Data Science Chalk Talk

Data Science

Chalk Talk

Information is the oil of the 21st century, and analytics is the combustion engine

Peter Sondergaard, Senior Vice President at Gartner

Hi! My name is Valentina and, for those who know me, launching a personal blog is the most unlikely thing I could do.

However, it’s been almost a year since I started studying and learning about data science thanks to the knowledge and expertise of many people who were willing to share their skills. Hence, I decided I wanted to contribute, in turn, by sharing some topics and news related to Data Science, Machine Learning and Deep Learning. At the same time, I will size this opportunity to update and improve my knowledge in that limitless field.

Far from being ‘academic’, my articles are meant to be a source of confrontation, and I’d be more than happy to receive opinions or criticism to improve them.

Hoping I did’t bore you already, I wish you a happy reading!

  • Understanding Agent Based Model with Python

    7 June 2020 by

    When we want to model a given phenomenon, we could employ a mathematical underlying structure which we believe describes the scenario. Then, we can parametrize and calibrate it so that our model is able to reproduce patterns seen in data. This technique is appealing since it provides us with a set of parameters which can… Read more

  • Understanding the compartmental SIR model

    9 April 2020 by

    In my last article, I’ve been writing about the spreading of COVID-19 without really inferring the structure of the process. I provided some visualization tools and interactive widgets to have an overview of the phenomenon throughout time. Here, I’m going to dwell on the modeling techniques which can be used to understand the diffusion of… Read more

  • Model Selection for Linear Regression

    5 April 2020 by

    Whenever you want to build a Machine Learning model, you have a set of p-dimensional inputs to start from. However not all of these inputs might be necessary to obtain the best predictive model. Moreover, using all of the p predictors might lead to overfitting problem, especially if the number of observations n is not… Read more

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Which is your main interest?

Check my latest articles:

  • Understanding Agent Based Model with Python
    When we want to model a given phenomenon, we could employ a mathematical underlying structure which we believe describes the scenario. Then, we can parametrize […]
  • Understanding the compartmental SIR model
    In my last article, I’ve been writing about the spreading of COVID-19 without really inferring the structure of the process. I provided some visualization tools […]
  • Model Selection for Linear Regression
    Whenever you want to build a Machine Learning model, you have a set of p-dimensional inputs to start from. However not all of these inputs […]
  • Bootstrap sampling: an implementation with Python
    Bootstrap methods are powerful techniques used in non-parametric statistics, that means, whenever we are provided with data drawn from an unknown distribution law. The underlying […]

If you are data-addicted and want to learn more, you can have a look at my e-book Machine Learning From Scratch: A Gentle Introduction

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