My brain god I need help Data science/ cyber security

[Quanta07-sudo]

Discussion Type

Help!!

Discussion Content

I want to my carrier to be smt like data science or cyber security by I want to be both of them at a time. I want to do both jobs at a time and learn stuff, idk please honestly tell me if I being delusional or if this is even achievable. But help me start somewhere I need someone to guide me walk the path with me( a friend circle). Thanksss!!! any help would be so much appreciated.

[mecodeatlas]

Thanks for posting in the GitHub Community, @Quanta07-sudo!

We're happy you're here. You are more likely to get a useful response if you are posting your question in the applicable category, the Discussions category is solely related to conversations around the GitHub product Discussions. This question should be in the Programming Help category. I've gone ahead and moved it for you. Good luck!

[Quanta07-sudo]

Thank you so much I was confused on where to post this thanks a bunch

[MVRU]

Hey @Quanta07-sudo! 👋🏻

Seriously, it's ok for you to be interested in cybersecurity and data science. There's even some tangible crossover between them (such as applying machine learning to identify security threats or looking at network flows for patterns).

That is, since and is a sentence-level, as is not, for most intents and purposes you should lead with one and playback with curiosity the other. Here's something I suggest in the interest of getting practical quickly:

---

1. Lay the groundwork (applicable to both routes)

• Python: the language for cybersecurity scripting and the base for data science.
• Linux: highly utilized for security purposes, as well as in data environments.
• Networking basics: essential for understanding data movement and system vulnerability.

---

2. Choose an initial track (research the other in parallel)

If you enjoy patterns and analysis:

Start with Data Science:

• Python for Data Science – freeCodeCamp (YouTube)
  

• 👉 Data Science Specialization – Coursera

If you are interested in systems / security especially:

Try Cybersecurity:

• The Cyber Mentor (YouTube)

• 👉 TryHackMe – Hands-on and beginner-friendly

---

3. Join a community

• r/learnprogramming
• Discord servers like DataTalks or Cyber Security Hangout
• GitHub repos tagged with #good-first-issue

Don't be afraid to reach out for questions either, there are TONS of beginners out there on the same path.

---

You already have the most important part: curiosity and motivation. Anything else is just practice, time and consistency!
:)

[Quanta07-sudo]

That is some wonderful and super useful advice thanks so much!!! and the thing is I already know a bit of pandas and sql and a tiny bit of terminal too. So I going to uni soon and the thing is I want to do a degree in data science and another degree in cybersecurity in the eu do you think its going to possible? I am a super grinder so I work best under pressure

[ZOR0-tech]

You can pursue both brother nowadays hybrid roles are there if you are interested in both no doubt go for it as i too am a person who is grasping knowledge in both

[Storm-Dev6]

You're absolutely not being delusional — it's great that you're passionate about both data science and cybersecurity! 🎯

Here’s some honest guidance:

✅ Is it possible to do both?

Yes, but you’ll probably want to start with one, then explore the second as you grow. In fact, cybersecurity uses a lot of data science techniques (like anomaly detection, threat modeling, etc.), so learning one will help the other.

🚀 Where to start:

Start with Python programming — it’s essential in both fields. Then choose one of these paths first:

If you start with Data Science:

• Learn Python, Pandas, NumPy
• Understand statistics and basic machine learning
• Try projects: analyze datasets from Kaggle

If you start with Cybersecurity:

• Learn Linux basics, networking, how the internet works
• Study OWASP Top 10 (common web vulnerabilities)
• Try free platforms like TryHackMe or Hack The Box

💡 Tip:

No matter what you choose first, don't rush. You have time. It’s better to build slowly than burn out.

👥 About finding friends / community:

You’re already on GitHub Discussions — great start! Also try:

• Reddit communities like r/datascience, r/netsecstudents
• Discord servers for cybersecurity and data science learners
• Open-source projects — contribute and meet people!

You're not alone. DM me anytime you want advice or motivation. You've got this. 🚀

[Quanta07-sudo]

yes sir!! thanks for the motivation!!

[Quanta07-sudo]

Same!!! I am so much into security but as there are a lot of people I gave up. but something clicked after a while I am I am suddenly interested in blockchain security and now somehow I am practicing in it

[Push4ck]

You're not being delusional at all — data science and cybersecurity absolutely intersect! Start with Python, Linux, and networking basics — they’re foundational for both. Since you already know some Pandas and SQL, you’re ahead. Pick one to major in (say, data science), and explore the other through side projects like TryHackMe or blockchain security labs. With consistency and curiosity, you can master both over time. You've got this! 💪

[jihanurrahman33]

Hi @Quanta07-sudo,

You’re not being delusional at all — both Data Science and Cybersecurity are huge and growing fields, and there is actually some overlap between them. For example, security teams increasingly use data analysis and machine learning for threat detection, anomaly detection, and risk modeling. So having skills in both can definitely be an advantage.

That said, trying to master both at the same time can feel overwhelming. A practical approach would be:

1. Pick a primary focus first. For example, start with Data Science (Python, statistics, machine learning, SQL) or Cybersecurity (network fundamentals, Linux, cloud security basics).
2. Learn the fundamentals.
   • Data Science: Python, Pandas, NumPy, scikit-learn, SQL.
   • Cybersecurity: Networking basics (OSI/TCP-IP), Linux command line, OWASP Top 10, and basic security tools.
3. Bridge them gradually. Once you’re comfortable in one, you can start applying it to the other — e.g., using data analysis for log monitoring, or applying security practices to data pipelines.
4. Build projects. Even small projects (like analyzing security logs with Python, or visualizing attack patterns) will give you confidence and help you find what you enjoy most.
5. Find a community. GitHub, Reddit, Discord groups, and open-source projects are great places to connect with others walking the same path.

🚀 Bottom line: It’s totally possible to build a career that touches both areas, but you’ll move faster if you focus on one as a foundation first and then blend the other in.

You got this! 🙌

[abouknan]

I’m really interested in both Data Science and Cybersecurity, and I’m wondering if it’s realistic to pursue both at the same time—either learning or working in both fields simultaneously.

I’m looking for guidance on where to start, how to balance learning both areas, and any advice on building a “friend circle” or network of people who can help me along the way.

Honestly, I don’t want to be delusional—I just really want to grow and explore these fields. Any advice, resources, or mentorship suggestions would be super appreciated! 🙏

[JEX01]

👌Hey! I’m a Cybersecurity student, and I totally get your ambition — wanting to learn both Data Science and Cybersecurity is doable, but it needs smart planning.

Here’s a practical path:

Start with one field first — build a strong foundation.

Focus on overlapping skills like Python, Linux, and cloud knowledge.

Do small projects: Kaggle for Data Science, TryHackMe/Hack The Box for Cybersecurity.

Join a learning community or study group for guidance.

Gradually add the second field once comfortable with the first.

Manage your time — don’t try to do both full-time from day one.

✅ Ambition is great! Start small, build mastery, then expand. Both paths can meet if you plan smartly.

If you found this helpful, marking it as 🎖️“Best Answer"🎖️ would really encourage me and others trying to guide beginners 🙂

[JEX01]

Go to the discussion thread.

Find the reply you want to mark.

Look for a “✔️ Mark as answer” button below that comment.

Click it — it becomes the “Best Answer.”

i want to do this for a badge " big brain " it will be helpfulll

[PRIYAtechky]

It's not delusional at all—you can absolutely explore both Data Science and Cybersecurity, but you’ll need to approach it with the right mindset and structure. Both fields are vast and constantly evolving, so trying to master both at the same time may feel overwhelming. Instead, think of it like building layers:

• Start with fundamentals: Programming (Python), databases (SQL), networking basics, and Linux. These skills are useful in both fields.
• Pick one as your primary focus first: For example, you could start with Data Science (statistics, machine learning, data analysis) while learning basic Cybersecurity concepts (network security, ethical hacking, cryptography).
• Then, cross-skill: Once comfortable, you can explore overlaps. For example, cybersecurity generates a ton of data (logs, intrusion reports), and data science can be used for threat detection, anomaly detection, and fraud analysis.

How to Begin

• Data Science path: Learn Python, Pandas, NumPy, Matplotlib, Machine Learning basics.
• Cybersecurity path: Start with CompTIA Security+ level knowledge, understand OSI model, encryption, vulnerabilities, and ethical hacking basics.
• Community & Friends: Join GitHub, Kaggle (for Data Science), and cybersecurity forums like HackTheBox, TryHackMe, and Discord groups. Surrounding yourself with learners makes the journey easier.

✅ It is achievable, but focus on one domain at a time while building transferable skills. Over time, you can specialize in areas where Data Science and Cybersecurity meet—like AI for Security or Threat Intelligence.

You’re not alone—many people combine these fields. Start small, stay consistent, and find peers to learn with. 🚀

[francescousseau]

You’re not delusional at all — combining Data Science and Cybersecurity is not only possible, it’s increasingly critical. Data is like digital oil: without analysis it has little value, and without security it can be stolen or corrupted. Mastering both makes you stand out in the market.

---

🌐 What this means in practice

• Dual path, shared foundation: start with the basics (Python, statistics, networks, Linux). Then develop side projects in data science and security labs in parallel.
• Complementarity: machine learning helps detect intrusions or fraud, while security protects data pipelines and ML models.
• Real-world example: banks use data science to predict fraud, but rely on cybersecurity to shield those systems from attackers trying to tamper with the models.

---

🚀 Where to start

1. Common technical foundations

   • Python: automation scripts and ML.
   • Networks & Operating Systems: TCP/IP, HTTP, DNS, Linux.
   • Math: statistics and linear algebra.

2. Data Science path

   • Learn Pandas, NumPy, Scikit-Learn.
   • Practice with open datasets (Kaggle is gold).
   • Suggested certifications: Google Data Analytics, IBM Data Science.

3. Cybersecurity path

   • Tools: Wireshark, Nmap, Metasploit.
   • Labs: OverTheWire, HackTheBox.
   • Suggested certifications: CompTIA Security+, Cybersecurity Essentials (Cisco).

4. Intersection areas

   • Anomaly detection in security logs with ML.
   • Fraud prevention in financial systems.
   • Security for AI systems (adversarial attacks, model protection).

---

🧭 Practical strategy

• First circle of friends: join Kaggle (data) and CTFs (cybersecurity).

• Small projects: publish a mini monitoring system on GitHub that detects suspicious server logins with Python + ML.

• Layered progression:

  1. Build the foundation (3–4 months).
  2. Choose a project that unites both fields.
  3. Test whether you enjoy more the analytical side (data) or the offensive/defensive side (security).

---

🔑 Brutal honesty

It’s not a short or easy road. But the beauty is you don’t have to pick one over the other right away. By advancing in layers, you can build a hybrid skill set. That’s the kind of journey where you can become a reference precisely because you bring the two worlds together.

📚 Expanded Course Suggestions

1. Shared Foundations

Programming & Computer Science

Python for Everybody (University of Michigan – Coursera)

CS50’s Introduction to Computer Science (Harvard – edX)

Math & Statistics for Data

Mathematics for Machine Learning (Imperial College – Coursera)

Statistics with Python (University of Michigan – Coursera)

Linux & Networking

Introduction to Linux (Linux Foundation – edX)

Computer Networking Basics (Google – Coursera)

2. Data Science Path

Core Data Science

IBM Data Science Professional Certificate (Coursera)

Applied Data Science with Python Specialization (University of Michigan – Coursera)

Machine Learning & Deep Learning

Machine Learning Specialization (Andrew Ng – Coursera)

Deep Learning Specialization (DeepLearning.AI – Coursera)

Data Engineering

Data Engineering with Google Cloud (Coursera)

Big Data Specialization (UC San Diego – Coursera)

3. Cybersecurity Path

Foundations

Google Cybersecurity Professional Certificate (Coursera)

Introduction to Cybersecurity (Cisco Networking Academy)

Network Security & Ethical Hacking

Fundamentals of Computer Network Security (University of Colorado – Coursera)

Ethical Hacking (University of Colorado – Coursera)

Cryptography & Advanced Security

Cryptography I (Stanford – Coursera)

Applied Cryptography (University of Colorado – Coursera)

Cloud & Defensive Security

AWS Cloud Security (Coursera – AWS)

Cybersecurity for Business (University of Colorado – Coursera)

4. Integration Zone (Data Science × Cybersecurity)

AI for Cybersecurity (Coursera Project Network)

Machine Learning for Cybersecurity (FutureLearn)

Adversarial Machine Learning (MIT OpenCourseWare – free)

[Applied AI in Cybersecurity (Udemy/independent platforms)] – practical labs for anomaly detection, phishing classification, intrusion detection.

5. Bonus Tracks

Cloud Security: Google Cloud Cybersecurity Specialization

Incident Response: IBM Cybersecurity Analyst Professional Certificate

MLOps (relevant for both DS & Security): MLOps Specialization (DeepLearning.AI – Coursera)