Here is a collection of six accelerated lessons to propel your computational skills like a railgun. These lessons, designed to make you more productive in research and academics, are the most useful to beginning graduate or senior undergraduates in engineering.

Pick a lesson to begin

Frequently Asked Questions

What is the purpose of this lesson collection?
While interacting with undergraduate and graduate students during classes and research in Materials Science, I felt that some of them could be a lot more productive if they are more familiar with some basic software tools like Matlab, Python, image processing softwares etc. While some of the instructors do include a Matlab how to section in their classes for example, the students have to ask around to learn about other tools that would make them more efficient in carrying their research or prepare more effective presentations. This set of lessons identifies the minimal set of productivity tools for a strating academic and gives an efficient introduction to them.
There exist other similar projects like Software Carpentry. Why another project?
That is absolutely true. There are other projects with similar goals. However I wanted to create a self contained lesson plan to introduce the tools that I think are the most useful based on my experience. In the future, perhaps, this project will interact with other similar efforts.
Is it essential to follow the lessons in given order?
It is not essenitial, but it would be beneficial. The lessons 1 to 3 follow some of the basic concepts e.g. installing and getting familiar with the most tools. And the latter topics are slightly more advanced. If you are beginning to get familiar with computational tools, perhaps following the lessons from 1 to 6 would be a good idea. But if you are already familiar with e.g. command line, then you can pick what specifically interests you.
What do I need to start with these lessons?
A working computer and an internet connection.
Are these plans available offline?
Not now. But there is a possibility that an offline app-like version will be available in the future.
What about content licensing?
The instructional lessons are licensed under the Creative Commons Attribution License.