| Slack Thread |
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Nov 9th at 11:22 PM |
| Hello guys, I am currently studying in 2nd year of engineering in computer science and my main problem is that I am confused about what to choose in tech. My classmates are far better as of now when compared to me. When I ask my friends someone say learn webdevolepment and some say do machine learning but my teachers say now a days there is demand for ML (machine learning and AI).SO I am totally confused I want some clarity that only ml and AI fields are better to go through or other techs are also as important as AI and ML. can anyone tell which one to choose and I am focusing more for placement. |
| Hi, It's quite common to worry about which technology to choose when you are in college. But, the best way is to work on your basic Computer Science concepts. If you have a very strong foundation, you will be doing good in almost every technological field in the future. The key is to develop your coding skills. At this point, I would suggest you to focus on Data Structures and Algorithms because that builds up your base. Most interviewers do not focus on how many projects or technologies you are aware of or have worked on. But they definitely care about your understanding of basic computer science concepts. Once you are done with that, you can choose any project(either Web Development or ML), it is totally your choice. 😀 |
| In my very honest opnion, do all the suggested task given in your compulsory classes. Complete all your tutorial task and questions. Identify your strength and work towards it. Machine learning and AI is in demand, and at the same time, there are other things sprouting in the background. By the time you graduate and are ready for the industry, there might be something else which could be more popular than ML or AI. Since Win 11 has been rolled out, there 'maybe' lots of opportunities in security side of programming. The IT industry has endless amount of opportunities. No matter what path you take, give it your best shot. Machine learning and AI is also promising but it all depends on which part are you planning to specialize in. If you have any data set, try and discover knowledge from that just to get a feel of it. If you don't have any data set, use the Covid-19 data which is freely available. When I was in high school, my dream was to program a chess engine, but someone else programmed stockfish. |
| +1 to Anantshree’s answer. In college it’s best to put most of your focus in DSA and little bit on core subjects(OS,DBMS,Networking). You don’t need to be worried about demand in specific technologies. Every field has shortage of talented engineers. Which field you choose is more of a personal choice. If you have strong fundamentals you’ll be able to transition into anything once you start working. |
| What are you interested in? Or rather why did you choose computer science/engineering? I personally is a believer in passion-driven work, so that you'd ideally getting paid to work on your hobbies. Sure that anything related to big data would be in high demand in the near future, but other cs-related jobs aren't dying out either. Also similar to what others already suggested, work on the fundamentals. You don't need to know about red-black tree per se, though you should know about bst. Regardless of the cs-path you eventually take, these fundamentals will always be needed. |
| Software Development Manager at Amazon here. When we hire university students, we look for fundamentals instead of specialization in any particular thing. +1 to the comments here that say you should focus on data structures, algorithms and runtime complexity. Unless you have a master's degree in ML or something similar, it will be tough for you to get hired straight out of college without prior work experience, to do work in that space. |
| Just to add to the good things our colleagues said above, if you want to see if AI interests you, just go to www.kaggle.com, the biggest platform for AI enthusiasts, where you can find datasets, forum discussions, notebooks with explanations, Q&A sections and even competitions. Another good place for datasets and state-of-the-art things in AI is www.paperswithcode.com. |
| @Alex Chiou we need to save this slack thread for any future ques by grads - when they are at crossroads in choosing tech |
| Done! https://github.com/Gear61/Tech-Career-Growth-Learning-Resources-And-Roadmaps/blob/main/threads/Choosing%20Your%20Path%20In%20Tech.md |
| I am pasting my answer to a similar question which was asked earlier- What I have done when I was exploring the same, was to ask experts from these fields. For eg, i did a two month part time job/internship with a devops consultancy under a devops expert to know what devops is and what that career entails. Then i tried to check if that aligns with my goals of being open to/have opportunity of freelance work/ teaching/bigtech job/startup job etc. I also tried to talk to professors in the University about the kind of work/research I could do in ML/Data Science, and checked up on youtube generally what Data Scientists/ML Engineers work is. I talked to my friend who is masters in ML and another who is doing PhD in ML, about careers in both fields and what kind of aptitude/skills are required to excel in it, and also what kind of opportunities I can get in future with these options. I realised I like software dev more than the degrees of mathematics/ML/DS I need to have to be able to contribute to the field/get job as a core ML scientist in a company. Lastly, I had been doing web dev and really liked people being able to use products directly to sovle their problems (personal liking factor). Then tried to see how mobile dev will play for my career and for similar opportunities. So i realised i need not worry about that anymore, as I could get a job i like which pays me well which will keep other options open as i want, but if in future I need to explore mobile dev, I would not have issues in doing so. That's how, i boiled down to web dev. In conclusion its about- exploring, asking the experts in the field, listening to others who want to enlighten about the field and what it entails (youtube videos etc), check the risk vs reward ratio (how many days/years you need to invest), check the opportunities available (and general pay), check if that gives you the options you want/like, check if you like doing that, you have the aptitude to excel in it etc. You can also check if you like the community of those people. |