16 answers
Raheel
Student
Islamabad, Islamabad Capital Territory, Pakistan
1
Question
Updated
975 views
Hello everyone! I am a CS student currently in 4th semester can anybody guide me on what kind of skills I should work on keeping the fast growing AI in mind
I am confused that what should i learn because most of the things is taken by AI. And about others I don't have sufficient information. it would be a great favour If you could help. Thank you
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15 answers
Paranjyoti Lahkar
Data Scientist
8
Answers
Falls Church, Virginia
Updated
Paranjyoti’s Answer
Master the basics: strengthen your understanding of data structures, algorithms, and Python. AI tools can't replace this essential knowledge.
Develop practical AI skills by learning the fundamentals of machine learning and experimenting with libraries like TensorFlow or PyTorch on small projects.
Enhance your data skills, including SQL, data cleaning, and visualization, as real-world AI heavily relies on data.
Gain hands-on experience through projects and internships to apply your knowledge; this is more valuable than just taking courses.
Don't stress about AI taking over. Focus on using AI as a tool to boost your productivity and make yourself stand out.
Develop practical AI skills by learning the fundamentals of machine learning and experimenting with libraries like TensorFlow or PyTorch on small projects.
Enhance your data skills, including SQL, data cleaning, and visualization, as real-world AI heavily relies on data.
Gain hands-on experience through projects and internships to apply your knowledge; this is more valuable than just taking courses.
Don't stress about AI taking over. Focus on using AI as a tool to boost your productivity and make yourself stand out.
Divyanshu Bhuria
Systems Analyst
14
Answers
Princeton, New Jersey
Updated
Divyanshu’s Answer
Hi Raheel,
Here are five things I would recommend:
1) AI will touch virtually every industry and role, so learning the fundamentals now will help you stay competitive:
- AI and Machine Learning fundamentals
- Natural Language Processing (NLP) basics
2) You will need to be fluent in at least one programming language. I recommend starting with Python.
Python is the main language used in AI.
Start with:
- Variables, loops, functions
- Working with files and data
- Common libraries later like pandas, numpy, scikit-learn
3) Certifications can strengthen your resume and improve your chances of landing a job by showing verified, job-ready skills.
Target an Alteryx certification, it is a powerful code-free data analytics tool.
Use the following link to start learning for free:
https://www.alteryx.com/sparked/learning-programs/students
I am recommending the Alteryx certification because:
- It is a widely recognized data analytics tool
- There are free online lessons and certifications provided by Alteryx
- Knowledge gained from Alteryx certification is useful in various roles
4) Strengthen your communication skills, they will help you perform better in interviews and set you up for success at work.
In Technology you often need to explain complex ideas simply. Work on:
- Storytelling, explaining the “why” behind your work
- Public speaking and presentation
- Active listening and asking good questions
5) Build experience through volunteering, internships, college projects, and competitions.
Here are five things I would recommend:
1) AI will touch virtually every industry and role, so learning the fundamentals now will help you stay competitive:
- AI and Machine Learning fundamentals
- Natural Language Processing (NLP) basics
2) You will need to be fluent in at least one programming language. I recommend starting with Python.
Python is the main language used in AI.
Start with:
- Variables, loops, functions
- Working with files and data
- Common libraries later like pandas, numpy, scikit-learn
3) Certifications can strengthen your resume and improve your chances of landing a job by showing verified, job-ready skills.
Target an Alteryx certification, it is a powerful code-free data analytics tool.
Use the following link to start learning for free:
https://www.alteryx.com/sparked/learning-programs/students
I am recommending the Alteryx certification because:
- It is a widely recognized data analytics tool
- There are free online lessons and certifications provided by Alteryx
- Knowledge gained from Alteryx certification is useful in various roles
4) Strengthen your communication skills, they will help you perform better in interviews and set you up for success at work.
In Technology you often need to explain complex ideas simply. Work on:
- Storytelling, explaining the “why” behind your work
- Public speaking and presentation
- Active listening and asking good questions
5) Build experience through volunteering, internships, college projects, and competitions.
Liam Rielly
Data Center Technician
180
Answers
Manassas, Virginia
Updated
Liam’s Answer
Critical thinking, creative thinking, constructive thinking, use your brain.
Right now I see too many people with their eyes on the prize and are trying to learn every computer language, every new model, prompting, and how they can get it in their stack as quickly as possible. If you cannot think for yourself, solve a problem, and make something yourself, how are you going to build something that is going to do any of that as a system?
There are IT job interviews that will be the acronym game, coding interviews that will ask you to correct syntax, and engineering job interviews that the correct answer will be to follow the procedure for the given technology. Whatever you are doing now will get you past those interviews just fine. The prompting you are doing today will work in a professional environment. The person hiring for the jobs today is looking for someone with added value. What is your added value? Do you speak multiple languages? Do you draw or play a musical instrument? Do you have a hobby that compliments your job? Do you work on puzzles or play chess or similar? How can you prove to someone in the course of an hour or two you have thinking skills beyond the next ten candidates?
Something that is not getting discussed in the tech career space right now is that each job listing you see online is getting around 600+ applicants and it is near impossible to say who is the best fit for the job out of that stack. The interview matters most (as long as you have the correct skill set) and a lot of employers are trying to think outside the box when it comes to hiring by bringing in candidates that have less time in a discipline but have a wider skill set from previous jobs.
If you watch the interviews on freecodecamp on youtube you will never hear anyone say "I want the best of the best for coding python or its a no hire". All of the interviews say things like "I want a creative thinker" and "I want someone that can think outside of the box and solve problems". Its unfortunate but jobs are no longer just what their title is. Your job will be an AND, as in you do the one job AND another job with it. The IT guy at my last company did IT AND graphic design down to the company logo and company T shirts. I work in IT AND I am an electrician. How are you going to fill that role?
I have some random recommendations to get you going, don't stop coding, don't stop doing what you are doing, just slowly add things you can do between studying and coding.
Also, I figure its worth mention, I do not work in the same field as you are studying. I went to school for CS for a semester and dropped out. I do work with AI but I am on the user end primarily and work with hardware serving AI. I know a lot of people who work in your area and I know what skills are desired by people hiring for big tech companies. I guess my advice would be seen as "soft skills"by most coders. Don't take what I said as absolute truth but more food for thought.
Read "Rise of the Machines: A Cybernetic History" by Thomas Rid, and every book mentioned in that book.
Learn a second (or third or fourth) language to a conversational level.
Draw something, anything, and keep that as a habit.
Interview for jobs outside of your comfort zone with no intent on actually working the job.
Keep part of the conversation. Interact with people in the field you are entering and learn for the sake of learning. Learn anything, learn about learning.
Right now I see too many people with their eyes on the prize and are trying to learn every computer language, every new model, prompting, and how they can get it in their stack as quickly as possible. If you cannot think for yourself, solve a problem, and make something yourself, how are you going to build something that is going to do any of that as a system?
There are IT job interviews that will be the acronym game, coding interviews that will ask you to correct syntax, and engineering job interviews that the correct answer will be to follow the procedure for the given technology. Whatever you are doing now will get you past those interviews just fine. The prompting you are doing today will work in a professional environment. The person hiring for the jobs today is looking for someone with added value. What is your added value? Do you speak multiple languages? Do you draw or play a musical instrument? Do you have a hobby that compliments your job? Do you work on puzzles or play chess or similar? How can you prove to someone in the course of an hour or two you have thinking skills beyond the next ten candidates?
Something that is not getting discussed in the tech career space right now is that each job listing you see online is getting around 600+ applicants and it is near impossible to say who is the best fit for the job out of that stack. The interview matters most (as long as you have the correct skill set) and a lot of employers are trying to think outside the box when it comes to hiring by bringing in candidates that have less time in a discipline but have a wider skill set from previous jobs.
If you watch the interviews on freecodecamp on youtube you will never hear anyone say "I want the best of the best for coding python or its a no hire". All of the interviews say things like "I want a creative thinker" and "I want someone that can think outside of the box and solve problems". Its unfortunate but jobs are no longer just what their title is. Your job will be an AND, as in you do the one job AND another job with it. The IT guy at my last company did IT AND graphic design down to the company logo and company T shirts. I work in IT AND I am an electrician. How are you going to fill that role?
I have some random recommendations to get you going, don't stop coding, don't stop doing what you are doing, just slowly add things you can do between studying and coding.
Also, I figure its worth mention, I do not work in the same field as you are studying. I went to school for CS for a semester and dropped out. I do work with AI but I am on the user end primarily and work with hardware serving AI. I know a lot of people who work in your area and I know what skills are desired by people hiring for big tech companies. I guess my advice would be seen as "soft skills"by most coders. Don't take what I said as absolute truth but more food for thought.
Liam recommends the following next steps:
Sandeep Khandelwal
Software Engineer
128
Answers
San Ramon, California
Updated
Sandeep’s Answer
Hi Raheel,
It’s great that you’re thinking about this early. Even with AI growing fast, strong computer science fundamentals are still very important. Focus on learning data structures, algorithms, and programming well (Python is a great choice).
Also try building small projects like a simple web app, chatbot, or data analysis project. Projects help you understand real problems and make your resume stronger.
It’s great that you’re thinking about this early. Even with AI growing fast, strong computer science fundamentals are still very important. Focus on learning data structures, algorithms, and programming well (Python is a great choice).
Also try building small projects like a simple web app, chatbot, or data analysis project. Projects help you understand real problems and make your resume stronger.
Srinivasa Gumma
Lead Data Engineer
3
Answers
Bengaluru, Karnataka, India
Updated
Srinivasa’s Answer
Here are the recommended AI tools. Again, every 6 months, new AI tools are on the market. 😊
• ChatGPT: Best at casual use, deep research, and voice mode
• Claude: Best at writing, instruction following, and task automation
• Gemini: Best at image and video generation. Great for learning
• Grok is a generative AI chatbot developed by xAI, designed to answer questions with wit, humor, and a "rebellious streak".
• Oracle APEX (Application Express) is a low-code development platform that now incorporates generative AI to accelerate app building, allowing developers to generate SQL, debug, and create app components using natural language prompts.
All the best :)
• ChatGPT: Best at casual use, deep research, and voice mode
• Claude: Best at writing, instruction following, and task automation
• Gemini: Best at image and video generation. Great for learning
• Grok is a generative AI chatbot developed by xAI, designed to answer questions with wit, humor, and a "rebellious streak".
• Oracle APEX (Application Express) is a low-code development platform that now incorporates generative AI to accelerate app building, allowing developers to generate SQL, debug, and create app components using natural language prompts.
All the best :)
Eric Brown
Solution Engineer
6
Answers
Fort Collins, Colorado
Updated
Eric’s Answer
Many people worry about AI, but as a computer science student, you're in a strong position.
AI isn't taking over everything; it's changing how we work. I use AI daily and find it works best when you can guide, question, and apply it. The more skilled you are, the more useful AI becomes.
Instead of trying to learn everything, focus on building a solid foundation and adding AI skills:
1. Core fundamentals are crucial
AI can help with coding, but you still need to understand:
- Data structures and algorithms
- Problem-solving
- One programming language (Python is a good choice)
These skills help you use AI effectively, rather than relying on it blindly.
2. Use AI as a tool
Get comfortable with:
- Creating effective prompts
- Using AI for debugging and learning
- Knowing its limits (AI can make mistakes!)
Think of AI as a tool to enhance your work, not replace it.
3. Build real projects
Stand out by creating:
- A simple app using an AI API
- Something automated for your daily life
- A project that solves a real problem
Projects show you can apply your knowledge.
4. Choose a focus
You don't need to know everything about AI. Explore, then specialize in one area:
- AI/ML basics (models, data, training)
- Web or app development with AI
- Data analysis or data science
5. Be adaptable
The key skill is learning quickly. Technology will keep changing, so stay ahead by adapting.
Don't worry about AI taking over. Focus on mastering the basics, building real projects, and working well with AI. This combination makes you valuable and hard to replace.
AI isn't taking over everything; it's changing how we work. I use AI daily and find it works best when you can guide, question, and apply it. The more skilled you are, the more useful AI becomes.
Instead of trying to learn everything, focus on building a solid foundation and adding AI skills:
1. Core fundamentals are crucial
AI can help with coding, but you still need to understand:
- Data structures and algorithms
- Problem-solving
- One programming language (Python is a good choice)
These skills help you use AI effectively, rather than relying on it blindly.
2. Use AI as a tool
Get comfortable with:
- Creating effective prompts
- Using AI for debugging and learning
- Knowing its limits (AI can make mistakes!)
Think of AI as a tool to enhance your work, not replace it.
3. Build real projects
Stand out by creating:
- A simple app using an AI API
- Something automated for your daily life
- A project that solves a real problem
Projects show you can apply your knowledge.
4. Choose a focus
You don't need to know everything about AI. Explore, then specialize in one area:
- AI/ML basics (models, data, training)
- Web or app development with AI
- Data analysis or data science
5. Be adaptable
The key skill is learning quickly. Technology will keep changing, so stay ahead by adapting.
Don't worry about AI taking over. Focus on mastering the basics, building real projects, and working well with AI. This combination makes you valuable and hard to replace.
Ricardo López
Software Intern
3
Answers
Mexico City, Mexico
Updated
Ricardo’s Answer
Hello, Raheel!!
As a current software intern, I’ve learned that AI is gradually becoming part of almost every job. Because of this, understanding the fundamentals of AI is a crucial first step to understand why and how these systems work. Diving into topics like linear algebra can be especially helpful, as it forms the mathematical foundation of many AI concepts. Besides, it's still important to learn data structures many large tech companies heavily focus on this topic during technical interviews, the reason? data structures helps you build efficient, scalable solutions.
As a current software intern, I’ve learned that AI is gradually becoming part of almost every job. Because of this, understanding the fundamentals of AI is a crucial first step to understand why and how these systems work. Diving into topics like linear algebra can be especially helpful, as it forms the mathematical foundation of many AI concepts. Besides, it's still important to learn data structures many large tech companies heavily focus on this topic during technical interviews, the reason? data structures helps you build efficient, scalable solutions.
Kelly Matta
IT Architect
2
Answers
Bridgewater, New Jersey
Updated
Kelly’s Answer
Python, Machine Learning, Excel(learn to do data analysis using this tool, its very powerful) , Get familiar with SQL.
Swasti Agarwal
Consultant
6
Answers
Neuss, North Rhine-Westphalia, Germany
Updated
Swasti’s Answer
Hey! Totally get your confusion—AI is moving fast, and it can feel like everything is being “taken over.” But there’s still a lot of valuable stuff for you to learn.
If I were in your place (4th semester CS) I’d focus on:
Strong fundamentals (non‑negotiable)
Get solid in one main language (Python is great, especially for AI).
Learn data structures & algorithms (arrays, lists, stacks, queues, trees, hash maps, Big‑O).
Understand basics of CS concepts: OS, networks, and databases.
AI‑friendly skills (without needing to be a researcher)
Use Python with numpy/pandas for basic data work.
Learn basic machine learning (supervised vs unsupervised, simple models with scikit‑learn).
Try calling an AI API to build something simple (e.g., chatbot, text summarizer).
Projects over perfection
Build small projects: a web app, a simple ML model, a script that automates something.
Use AI tools as assistants (to explain errors, suggest code), but always understand what the code is doing.
Human skills that AI can’t replace easily
Practice explaining your code to others.
Work in teams (group projects, open source).
You don’t need to have your whole future decided right now. Keep building strong fundamentals, learn how to work with AI, and do small real projects. Those three together will keep you very relevant, no matter how fast AI grows.
If I were in your place (4th semester CS) I’d focus on:
Strong fundamentals (non‑negotiable)
Get solid in one main language (Python is great, especially for AI).
Learn data structures & algorithms (arrays, lists, stacks, queues, trees, hash maps, Big‑O).
Understand basics of CS concepts: OS, networks, and databases.
AI‑friendly skills (without needing to be a researcher)
Use Python with numpy/pandas for basic data work.
Learn basic machine learning (supervised vs unsupervised, simple models with scikit‑learn).
Try calling an AI API to build something simple (e.g., chatbot, text summarizer).
Projects over perfection
Build small projects: a web app, a simple ML model, a script that automates something.
Use AI tools as assistants (to explain errors, suggest code), but always understand what the code is doing.
Human skills that AI can’t replace easily
Practice explaining your code to others.
Work in teams (group projects, open source).
You don’t need to have your whole future decided right now. Keep building strong fundamentals, learn how to work with AI, and do small real projects. Those three together will keep you very relevant, no matter how fast AI grows.
Teklemuz Ayenew Tesfay
Software Engineer and Career Mentor
654
Answers
Ethiopia
Updated
Teklemuz Ayenew’s Answer
Pursuing a computer science major is a fantastic choice, opening up a world of opportunities. Embrace AI as a tool that enhances human abilities rather than replaces them. Those who understand AI will be in high demand as it grows. While AI might take over some basic jobs, it will also create new roles that need human creativity and wisdom. Keep learning and adapting, and you'll always be ahead.
If you're worried about AI taking jobs, think of it as a partner in your journey. It can help you automate tasks, improve your business operations, and make smarter decisions, giving you an edge as an entrepreneur.
Choose a specialization that matches your interests and strengths, future career goals and keep an eye on the job market. Computer science offers many paths, like software engineering, AI, robotics, and quantum computing. You can easily switch between these areas as your interests change. The possibilities are endless!
If you're worried about AI taking jobs, think of it as a partner in your journey. It can help you automate tasks, improve your business operations, and make smarter decisions, giving you an edge as an entrepreneur.
Choose a specialization that matches your interests and strengths, future career goals and keep an eye on the job market. Computer science offers many paths, like software engineering, AI, robotics, and quantum computing. You can easily switch between these areas as your interests change. The possibilities are endless!
Folayemi Adebayo
15
Answers
Grambling, Louisiana
Updated
Folayemi’s Answer
Hi Raheel,
Being a CS student right now is actually a really exciting position to be in because the rise of AI creates more opportunity than it takes away, especially for someone who understands the technical side of things.
The most important thing to recognize is that AI is a tool, and the people who know how to build, deploy, and work alongside it are the ones who will be most valuable. With that in mind, a few areas worth focusing on are machine learning and data science fundamentals since understanding how AI models actually work gives you a significant edge. Python is the dominant language in this space so if you are not already comfortable with it that is the place to start. From there libraries like TensorFlow, PyTorch, and scikit-learn are worth getting familiar with.
Cloud computing is another really strong skill to build since most AI systems are deployed on platforms like AWS, Google Cloud, or Azure. Getting certified in even one of these platforms adds a lot of weight to your resume. Prompt engineering and working with large language model APIs is also becoming a practical and in demand skill as more companies integrate AI into their products.
Beyond the technical side, software engineering fundamentals like data structures, algorithms, and system design are still very much in demand and AI will not replace the need for strong engineers who understand these deeply.
For learning resources, Coursera and fast.ai are excellent for machine learning, and building personal projects that incorporate AI is honestly one of the best ways to stand out. The key is not to learn everything at once but to pick one area, go deep, and build something with it.
Being a CS student right now is actually a really exciting position to be in because the rise of AI creates more opportunity than it takes away, especially for someone who understands the technical side of things.
The most important thing to recognize is that AI is a tool, and the people who know how to build, deploy, and work alongside it are the ones who will be most valuable. With that in mind, a few areas worth focusing on are machine learning and data science fundamentals since understanding how AI models actually work gives you a significant edge. Python is the dominant language in this space so if you are not already comfortable with it that is the place to start. From there libraries like TensorFlow, PyTorch, and scikit-learn are worth getting familiar with.
Cloud computing is another really strong skill to build since most AI systems are deployed on platforms like AWS, Google Cloud, or Azure. Getting certified in even one of these platforms adds a lot of weight to your resume. Prompt engineering and working with large language model APIs is also becoming a practical and in demand skill as more companies integrate AI into their products.
Beyond the technical side, software engineering fundamentals like data structures, algorithms, and system design are still very much in demand and AI will not replace the need for strong engineers who understand these deeply.
For learning resources, Coursera and fast.ai are excellent for machine learning, and building personal projects that incorporate AI is honestly one of the best ways to stand out. The key is not to learn everything at once but to pick one area, go deep, and build something with it.
Ibrahim Ali
Software Developer
21
Answers
Mogadishu, Banaadir, Somalia
Updated
Ibrahim’s Answer
AI is definitely changing the tech industry, but it is not replacing the need for strong computer science fundamentals. Instead, it is shifting the skills that are most valuable. As a CS student in your 4th semester, this is actually a great time to focus on building a strong foundation.
First, make sure your "core computer science fundamentals" are strong. Skills like data structures, algorithms, operating systems, databases, and networking are still essential. AI tools can generate code, but they still rely on engineers who understand how systems work and how to solve problems efficiently.
Second, focus on becoming a "strong programmer and problem solver". Choose one main language (Python, Java, or JavaScript) and practice building real projects. Projects teach much more than theory because they help you understand debugging, system design, and real-world constraints.
Third, learn how "AI actually works instead of only using it". Try studying machine learning basics, statistics, and data analysis. Libraries like Python’s NumPy, Pandas, and frameworks such as TensorFlow or PyTorch are good starting points if you are interested in AI-related fields.
Fourth, develop **skills that AI cannot easily replace**, such as system design, critical thinking, and understanding user problems. The most valuable engineers are those who can design solutions, not just write code.
Finally, start building a "portfolio of projects". For example:
* A small web application
* A data analysis project
* A simple machine learning model
* An open-source contribution
Employers care a lot about what you have built, not only what you studied.
To summarize, focus on:
• Strong CS fundamentals
• Real programming projects
• Understanding AI and data
• Problem solving and system design
AI is best seen as a "tool that makes engineers more productive", not as something that replaces them. Students who understand both software engineering and AI concepts will have very strong opportunities in the future.
Keep learning, keep building projects, and stay curious. You are still early in your journey, which gives you plenty of time to develop these skills.
First, make sure your "core computer science fundamentals" are strong. Skills like data structures, algorithms, operating systems, databases, and networking are still essential. AI tools can generate code, but they still rely on engineers who understand how systems work and how to solve problems efficiently.
Second, focus on becoming a "strong programmer and problem solver". Choose one main language (Python, Java, or JavaScript) and practice building real projects. Projects teach much more than theory because they help you understand debugging, system design, and real-world constraints.
Third, learn how "AI actually works instead of only using it". Try studying machine learning basics, statistics, and data analysis. Libraries like Python’s NumPy, Pandas, and frameworks such as TensorFlow or PyTorch are good starting points if you are interested in AI-related fields.
Fourth, develop **skills that AI cannot easily replace**, such as system design, critical thinking, and understanding user problems. The most valuable engineers are those who can design solutions, not just write code.
Finally, start building a "portfolio of projects". For example:
* A small web application
* A data analysis project
* A simple machine learning model
* An open-source contribution
Employers care a lot about what you have built, not only what you studied.
To summarize, focus on:
• Strong CS fundamentals
• Real programming projects
• Understanding AI and data
• Problem solving and system design
AI is best seen as a "tool that makes engineers more productive", not as something that replaces them. Students who understand both software engineering and AI concepts will have very strong opportunities in the future.
Keep learning, keep building projects, and stay curious. You are still early in your journey, which gives you plenty of time to develop these skills.
Harsha Priya Ganapathy
Independent AI, ML, DL Researcher at AWS Cloud
27
Answers
Dallas, Texas
Updated
Harsha Priya’s Answer
Hello! I'm Harsha Priya Ganapathy. I work in AI/ML, full-stack systems, and mentor students in cloud and AI.
I understand why you're confused—many CS students feel the same way. Let me make things clearer for you.
First, it's important to know that AI isn't taking over everything. It's mostly handling low-level, repetitive tasks. So, aim to be one step ahead of AI.
Here's what you should focus on for the future:
1. Strong Fundamentals:
- Learn data structures and algorithms, problem-solving, and system thinking. These are crucial for interviews and real-world systems.
2. One Strong Language:
- Python is recommended because it's used in AI/ML, backend development, and automation.
3. AI and ML (Practical Focus):
- Start with Scikit-learn and basic ML models, then explore tools like HuggingFace for NLP. Focus on using AI effectively rather than building complex models like GPT.
4. Build Real Projects:
- Projects like an AI chatbot, data analysis dashboard, or a mini ML project are more valuable than certificates. Real projects showcase your ability to combine AI with real-world data.
5. Cloud and Deployment:
- Learn the basics of AWS or Azure and how to deploy applications. This skill is often lacking among students.
6. One Specialization:
- Choose one area to specialize in, such as AI/ML, backend development, data science, or cybersecurity. Don't try to master everything at once.
My personal strategy is simple: Learn, build, apply, and repeat. Avoid taking too many courses without practical application.
Focus on what AI can't replace: problem-solving, system design, real-world understanding, and communication.
Here's a simple roadmap for you:
- Next 2–3 months: Learn Python and DSA basics, and complete 1–2 small projects.
- Next 3–6 months: Learn AI/ML basics and build one solid project.
- Next 6–12 months: Learn cloud and deployment, and work on an internship or portfolio.
Remember, you don't need to compete with AI. Instead, learn to use AI better than others.
If you're confused about what to learn, focus on skills that help you build real solutions, not just theory.
One key takeaway: AI won't replace you, but someone who knows how to use AI might.
I understand why you're confused—many CS students feel the same way. Let me make things clearer for you.
First, it's important to know that AI isn't taking over everything. It's mostly handling low-level, repetitive tasks. So, aim to be one step ahead of AI.
Here's what you should focus on for the future:
1. Strong Fundamentals:
- Learn data structures and algorithms, problem-solving, and system thinking. These are crucial for interviews and real-world systems.
2. One Strong Language:
- Python is recommended because it's used in AI/ML, backend development, and automation.
3. AI and ML (Practical Focus):
- Start with Scikit-learn and basic ML models, then explore tools like HuggingFace for NLP. Focus on using AI effectively rather than building complex models like GPT.
4. Build Real Projects:
- Projects like an AI chatbot, data analysis dashboard, or a mini ML project are more valuable than certificates. Real projects showcase your ability to combine AI with real-world data.
5. Cloud and Deployment:
- Learn the basics of AWS or Azure and how to deploy applications. This skill is often lacking among students.
6. One Specialization:
- Choose one area to specialize in, such as AI/ML, backend development, data science, or cybersecurity. Don't try to master everything at once.
My personal strategy is simple: Learn, build, apply, and repeat. Avoid taking too many courses without practical application.
Focus on what AI can't replace: problem-solving, system design, real-world understanding, and communication.
Here's a simple roadmap for you:
- Next 2–3 months: Learn Python and DSA basics, and complete 1–2 small projects.
- Next 3–6 months: Learn AI/ML basics and build one solid project.
- Next 6–12 months: Learn cloud and deployment, and work on an internship or portfolio.
Remember, you don't need to compete with AI. Instead, learn to use AI better than others.
If you're confused about what to learn, focus on skills that help you build real solutions, not just theory.
One key takeaway: AI won't replace you, but someone who knows how to use AI might.
Tianxin Shao
Software Qualification
1
Answer
Ottawa, Ontario, Canada
Updated
Tianxin’s Answer
Hi Raheel,
Even AI growing fast, the foundation are still very important in computer science like advanced algorithms, and know what's the high effective programming (recommend book Fluent Python).
Keep in reading the latest AI trend and news, which is happened very quickly, of course try building small projects like web app, chatbot, or business project. Keep communication with your peers.
CS is still a glorious career in AI age, keep going!
Even AI growing fast, the foundation are still very important in computer science like advanced algorithms, and know what's the high effective programming (recommend book Fluent Python).
Keep in reading the latest AI trend and news, which is happened very quickly, of course try building small projects like web app, chatbot, or business project. Keep communication with your peers.
CS is still a glorious career in AI age, keep going!
Pushkar Padmanabhuni
Software Engineer
1
Answer
Hyderabad, Telangana, India
Updated
Pushkar’s Answer
Hi Raheel,
You have a good idea and a great way of thinking about your career path. Although AI is growing faster now, it's essential to learn some programming languages for AI development (Python is the best to learn and implement easily). To understand the impact of AI on day-to-day life, examine real-world scenarios and then execute a small project that would benefit your career.
Your initial focus should be on academic subjects; however, you can also learn advanced AI topics.
You have a good idea and a great way of thinking about your career path. Although AI is growing faster now, it's essential to learn some programming languages for AI development (Python is the best to learn and implement easily). To understand the impact of AI on day-to-day life, examine real-world scenarios and then execute a small project that would benefit your career.
Your initial focus should be on academic subjects; however, you can also learn advanced AI topics.