[Blog Reads] February 2018

I started to notice that more and more of my posts are shifting toward data science articles. It is not only because of my strong interest in the subject, but also because there are a high quality of good content coming from this field in the last few years. Everyone is saying that data science is becoming competitive and more saturated of amateur data lovers. I have to agree. Until I become a specialist myself, I just wanna join the troops of data lover. ^^

Tools for Data Science

  • JupyterLab is Ready for Users (link): As an avid fan of Jupyter Notebook, the release of JupyterLab is exciting news for both me and the data science community. This new open source editor allows many new functionalities such as:

    • Drag-and-drop to reorder notebook cells and copy them between notebooks.
    • Run code blocks interactively from text files (.py, .R, .md, .tex, etc.).
    • Link a code console to a notebook kernel to explore code interactively without cluttering up the notebook with temporary scratch work.
    • Edit popular file formats with live preview, such as Markdown, JSON, CSV, Vega, VegaLite, and more.
    • Cookiecutter Data Science (link): Ever wondered what is the best method to present your analytics project? This pip installable package allows for well-organize and standard project structure.

AI News

  • As China Marches Forward on A.I., the White House Is Silent (link): this is a hot topic recently and for good reason. AI is becoming an important battle between countries to see who can advance AI to the next-level and change the way humans interact with the world.


  • Just How Shallow is the Artificial Intelligence Talent Pool? (link): Element.AI is a startup I love as I am from Montreal and there are many events locally sponsored by the amazing people at Element.AI. Citing the article, “[Element.AI] estimated that there were fewer than 10,000 people in the world with the expertise needed to create machine learning systems”.
  • What is a Senior Data Visualization Engineer? (link)

  • Struggling Data Analyst (link): One of the most interesting post on Reddit I read. It is due to the insightful replies that remind a struggling analyst that you always to check and double check your data, that you need to understand your problem well before starting the analytics work and the path to data science is a tedious path.

Research Advancements

  • Google Brain wrote a great recap post about their amazing research in the past year (link).

Data Visualization Projects

  • Using Twitter data, here is a visualization of all the news that trended in 2014 (link).

The two  projects below are some of the most amazing project I ever saw and both of them are structural analysis of narrative projects. This type of project need deep analysis of character and story developments and good understanding of text analysis (including NLP) to extract interesting text related information.

  • Visualizing Archer Data visualization to further your enjoyment of narrative (link1, link2)

  • Jules Verne Great Trilogy (link)

Machine Learning Projects

  • Introduction to Learning to Trade with Reinforcement Learning (link): This project takes a research robust approach to the classic trading algorithm problem. Not only does it cover supervised learning (the more popular approach), it also looks at deep reinforcement learning. I personally believe that DRL could be the right algorithm to better understand trading trends.


  • Rewriting Life 100,000 happy moments (link): This is a huge and extremely interesting dataset for anyone interested by Natural Language Processing.
  • Open Access at The Met: Animating Artworks in the Collection (link): Another extremely interesting dataset. This can be of interest for people interested by computer graphics, animation, arts and computer vision.



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Hi! I am a McGill University Alumni and work in the Data Science field in Toronto. I love to eat, travel, read and blog.

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