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PyCharm for Data Scientists
This article is a discussion of some of PyCharm's features, and a comparison with Spyder, an another popular IDE for Python. Read on to find the benefits and drawbacks of PyCharm, and an outline of when to prefer it to Spyder and vice versa.
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How to Automate Tasks on GitHub With Machine Learning for Fun and Profit
Check this tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
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Modeling Price with Regularized Linear Model & XGBoost
We are going to implement regularization techniques for linear regression of house pricing data. Our goal in price modeling is to model the pattern and ignore the noise.
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Projects to Include in a Data Science Portfolio
“Don’t pick just random projects to work on and add it to your resume or portfolio. Solve a problem that relates to the companies that you’re interested in.”
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The Mueller Report Word Cloud: A brief tutorial in R
Word clouds are simple visual summaries of the mostly frequently used words in a text, presenting essentially the same information as a histogram but are somewhat less precise and vastly more eye-catching. Get a quick sense of the themes in the recently released Mueller Report and its 448 pages of legal content.
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Building a Flask API to Automatically Extract Named Entities Using SpaCy
This article discusses how to use the Named Entity Recognition module in spaCy to identify people, organizations, or locations in text, then deploy a Python API with Flask.
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 Why Data Scientists Need To Work In Groups
If you read this article you will see that the job of data scientist is NOT listed. The rest of this article will explore why it is true that data scientists need to work in groups.
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Top 8 Data Science Use Cases in Gaming
The understanding of the data value for optimization and improvement of gaming makes specialists search for new ways to apply data science and its benefits in the gaming business. Therefore, various specific data science use cases appear. Here is our list of the most efficient and widely applied data science use cases in gaming.
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 Top 10 Coding Mistakes Made by Data Scientists
Here is a list of 10 common mistakes that a senior data scientist — who is ranked in the top 1% on Stackoverflow for python coding and who works with a lot of (junior) data scientists — frequently sees.
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Interpolation in Autoencoders via an Adversarial Regularizer
Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.
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Why Data Scientists Need To Work In Groups
Top 10 Coding Mistakes Made by Data Scientists