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Data_Science/README.md

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+[![nbviewer](https://github.com/jupyter/design/blob/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/JuliaAcademy/DataScience/)
+
+# Julia for Data Science
+Prepared by [@nassarhuda](https://github.com/nassarhuda). Last updated: May 2020. Julia version used: 1.4.0
+
+Accompanying videos are available at [JuliaAcademy](https://juliaacademy.com/p/julia-for-data-science).
+
+There are three main components in this tutorial.
+- Data (1 notebook)
+- Methods (10 notebooks)
+- Visualization (1 notebook)
+
+What we **don't** cover:
+- Setting up Julia.
+    - Here is a youtube video on how to use Jupyter notebooks in Julia: https://youtube.com/watch?v=oyx8M1yoboY
+    - 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
+- Intro to Julia.
+    - 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.
+    - 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).
+
+----------------------------
+# What we cover in more detail
+
+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_.  
+
+### Data
+In the first part we will make sure you can
+- read and save your data via various File I/O options
+- store, reformat, and process your data using different data structures.
+- ⭐Bonus: we will also cover quick basic Julia syntax in case this is your first Julia experience.
+
+---
+
+Next, we will explore some of the most popular methods and tools used in Data Science to process this data.
+### Methods
+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
+- understanding how Linear Algebra and Statistics tasks are performed in Julia
+- going through some of the most popular data science methods such as classification, regression, clustering, and more.
+- calling your existing Python, R, or C code from Julia.
+-----
+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.
+### Visualization
+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
+- statiscs plots
+- managing the axes (double axes)
+- showing error bars
+- managing colors and color schemes
+- plot annotations.
+
+--------
+### This is what you should expect in every notebook
+<img src="data/format.png" width="400">
+
+-----
+#### And finally... Enjoy this tutorial!
+