Congratulations! You’ve graduated from the Flatiron program! That took a lot of work!
But wait, there’s more work to be done! There is a treasure of information that has been curated to help you learn some of the things that weren’t covered in the curriculum. These aren’t just the resources and information you’ll need to know to be effective at your job search (Flatiron provides a ton of great information on that, too!). These are the topics that Computer Science majors learn that cover things related to how a computer operates, stores data, allocates memory, etc. You’ll need all of this information in order to feel more confident moving forward.
Generally, students graduating with CS degrees have a deeper understanding of how a computer works, but less experience actually building applications, whereas students graduating from bootcamps have more experience building things, and less understanding/exposure to how computers actually make it all happen.
That’s where the graduate access to additional Flatiron tracks (like Computer Science) comes into play. But before we talk about that, I want to make a suggestion:
After you graduate, I strongly suggest working through the whole Career Prep track, beginning to end. Then go back and review each lesson as it pertains to your current job search situation. There is a LOT of fantastic resources and important instructions throughout the track, but the latter part of the track includes suggestions and topics you’ll want to get started learning about before you are too far along in your job search. These are covered in the Computer Science track.
So, if you’re thinking that you’re done with Flatiron, and can run out there and land your dream job right now, I’m going to spoil it for you. You’ll be competing for jobs with CS grads, and you’ll be asked about those CS topics as well. Why? Because you need to know how a computer executes and handles data in order to write your programs in a way that caters to things like speed and memory. Users don’t like sites that take forever to load information, and businesses don’t like to waste resources on endless storage needed to run their applications. You need to understand these trade offs.
If you’re thinking that you don’t need to do the additional tracks on Learn, then I hope you already understand what Big O notation is, can determine how to calculate it and express it in terms of runtime and space, and understand what Log n is. Do you know what a Linked List is and why it is used? How do Linked Lists and Arrays differ when storing information? Did you know that Arrays and Linked Lists have different impacts on Big O? And there’s more, like Trees and Binary Trees…
Simply put, Big O is used to describe the running time or space requirements of executing an algorithm as the size of the input to the algorithm increases. The differences between two functions that do the same thing, but in different ways (like iteration vs. recursion), becomes very clear when you consider very large datasets passed into the algorithm.
Liked lists are like arrays, but an array takes up a continuous portion of memory, so each element in the array is next to each other. So, imagine changing an element in the beginning of the array. It’s very likely that the space in memory right before (and after) the array is already being taken up by some other, unrelated data. So in order to keep all the elements grouped together next to each other in memory, you’ll need additional space and would have to move them all to a new location big enough to hold them all together. This is where linked lists come in. Each element in a linked list is in it’s own location in memory, and points to where in memory the next element is located. With this setup, each element only requires one space, so adding or removing an element in the list only involves updating the information pointing to the next element.
Now, don’t be intimidated by these new terms and topics. Flatiron taught us how to build things we weren’t able to build before the program. Their CS track will teach you about the computer science topics you’ll need to understand, too. That’s why I strongly suggest working through the Career Prep and Computer Science tracks immediately after graduation. Yes, take a moment and really celebrate your accomplishment, we all deserve that after putting in the hard work. But then go back, focus and work through the rest… You’ll be very glad that you did!