Career change at 40s

Hi all,

I am thinking of career change from finance to IT (data bases and analysis + python), but I have some doubts regards my age - I am at about 40. I know that not only age is important, there are many more factors involved, but still it is not the best age to do U turn.

Has anyone had successful switch as similar age? Please, can you share your story? Can you give any tips or advice?

Thank you
S

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Hi @Saul, I am in a similar position as you are. I’m 45 (turning 46 in a week) and am interested in making a career change. I’ve been working as an IT/Software Project Manager for the past 20 years and trying to pivot into Data Science. I have been learning on DQ and on my own for the past year and change but don’t have a success story to share just yet. I’m about 90% on the Data Scientist path (have gone through a lot of it twice though) and am planning to start looking for a new job starting in early 2020. I’ll definitely share my experience here and would also love to hear from others of a similar age/position.

Best of luck to you in the new year!
Steve

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Hello @Saul! Thanks for posting your question. Your situation is similar to mine, so I’d be more than happy to share my story and offer some advice.

Here’s my story in a nutshell… I’m 38 and joined Dataquest as a Data Scientist and Content Author (for the R language) in June, 2019. I transitioned into this position after a 11 year career in environmental consulting for a global consulting firm. I got hooked on data science and coding and was able to apply my skills pretty quickly on the job at the consulting firm. I chose R over Python because the company I worked for had a few-dozen R practitioners, but very few consultants using Python.

So that leads me to a question and my first piece of advice. Do you know anybody in your field that uses Python for financial analysis? If so, it might be worth connecting with them. It it possible to “pivot” your career instead of “U turn”? If you are able to apply data analysis skills in any way within your current field, this can be a way to have some overlap between your career and your learning aspirations.

Also, as you build your data science skills it is important to apply your coding skills to real (i.e. “messy”) datasets. The Guided Projects on the Dataquest platform are one way to do this. I also found it useful to apply the workflow from a Guided Project to my own personal datasets. And it does not have to be a fancy or elaborate dataset either. I learned a great deal about coding, visualization, iteration, and linear regression from a publically available real estate dataset that had less than 2,000 rows. So, getting back to my point from above…if you are able to apply coding skills to financial datasets that you are familiar with then you may find that you can get a bit deeper into the analysis because of your domain expertise.

Finding time to learn is one of the most important assets to manage when one is already immersed in a career. My advice there is to chase what motivates you. There are many data-adjacent careers out there. It’s important to stay motivated by what interests you so that when the coding difficult and you are confronted with debugging challenges you have the motivation to push through.

As you probably have seen, Dataquest has a few paths to choose from. If you are interested in analysis and databases, then any of our python paths should have relevant options for you. The Data Analyst and Data Scientist paths will cover analysis and database fundamentals. And the Data Engineering path goes deeper into databases.

Best wishes on your learning journey and let me know if you have any follow-up questions! Best,
-Casey

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Hi again @Saul. There’s a great post here from @bvalgard that I’d recommend as well.

Best,
-Casey

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Hi Saul, I am in a similar position - scratching the 40 mark and decided to make a career change. While I cannot offer you a success story being new to DQ, I can tell you that the best time to make that kind of decisions is when you feel the desire for it.

Sure, it would have been better while you were still single, didn’t have a full-time job and the need to provide for a family. But hey, the master will come when the student is ready.

Just stick with it.

p.s. Maybe we can create a support group, old-age-carrer-changers or smth :slight_smile:

@casey, link doesn’t work (for me).

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Hi @Bojan, thanks for letting me know the link did not work for you. Does this link work any better for you?
https://community.dataquest.io/t/looking-for-guidance-on-transitioning-into-data-science/92561

No, same result.

@Bojan, thanks for letting me know! I’ll look into this

Hi @casey, thank you for sharing your story and advice, that keeps me motivated. I agree with everything what you say and especially with the time management part. As a freshly baked father of twins, I can say that time now has absolutely different value to me :smiley:

The problem that I am trying to solve is that my career path is quite mixed and inconsistent - finance, banking, finance, half year in IT with Oracle and SQL, then emigration (unfortunately, most of the skills were not transferable), now accounting. Some companies are quite happy to see such variety of skills in one person and in some situations that works quite well, but in general narrow and deep specialisation is in much bigger demand. So I’ve started to look to develop skills that could be easier transferable and Data analysis, Python, SQL look quite good addition to the overall skill set.

You are very right saying that it would be better to find someone who already has got skills and do a pivot instead of U turn. I know what you mean and that would be the most effective way of achieving it, but unfortunately I have no such person. Actually I’ve been trying to find someone trough various mentoring programs (that is formal way and that is in UK) to fast track trough career path, but no success as well. Anyway, DQ is great place to do self study and I am here.

By the way, the link to the article is not working:
image

S.

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Hi Bojan, you are very right here. In one of the company’s I used to work, there was a guy at about 75 yo working as full time Oracle developer and he was doing quite well there. So age is not limit, usually there are other limiting factors (like limited time or environment suitable to learn and develop new skills).

I think it is a good idea, many of us are here for this reason, but I wouldn’t call it old age :slight_smile:

S.

Hi @Saul, I just got out of the same situation that you’re in, and can tell you that it’s absolutely possible to do a U turn at ~40! I’m 38, and over the course of the past 10 months I left a job as a researcher at a law firm, spent about 6.5 months learning python, SQL, etc, and just over a month ago I got hired at a startup as a Data Engineer. My situation was a bit unique because I was able to devote myself to learning full-time for those 6.5 months, so the timeline may be longer for others that can only do part-time learning, but it’s still possible as long as you stick with it and keep applying to as many jobs as possible.

I’ll also say that going into my transition, I was very concerned that age would be an issue for potential employers, but it turned out to never be an issue (at least that I was aware of). All they cared about was whether I had the data skills they were looking for. Although, it is a little strange to now be 10 years older than all of my co-workers, haha!

Hopefully this helps, and I wish you all the best in your career change! :smile:

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If I’m not mistaken, this is probably because the career services category is only accessible to Basic/Premium members.

@blueberrypudding85, thank you. You are correct!

Sorry for the confusion about the link everybody. I’ve contacted the author of the other post and asked them to share it here.

Hey guys,

See if this link will work.

https://community.dataquest.io/t/looking-for-guidance-on-transitioning-into-data-science/92561/3?u=bvalgard

nope, still the same for me:

Here is the response to the post. It was responding to someone looking to transition from a data science role to a data science role.

I’m in a similar situation as you. I am currently working as a data analyst (DA), but am hoping to transition to a data science (DS) role.

Dataquest is a great place to start. It gave me a foundation in DS and has also helped me know enough to know what I should learn next.

Now a days, DS roles are becoming more and more competitive which means we need to work harder to transition smoothly from a DA position to a DS position.

Here are some of the things I have been doing (in addition to Dataquest) to help me with this transition.

  1. Focus on your goals? Is there an field that you want to apply your DS skills to (health care, business, criminal justice, education, etc.)? Gaining domain knowledge can be a great way to get a leg up on your competition. This was the case for me getting my DA job, and from what I have read and heard, it is the same with DS.
  2. Get involved in the DS community.

a) Attend DS related meet up groups. I downloaded the app “Meetup” and found a group in my city that explores different DS related topics once a month. This has been a perfect opportunity to get exposed to DS in the real world, but also to network and make connections.

b) Look for companies you would like to work for as a DS. Follow those companies on LinkedIn and other social media platforms. This can help you learn more about a company (so you can really personalize a cover letter) and to show that you are interested in the company and not just any job.

c) Answer questions on the Dataquest community forum :wink: This can be a fun way to practice what you learn and help others out in return.

  1. Practice, the skills you learn. After deciding what types of area of DS you wan a job in practice the skills most relevant to that area. For example, if you are interested in detecting brain tumours in the health field, focus on developing your skills in image recognition. It maybe difficult to find data on this specific topic, but develop the skill so that it is obvious you have the capacity to contribute in your area of interest when applying for a job.
  2. Apply what your learning at work. At my job, I have access to data that I can practice machine learning techniques at work. This can be a great way to show you are capable of applying machine learning/DS techniques in the real world to solve actual problems.
  3. Be prepared. As an experienced DA it might be difficulty to transition to a DS position without a pay cut. I am hoping that following the above, will help me (and hopefully you too) to make the transition as smothly as possible. However, it is likely that moving from and expert in your current field to a beginner in DS you will likely receive a pay cut. Is this worth it? If so, plan now for that possibility.

Hope you found this helpful. If you come up with any other ideas that you find helpful it would be great if you shared it on this post for myself and others in a similar situation.

Best of luck,

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I’ve opened a slack group to hang out and help each other out. Feel free to join.

Obviously, you should be 35+ and interested in a career switch (or completed one).

p.s. If this goes against the forum rules I’ll remove the post.

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Saul,

I’m somewhat older than you +7 . Not that I really care… :slight_smile: My path was and is IT (developer, sysadmin, devops, support). Several months ago I started with data science company in support capacity and push heavily towards switching carreer to DS completely. I’m taking online courses and DS Masters program online. I will see where it will lead me. I’m not giving up, you should not too. :slight_smile: Industry has a lack of data science and machine learning expertise… I doubt that will be addressed soon. :). In the end, if you are good DS / ML you can learn living by competing in kaggle and consulting. :roll_eyes:

Check out Kaggle’s latest skillset analysis of DS…

https://www.kaggle.com/paultimothymooney/how-to-explore-the-2019-kaggle-survey-data

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@dmitryk why did you decide to pivot into DS already being in IT? I guess the pay is similar…

In the end, if you are good DS / ML you can learn living by competing in kaggle and consulting.

Can you elaborate on that? Is the prize pool so big on Kaggle, never looked into this really.

@Bojan Good question… “Been there, done that” - mentality :slight_smile: I consider IT as more in a sense of civic engineering type of roles… where you deal with piping, highways… etc. I like treasure hunting, chasing solutions and creativity that DS brings. I do believe that expertise in multiple areas does make you more marketable. In the end, even with IT, I have always been driven to the analytical side of it.

My view on the “age” - that it is a limiting belief. Expectation are higher from you as you get older. The companies want to see expertise, the wisdom you bring to the table. You only compete with the younger generation if you decide to do that.

As of “time left”, I want to follow the lead of Steven Hawkins and die solving universe problems not playing bingo. :roll_eyes:

We think of this as a career change move- pivot as we normally describe it… yet it is more next level of spiral for me :smile:

Today, I’m in a much better position to learn and develop skills then I was before. I have a job, income and I don’t have to prove my IT expertise. I have a choice to go deeper (more IT) or wider (other fields like DS). I choose fun of DS for now.

Hope that my personal inside helps here :slight_smile:

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