the 1960s, it has only gained traction in the last few decades. This is one of the main reasons why budding data scientists find it quite challenging to find the right mentors. However, this scenario is drastically changing now. With the right approach and by looking at the right corners, you can find data scientist mentors who can help you bridge the gap between theoretical and practical applications of data science.In this article, we will be looking at why there is even a need for individuals to have mentors in data science and how can we find them.

In my previous post, I described a (relatively) simple way to simulate observational data in order to compare different methods to estimate the causal effect of some exposure or treatment on an outcome. The underlying data generating process (DGP) included a possibly unmeasured confounder and an instrumental variable. (If you haven’t already, you should probably take a quick look.)

Azure SQL Database, the database-as-a-service based on Microsoft SQL Server, now offers R integration. (The service is currently in preview; details on how to sign up for the preview are provided in that link.) While you’ve been able to run R in SQL Server in the cloud since the release of SQL Server 2016 by running a virtual machine, Azure SQL Database is a fully-managed instance that doesn’t require you to set up and maintain the underlying infrastructure. You just choose the size and scale of the database you want to manage, and then connect to it like any other SQL Server instance. (If you want to learn how to set up an Azure SQL database, this Microsoft Learn module is a good place to start.)

I’ve been working with the Scottish census recently, to investigate employment in land-based (agriculture, forestry and fishing) industry. A friend of mine has recently moved to Dumfries and Galloway – a rural, farming area of Scotland. He’s commented on the ageing population in the area, so I pulled out the age profile from the census for his civil parish. This post shows how to plot up an age profile from the Scottish census table KS102SC, which is available online.

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By Lukas Biewald, Founder, Weights and Biases; Founder, Figure Eight (formerly known as CrowdFlower).

First derived from human intuition, later adapted to machine translation for automatic token alignment, attention mechanism, a simple method that can be used for encoding sequence data based on the importance score each element is assigned, has been widely applied to and attained significant improvement in various tasks in natural language processing, including sentiment classification, text summarization, question answering, dependency parsing, etc. In this paper, we survey through recent works and conduct an introductory summary of the attention mechanism in different NLP problems, aiming to provide our readers with basic knowledge on this widely used method, discuss its different variants for different tasks, explore its association with other techniques in machine learning, and examine methods for evaluating its performance. An Introductory Survey on Attention Mechanisms in NLP Problems

Vote in new KDnuggets Poll: What Python editors or IDEs you use the most?. Also see a very comprehensive chart in Intro to Data Science for Managers, check the 6 Goals for wannabe Data Scientists, find the secret sauce to be in the top 2% in Kaggle competitions, and learn a clever way to engineer your way out of slow Deep Learning models. Features