# Data Science Workflow #

This repository explores through examples how to use the command line in an efficient and productive way for data science tasks. Learning to obtain, scrub, explore, and model your data.

# Introduction #
During this examples your will learn how to: (*i*) run docker containers, (*ii*) use the command line, (*iii*) run a basic application.

## Docker ##

Let us introduce docker, the first platform to make data science. Docker is a tool that allows developers, sys-admins or data-scientist to easily deploy their applications in a sandbox (**called containers**) to run on a host *operating system i.e. Linux*. The key benefit of Docker is that it allows users to package an application with all of its dependencies into a standardized unit for software development. Unlike virtual machines, containers do not have high overhead and hence enable more efficient usage of the underlying system and resources.[^1]

### Installing and using the Docker image ###


Docker pull

We recommend that you create a new directory, navigate to this new directory, and then run the following when you’re on macOS or Linux:

``` shell
$ docker run --rm -it -v`pwd`:/data datascienceworkshops/data-science-at-the-command-line
```

Or the following when you’re on Windows and using the command line:

``` shell
$ docker run --rm -it -v %cd%:/data datascienceworkshops/data-science-at-the-command-line
```

Or the following when you’re using Windows PowerShell:

``` shell
$ docker run --rm -it -v ${PWD}:/data datascienceworkshops/data-science-at-the-command-line
```

In the above commands, the option -v instructs docker to map the current directory to the /data directory inside the container, so this is the place to get data in and out of the Docker container.


# Notes #

- [ ] Make an container with Ubuntu 18.04 
- [ ] Packages to install: csvkit, 

[^1]: Docker for beginners, https://docker-curriculum.com/.