DataScienceInJuliaForHackers.jl or Data Science in Julia for Hackers

How to cite this book (note you need to replace the highlighted text with the date in which you retrieved the information):

Carrone, F., Nicolini, M., & Obst Demaestri, H. (2021). Data Science in Julia for Hackers. Retrieved October 6, 2021, from https://datasciencejuliahackers.com/

This is still a beta version of the book. We hope to finish it in the following months.

You can visit the book page here

A book written by Federico Carrone, Herman Obst Demaestri and Mariano Nicolini, with a focus on applied knowledge and computational techniques, written in the Julia language.

Thanks to Martina Cantaro, Camilo Plata, Manuel Puebla, Lucas Raúl Fernandez Piana, Osvaldo Martin, Iñaki Garay and Mariana Vinyolas.

Table of contents

Part I: Data Science and Julia

• First chapter: Science, technology, models and epistemology.

• Second chapter: Introduction to the Julia programming language, showing examples of code and some first steps.

Part II: Bayesian Statistics

• Third chapter: An introduction to probability, probability distributions and Bayes' interpretation.

• Fourth chapter: Using a Naive-Bayes approach we construct a simple spam email filter.

• Fifth chapter: An introduction to Probabilistic Programming and some simple examples using the Turing.jl package.

• Sixth chapter: We estimate the gravity of Mars to compute the escape velocity, throwing stones and taking very simple measurements from it.

• Seventh chapter: We use a hierarchical bayesian model to estimate latent variables that describe Premier League´s football teams.

• Eighth chapter: We analyze how the scoring probability is affected by some variables, such as the distance from the hoop and the angle of shooting.

• Ninth chapter: We solve a problem of optimal pricing optimization using a bayesian point of view.

Part III: Machine Learning

• Work in progress

Part IV: Deep Learning

• Tenth chapter: Overview of Machine Learning and implementation of a simple convolutional neural network that is able to discriminate between pictures of bees and wasps.

Part V: Scientific Machine Learning

• Eleventh chapter: We explain the Ultima Online Catastrophe using differential equations to build a population dynamics model.

• Twelfth chapter: A continuation of the Ultima Online Catastrophe, introducing the Universal Differential Equations to recover missing parts of scientific models.

Part VI: Time Series and Forecasting

• Thirteenth chapter: We lay the foundations for time series analysis, focusing on the exponential smoothing method.