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+# 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/.