A lot of students ask me what to do to become a Cybersecurity Data Scientist (CSDS). Like all worthwhile things, you have to put in to get out. And becoming a CSDS is no different.
If you’re reading this, you probably already realize that CSDS is one of the hottest and fastest growing professions to be changing Cybersecurity[1].
From the perspective of an employer (typically a large enterprise, such as a cybersecurity or finance company with large amounts of data), to let you be in charge of an important task (like designing and improving a next-generation AntiVirus or Insider Threat Detection solution) and to pay you the large salary that comes with this technical profession, you better have some proof that you know what you are doing!
In other words, you should have competence. There are two ways to demonstrate competence. The first, and best one, is having experience. The tricky thing about getting experience is that we often find ourselves in the “you need experience to get experience” trap, so true for many high-value professions in our economy.
The other way to demonstrate competence is through a background of education in CSDS. However, not everyone can get a Master’s or PhD in Cybersecurity Data Science, for a variety of reasons. For this reason it is not easy to get into this hot lucrative profession.
But don’t despair. As someone who has gone through many years of both education and experience, starting with my undergraduate at Stanford University and continuing on to becoming an early member in a startup that designed an award-winning Machine-Learning Ransomware solution, to now being a well-known author in the field, my team and I have created several solutions to enable students and professionals to overcome the barrier to entry into the field.
To attain an education as a CSDS without having to enroll in a 2-year Master’s or 5-year PhD program in Cybersecurity Data Science, enroll in the best-selling Cybersecurity Data Science Course.
It took us months to develop this course, and this course covers
✔ How to use machine learning to classify malware.
✔ Malware analysis 101.
✔ Setting up a cybersecurity lab environment.
✔ Learning how to tackle data class imbalance.
✔ Unsupervised anomaly detection.
✔ End-to-end deep neural networks for malware classification.
✔ Creating a machine learning Intrusion Detection System (IDS).
✔ Employing machine learning for offensive security.
✔ Learning how to address False Positive constraints.
✔ Breaking a CAPTCHA system using machine learning.
Click here to fast-track to becoming a Cybersecurity Data Scientist by enrolling in the course.
So that’s how to demonstrate knowledge of the field. Now, what about experience?
One of the best (and most exciting in my view) ways to demonstrate it if you’re not already in the field is by completing beginner projects that you can post on GitHub as well as use to build up your resume. To help you choose worthy projects, prepare a dataset, have an industry-acknowledged goal and method to solving this project, it’s necessary to have guidance. In Cybersecurity Data Science Projects for Students you get that guidance!
Project for Students will guide you on the path to using machine learning to
✔ Classify and Detect Malware.
✔ Catch Network Intruders.
✔ Detect Insider Threats.
✔ Break CAPTCHAs.
✔ Construct an Evolutionary Fuzzer.
✔ Construct Adversarial Attacks on Deep Neural Networks.
✔ Impersonate Voice.
✔ Create DeepFakes.
✔ Generate Fake Reviews.
Isn’t that ridiculously exciting? If you’d like to be guided on interesting and fun projects that will allow you to display your skills and growing knowledge enroll here now.
[1]: https://blog.datasciencedojo.com/data-science-changed-cybersecurity/#:~:text=Data%20science%20brings%20a%20logical,network%20traffic%20and%20safe%20traffic.
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