57 lines
2.9 KiB
Markdown
57 lines
2.9 KiB
Markdown
[](https://nbviewer.jupyter.org/github/JuliaAcademy/DataScience/)
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# Julia for Data Science
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Prepared by [@nassarhuda](https://github.com/nassarhuda). Last updated: May 2020. Julia version used: 1.4.0
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Accompanying videos are available at [JuliaAcademy](https://juliaacademy.com/p/julia-for-data-science).
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There are three main components in this tutorial.
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- Data (1 notebook)
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- Methods (10 notebooks)
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- Visualization (1 notebook)
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What we **don't** cover:
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- Setting up Julia.
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- Here is a youtube video on how to use Jupyter notebooks in Julia: https://youtube.com/watch?v=oyx8M1yoboY
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- Here is a youtube video on how to install, setup your IDE, and get started with plotting in Julia: https://www.youtube.com/watch?v=eXk9PXzuKXE&t=890s
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- Intro to Julia.
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- If you have a fair programming experience in a different language or in Julia, I would say you can jump in to this tutorial, I try to keep all functions easy and simple to understand. Also, the first notebook covers a little bit of syntax.
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- If you have the gift of time, you can start with an Intro to Julia tutorial from [JuliaAcademy](https://juliaacademy.com/p/intro-to-julia).
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----------------------------
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# What we cover in more detail
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Every data science task has one main ingredient, the **data**! Most likely, you want to use your data to learn something new. But before the **new** part, what about the data you already have? This is why our first section is on _Data_.
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### Data
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In the first part we will make sure you can
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- read and save your data via various File I/O options
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- store, reformat, and process your data using different data structures.
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- ⭐Bonus: we will also cover quick basic Julia syntax in case this is your first Julia experience.
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---
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Next, we will explore some of the most popular methods and tools used in Data Science to process this data.
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### Methods
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In this section, we hope to give you (the data scientist) all the tools you need to use Julia as a programming language for your data science tasks. This includes
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- understanding how Linear Algebra and Statistics tasks are performed in Julia
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- going through some of the most popular data science methods such as classification, regression, clustering, and more.
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- calling your existing Python, R, or C code from Julia.
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-----
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And finally, we will go over a few visualizations that will hopefully reveal a few tips and tricks to generate good looking and informative visualizations.
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### Visualization
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Note that the methods section will show several kinds of plots too. But here, we will cover plotting tools that we didn't get to in the Methods section. This includes
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- statiscs plots
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- managing the axes (double axes)
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- showing error bars
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- managing colors and color schemes
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- plot annotations.
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--------
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### This is what you should expect in every notebook
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<img src="data/format.png" width="400">
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-----
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#### And finally... Enjoy this tutorial!
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