9. 6 Take-Aways after Reading "The Signal and The Noise"

   The Signal and The Noise by Nate Silver is a must-read book for those interested in predictions. It is not a technical book. You will not learn any algorithm. However, it presents a series of real-world scenarios when predictions did work and where predictions did not work. The book is …

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10. My Talk about Superset [Python Milano Meetup]

    Yesterday, I gave a talk Python Milano Meetup. The Meetup was designed as Python pills: three 20-minutes talks in a row. The talks:

    • Superset: data visualization at AirBnB - Marco Santoni
    • Java Vs Python - Cesare Placanica
    • pdb in action - Lorenzo Mele

    Very nice talk of @Airbnb #Superset with @MrSantoni at #PythonMilano …

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11. Manufacturing. When data is not a commodity

    What does it mean to work as a data scientist in manufacturing? What is the value behind data? Data science has gained popularity in domains like internet, but the industrial production domain has specific requirements.

    

    I gave a talk at Data Driven Innovation about the specific challenges when doing data …

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12. Weighted Random Sampling with PostgreSQL [Follow-up]

    I received valuable feedbacks by Jim Nasby regarding the post about weighted random sampling with PostgreSQL. I will report here Jim's email.

    Sadly, Common Table Expressions (CTE)s are insanely expensive, because each one must be fully materialized. So in your example, you're essentially creating 5 temp tables (one for …

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13. Monitoring Bus Frequencies in Rome

    I have just launched atacmonitor. It is a website providing information about the waiting time at bus stops in Rome.

    

    Overview

    The datasource is live data about bus waiting time of ATAC, Rome's public transport company. The transport office provides public API with real-time data.

    I have implemented a simple …

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14. Blog Migrated to Pelican on GitHub Pages

    I have migrated my blog. It is built under Pelican, a static site generator. It allows me to write posts as plain markdown or even Jupyter notebooks. I then use GitHub Pages to version and publish the blog. I am continuing to use Aruba as domain provider. It is sufficient …

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15. Insights from IEEE Big Data 16

    I have attended the IEEE Big Data 16 conference in Washington DC. I thank my company for sponsoring the trip. The conference included a special symposium dedicated to manufacturing. The symposium hosted some participants of the Bosch Production Line Performance competition from Kaggle.

    2016 IEEE International Conference on Big Data …

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16. Weighted Random Sampling with PostgreSQL

    You have a table like the following:

    CREATE TABLE weights (
    color varchar primary key,
    weight float
    );
    
    INSERT INTO weights (color, weight)
    VALUES
    ('red', 8),
    ('blue', 3),
    ('green', 10),
    ('yellow', 10);
    

    The table lists the weights associated with certain colors. Imagine a weight representing how much you like that color.

    Now …

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17. Applied Bayesian Inference with PyMC [video]

    I was glad to give an intro to Bayesian Inference at PyData Florence 2016. The video of the talk is now out.

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18. A Simple Machine Learning Pipeline

    This post contains the code that I used in my talk at Python Milano Meetup on June 22nd 2016. The talk was a quick overview of Pipeline, a nice API by scikitlearn to abstract your machine learning algorithm. It is based on the Boston Housing Data Set.

    We'll just load …

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