Is it too late to start preparing for data science 4–5 years from now?

（原文是用英文写的，我自己能说中文，但翻译转换太烂，GPT翻译也不太对的上意思，干脆把最流畅的原文贴在这儿啦）
Hi everyone,



I’m a first-year international student from China studying Statistics and Mathematics at the University of Toronto. I’ve only taken CSC108 so far (not CSC148 or CSC165), so I don’t have a solid CS background yet — just some basic Python.



Right now I’m trying to figure out which career path I should start seriously preparing for: data science, actuarial science, or something in finance.



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1. Is it too late to get into data science 4–5 years from now?  

I’m wondering if I still have time to prepare myself for a data science role after at least completing a master’s program which is necessary for DS. I know I’d need to build up programming, statistics, and machine learning knowledge, and ideally work on relevant projects and internships.



That said, I’ve been hearing mixed things about the future of data science due to the rise of AI, automation, and recent waves of layoffs in the tech sector. I’m also concerned that not having a CS major (only a minor), thus taking less CS courses could hold me back in the long run, even with a strong stats/math background. Finally, DS is simply not a very stable career. The outcome is very ambiguous and uncertain, and what we consider now as typical "Data Science" would CERTAINLY die away (or "evolve into something new unseen before", depending on how you frame these things cognitively) Is this a realistic concern?



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2. What about becoming an actuary instead?  

Actuarial science appeals to me because the path feels more structured: exams, internships, decent pay, high job security. But recent immigration policy changes in Canada removed actuary from the Express Entry category-based selection list, and since most actuaries don’t pursue a master’s degree (which means no ONIP nominee immigration), it seems hard to qualify for PR (Permanent Residency) with just a bachelor’s in the Express Entry general selection category — especially looking at how competitive the CRS scores are right now.



That makes me hesitant. I’m worried I could invest years studying for exams only to have to exit the job and this country later due to the termination of my 3-year post-graduation work permit. The actuarial profession is far less developed in China, with literally bs pay and terrible wlb and pretty darn dark career outlook. so without a nice "fallback plan", this is essentially a Make or break, Do or Die, all-in situation.



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3. What about finance-related jobs for stats/math majors?  

I also know there are other options like financial analyst, risk analyst, equity research analyst, and maybe even quantitative analyst roles. But I’m unsure how accessible those are to international students without a pre-existing local social network. I understand that these roles depend on networking and connections, just like, if not even more than, any other industry. I will work on the soft skills for sure, but I’ve heard that finance recruiting in some areas can be quite nepotistic.



I plan to start connecting with people from similar backgrounds on LinkedIn soon to learn more. But as of now, I don’t know where else to get clear, structured information about what these jobs are really like and how to prepare for each one.



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4. Confusion about job titles and skillsets:  

Another thing I struggle with is understanding the actual difference between roles like:

• Financial Analyst  
  
• Risk Analyst  
  
• Quantitative Risk Analyst  
  
• Quantitative Analyst  
  
• Data Analyst  
  
• Data Scientist  
  
  
  
  They all sound kind of similar, but I assume they fall on a spectrum. Some likely require specialized financial math — PDEs, stochastic processes, derivative pricing, etc. — while others are more rooted in general statistics, programming, and machine learning.
  
  
  
  I wish I had a clearer roadmap of what skills are actually required for each, so I could start developing those now instead of wandering blindly. If anyone has insights into how to think about these categories — and how to prep for them strategically — I’d really appreciate it.
  
  
  
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  Thanks so much for reading! I’d love to hear from anyone who has gone through similar dilemmas or is working in any of these areas.
  

英语好坏先放一边。你给外国人发信，你的格式先搞搞正确。

I've  你写 I'  ve..你到底打了几个空格，看的好别扭。估计老外看了感觉比我感觉还难受。

smurf1 发表于 2025-4-22 18:39
英语好坏先放一边。你给外国人发信，你的格式先搞搞正确。

I've  你写 I'  ve..你到底打了几个空格，看 ...

这个也有可能是输入法的锅，不过确实建议打英文的时候把中文输入法关掉。

smurf1 发表于 2025-04-22 16:39:21
英语好坏先放一边。你给外国人发信，你的格式先搞搞正确。

I've  你写 I'  ve..你到底打了几个空格，看的好别扭。估计老外看了感觉比我感觉还难受。

在Reddit上正常的，应该是地里Markdown render的问题

前Torontonian，看到了非常熟悉的数字啊……

个人思路：
- 没有人能预测4年之后的情况，所以对于绝大多数的问题，我只能回答“不知道”
- 最近听到的一个想法是建议学Mechatronics相关，因为最近两年，AI开始转向实体，然后这个领域就因此开始缺人了。不过这个必须在Engineering Department，你应该是Arts and Science，是转不过去的
- 多大本科的一个很重要的任务是打下数学基础，既然你是stats and MATH，我建议你MAT课打满（240+247+257+267+347+357这条线），毕竟只有数学基础好的人，才擅长应对变化

denev2004 发表于 2025-04-22 17:45:16
前Torontonian，看到了非常熟悉的数字啊……

个人思路：

我是137+237+337这个track的。224/244/246/337 MAT247也都选了。剩下MAT301/315/334没有选，实在用不上

不安的南瓜 发表于 2025-4-22 19:54
我是137+237+337这个track的。224/244/246/337 MAT247也都选了。剩下MAT301/315/334没有选，实在用不上

面对未知最好的办法是准备自己的综合实力，而mathematical maturity本质上是综合实力的一种，我不建议用“用不上”这个思路来

而且CS不一定不用301/347的：https://en.wikipedia.org/wiki/Graph_isomorphism_problem

至于track，237这个track也不是不行，不过大部分想学4xx级别的数学课的人，基本上要么不会在237上，要么也会在大三补257的直接后续课357。所以，这方面你看你的数学学习能力吧。你要是觉得137太无聊了，而且137成绩高，去跟BA 6楼聊，他们会允许你大二选257的。

denev2004 发表于 2025-04-22 18:08:19
面对未知最好的办法是准备自己的综合实力，而mathematical maturity本质上是综合实力的一种，我不建议用“用不上”这个思路来

而且CS不一定

我137应该88左右。A+以下一点。257可能心有余力不足。而且我没有CS Major，以后大概率干不了SWE。我知道数学深能打好基础，可这个基础究竟能怎么落到实处，给自己创造更好的生活呢？统计研究生的话237，337基本就够了；数学研究生真不确定自己能不能学的来，学出来可能还是穷

denev2004 发表于 2025-04-22 18:08:19
面对未知最好的办法是准备自己的综合实力，而mathematical maturity本质上是综合实力的一种，我不建议用“用不上”这个思路来

而且CS不一定

你说的S347是STA347概率论吗？这个我会上的

不安的南瓜 发表于 2025-4-22 20:22
我137应该88左右。A+以下一点。257可能心有余力不足。而且我没有CS Major，以后大概率干不了SWE。我知道数 ...

我不知道这个基础怎么能落到实处，但是同样的，现在是一个多变的社会，我也不知道任何不是基础的，能在4年后，落到实处的东西。
这就是为什么我考虑到你的专业，外加你本来就对Data Science有疑问，所以推荐你多上数学基础课。
Mathematical maturity是能跟你一辈子的。