The fine folks at DataCamp, a great site for learning data science right in your browser, have come up with another great infographic. This time it compares some of the many job titles in the data science field.
The infographic lays out the roles and skills needed for the following job titles. Note: not all the job roles can be confused with a data scientist, but all the roles can be important when completing an entire data science project.
If you have been hearing about Jupyter (formerly iPython) and have not tried it out, here are a couple quick, free, and easy options for giving it a try. No installation need, and no account setup. Just visit a link.
Easy Jupyter Notebook
Try Jupyter and tmpnb are two projects for instantly getting a jupyter notebook with just a simple URL. Tmpnb was created by Rackspace for Nature and Try Jupyter is a demo from the main Jupyter website. I believe both projects use the same open source code found on GitHub. They might even be 2 URLs to the same infrastructure.
The major limitation is the lack of an ability to comeback to your notebook later (which is not a problem if you host the Jupyter notebook on your own). The notebooks die after some time of inactivity, but you can always create a new one. For more on the design decisions, see the Rackspace blog post, How did we serve more than 20,000 IPython notebooks for Nature readers? or join the open source project on GitHub.
If you have been wishing to try our Jupyter, it cannot get much easier than these options.
Dat is an open source project focusing on data storage. In particular, the project wants to version control data. What is version control? In short it allows for tracking of history associated with something (typically source code files or documents). Dat takes the idea a bit further, and the data is versioned at the row level and not the file level. Plus, it is built for collaboration among teams.
The lines between analytics and data science can definitely be very blurry. Different companies might call the same position by two different names, but at their core, they do have some differences.
Below is an infographic from the faculty of the Online MS in Analytics at American University. I think the infographic is accurate.
In my opinion, a true data scientist should spend more time creating and programming new algorithms while a business analyst should spend more time applying existing algorithms.
A couple of notes
Years of Education are not much different, but the academic disciplines are very different. Data Scientists tend to have degrees with more rigorous mathematical training. For me, this is the biggest differentiator.
It appears financial institutions prefer business analysts while the government and colleges prefers data scientists
Surprisingly, Business analyst jobs are projected to grow faster than data scientists (27% to 15%), not sure I totally agree with that!