CyberSecurity & Data Science Converge with BowTiedCyber
I made a post on BowTiedCyber's stack on the convergence of Data Science and Cybersecurity
I took some time in order to create a post on how data scientist that enjoy Cybersecurity can basically have their cake and eat it too.
Kudos go to @BowTiedCyber, had it not been for his help, I would not have understood Cybersecurity as well as I did for this post. Highly recommend checking him out if you are even the slightest bit curious about Cybersecurity.
There are lots of domains in the real world that will be very disappointed with what results Data Science has achieved for them thus far…. However, Cybersecurity will not be one of them.
This is because Cybersecurity is the art of finding that 1 Needle in a stack of needles, the moment you find that right needle, you basically won. AI/ML models are really good for this, because the data overall is quite stationary, it will not change much over a few months, and this is what a lot of networks companies are finding out, and are very quietly starting to scoop up a lot of Data Scientists and Machine Learning Engineers.
If you have been Following BowTiedCyber’s substack for quite some time, you will have no doubt got some solid skills in Tech programming, and especially Python programming. If you have a strong talent in Mathematics, and Python, but are wondering if the skills you are learning from Cyber can be translated in another field which is a little bit more math heavy, but involves the same level of Technical Python knowledge, then you would be interested in a career path known as a Data Scientist/Machine Learning (ML) Engineer.
Here is a graph that showcases the popularity of Cybersecurity alongside Data Science & ML:
Even if you are the type of person who loves Cybersecurity, but were hoping to stick with the exact same domain, and wanted a little bit of mathematics in your day to day career. You can find plenty of Machine Learning roles that specialize on Cybersecurity. Here is an example of 1 such position:
What exactly is Data Science anyways?
Data Science is a field in tech where you attempt to use some relevant piece of data and try to solve some sort of a problem with it. In the above job posting, your goal was to use whatever data Netography collected in order to build a model that will attempt to see how an attacker could potentially try to attack their network.
In this field, you will use: linear algebra, statistical modelling, and programming in order to create algorithms that extract knowledge, and insights from noisy data, and apply the hindsight learned across several different domains. Typically, in Data Science, you work with what’s known as big data: this could be several thousands of images in Red Green Blue (RGB) layers (Image Recognition), this could be text data that was extracted from Facebook (Natural Language Processing), or this could be a company’s stock information (Quants).
Click here to get a more in-depth explanation on it.
Here is the pay for an average Data Scientist:
Programming languages to use
Most Data Scientist typically work heavily with Python, but some roles that are a bit more biased towards statistics prefer to deal with R instead of Python.
Note: A lot of what we learn in the DS industry using Python code can easily be altered to work in the Cybersecurity domain. Check it out.
The thing is R and Python is super super similar anyways. Most R and Python code looks super identical. If you know one, you basically automatically know the other one anyways.
Resume Advice for Cybersecurity & Data Science
Typically, when you are applying for tech jobs, you always have to keep in mind something important. What does the person who is hiring care about? Typically, most people doing the hiring really don’t care about your volunteering activities, your hobbies, and all that stuff that people in HR say. It’s cool to have interesting hobbies, but if the hiring manager doesn’t even trust your ability to do the job yet…. he really doesn’t care about your hobbies or your personality.
The 2 main things most hiring managers that are targeting tech (data science & cybersecurity) care about are: Can you increase the amount of revenue we make, and can you help me save some time.
Increasing the Company’s Revenue
Just show how some of the stuff you did actually helped the business make more money, or reduce expenses. The preference is towards making more money, as this shows the business the DS team is super valuable which means bigger bonuses for your manager (boss), which means a more likely chance to be called in for an interview.
Since, I work as a Quant (specializing in DS), I work at a simple hedge fund. How does a hedge fund make money? By betting on stocks which will go up, and betting against stocks that will go down.
What Quants can do is monitor a simple passive Exchange Traded Fund (ETF), and predict which stock is going to go in the ETF, and which stock will leave the ETF. Being correct on this can earn your firm several hundred thousands in just a few months. So, in other words, the hit rate for this is extremely important, as the better the hit rate, the better the revenue. But, how would you convey this to other hiring managers
Here’s what I did:
For those that are in the industry, they immediately know what I did, and know 73% is pretty solid, especially since the data isn’t stationary. For those that are outside of the industry, it makes it clear I used some sort of a ML model to solve a problem, and solving this problem helped the firm make money. Which means if the new manager were to hire me, I could do something similar and have the DS team earn bigger bonuses by solving problems.
Remember: your hiring manager also wants more money
Saving Your Boss Time
We’ve all had to do boring responsibilities in our DS career, that we know will translate super well at showing the hiring manager our level of competence.
The keywords you are looking for in this case are: Created & Maintained, Responsible for maintaining …. that was used for weekly meetings, Created & Preserved a new…, Worked with … to automate … freeing up his time for more revenue generating activities.
The … are just filler, feel free to use them for whatever it was that you were doing in your role.
What the above keywords show is that you took on some sort of a new responsibility, and kept that process running properly. This freed up the time for one of the guys higher up than you, who used this free time to explore further ways to make even more money, or do some sort of a research activity that was needed.
Here is a simple picture that illustrates the above ideas:
Once you have everything in order, and are ready to apply, you can see the techniques used by BowTiedCyber to massively get ahead of the competition during the hiring process.
The cool thing is that the same techniques would basically work in the Data Science industry as well
For More Information on becoming a Data Science Hoodie, you can click here.