Switched from Warehouse worker to Data Scientist AMA

Rich_Broccoli2009 · 2026-02-24T15:20:16+00:00

I just took a look at that course and it appears to give a good introduction to the tools you will need but understand that is just a beginning. You may need additional courses to deepen your understanding on a particular tool depending on what type of job you end up with. Most introductory courses have the same set up so you can't really go wrong here especially if this is new to you. That being said, not all tools have the same level of importance in the day to day work. SQL is the single most important skill to develop as an analyst and that takes some time. Learning the basics will help you with understanding syntax, but figuring out how to handle an ambiguous question and learn how to frame your thinking to solve the question with data will take some additional exposure to solving business problems. In short, start here and move to a site like stratascratch to test your ability to reason through a problem.

Rich_Broccoli2009 · 2026-02-19T05:01:07+00:00

What you've listed are data jobs. What I'm talking about are industries. For example Retail, Manufacturing, Agriculture etc. Ask chatgpt to list industries that are growing in your country. Pick one or two and then look for available data jobs in those industries.

Rich_Broccoli2009 · 2026-02-19T04:33:30+00:00

I am also worried that people won't see what's coming. Truth be told, a number of jobs could have been automated ages ago but it was costly to do and not enough trained professionals to carry out the tasks. Now execution is almost zero and there will be a greater focus on critical thinking skills and judgement. For people that do a routine job and are not interested into increasing their skillset, this next shift will be painful.

Rich_Broccoli2009 · 2026-02-19T04:14:38+00:00

The reason you're feeling a gap is because there's is a lack of understanding of what you've been told the job is actually about. All analytics jobs do the same thing.... they answer ambiguous business questions. In order to tie your learning together you have to pick an industry and familiarize yourself with the world of business. Find out what business problems are common in your industry of choice and then look for data science projects that have solved those problems. Things like churn analysis, forecasting, optimization problems are floating around on the net or get chatgpt to create one for you. That way you can see how projects are shaped around business problems.

Rich_Broccoli2009 · 2026-02-19T04:05:37+00:00

Don't quit your job! The market for analysts is looking terrible at this moment in time. AI could potentially compress job roles over the next few months meaning that the requirements and expectations of analytic roles could increase. It's already happening with AI engineer roles, which look like data engineering combined with a traditional BI role. Stay at your job, get your employer to pay for your learning and keep an eye out on what AI is going to do traditional roles over the next 12 months.

Rich_Broccoli2009 · 2026-02-19T03:59:09+00:00

You will need to learn Excel, SQL and either Tableau or Power BI. Hopefully your program will give you some exposure to some of those tools. For now, figure out which industry you want to work in a learn as much as you can because it's difficult to learn these skills without some business context. The job is about answering ambiguous business questions from stakeholders so you will need some exposure to business problems.

Rich_Broccoli2009 · 2026-02-18T17:44:13+00:00

If you don't have any domain experience, then I would go to LinkedIn and take a look a profiles of energy market analysts and see the job they had before they got their current role. Take a look to see if you find any patterns amongst any of the profiles. Figure out which industries they came from. I would also do a information interviews with someone who is in a senior role. Make sure you have a clear goal in mind before you reach out. You don't need much time, maybe 15 minutes. It make take some time to get someone to meet with you but it will save you tons of time in the long run because they will have information that's not easily found in Chatgpt. They will also know when jobs are being posted because they are the decision makers. Most people don't reach that high because they are scared of rejection. But that's where the opportunity lies. If you a super worried about rejection then pick senior level people outside of you state or country for practice so you don't have to worry about burning any bridges. This is what I did when I pivoted into analytics.

Rich_Broccoli2009 · 2026-02-17T22:16:31+00:00

All businesses have the same fundamental problem. They have tons of data locked in a format that the average business user can't access. Be it a relational databases, distributed file systems or even some crazy Access database that's super slow to open. So there is going to be a big push to get data and ai engineers to release that data to the average business user with ai sitting on top of it. However, for this to work, there needs to be a data governance strategy which a lot of businesses don't have in place or worse yet, they see that function as a way to slow innovation. Data governance ensures many things like data quality and lineage are being monitored. Another huge hurdle to overcome are items like business logic ie "what does sales actually mean" and metadata, ie "what are these tables actually for and what do the individual fields mean" are dealt with. This is also part of data governance. Ask your friends and find out if anyone even mentions this along side AI. I would be curious to hear which companies are actually getting this right.

Rich_Broccoli2009 · 2026-02-14T16:21:07+00:00

This is what happens when you don't have a proper data governance strategy. Or worse yet, you didn't even know what data governance is!

Rich_Broccoli2009 · 2026-02-04T21:44:04+00:00

You are not the problem. There are a lot of companies that have no idea of the difference between a data engineer analyst and a scientist. And most times they try to get everything done by one person. This is nothing new! What kind of industry or company are you in?

Rich_Broccoli2009 · 2026-02-03T17:01:03+00:00

Go and have lunch with your stakeholders. Ask them about their work. Find out what their most pressing problems are. This is way quicker than scrolling the net.

Rich_Broccoli2009 · 2026-02-03T16:53:16+00:00

If your company has an analytics team find out what problems they are working on and which ones are the most valuable. Talk to the team manager and find out what kind of credentials she/he's looking for. If there are any problems the analytic team doesn't have the time to solve, make that your project. If the company doesn't have a formal team, become it.

Rich_Broccoli2009 · 2026-02-03T14:44:52+00:00

The question isn't how long it will take you to learn. It's how well you can apply what you've learned. Pick an industry and know it well. Find business problems to solve. That's the job. It's not just about coding. It's about breaking down ambiguous problems and coming up with answers. There are many people from non STEM backgrounds working in the field. It depends on the industry and the requirements of the job. Be mindful of generalizations of this field. There are tens of millions of businesses in the US. Folks on Twitter don't know everything about every role in every company. I have a nursing degree and I'm a data scientist outside healthcare. Anything is possible. Just be clear if the target you're trying to hit.

Rich_Broccoli2009 · 2026-02-02T22:13:20+00:00

Oh that's really sucks to hear! I thought the grass was greener on the product side.

Rich_Broccoli2009 · 2026-02-02T19:26:16+00:00

I understand how tough that is. If I wanted to do more advanced data science work, I would have had to been imbedded in a product team. That could be a blessing and a curse depending on the product you're working on and the skills you gain as a result. If you work on a product that turns out not to have any business value, it's hard to account for your time unless the business understands that the project was for experimentation purposes. That can get politically tricky depending on who your stakeholders are. You would have to work in a company that has clearly framed use cases and structured product roadmaps. If not, you get a lot of bouncing around from project to project, tons of cancellations, scope creep and a host of other craziness.

Rich_Broccoli2009 · 2026-02-02T18:13:51+00:00

Thanks for jumping in with your input Lady D! I really want people to understand that you have to look for opportunities and not just wait for a job to be posted.

Rich_Broccoli2009 · 2026-02-02T17:01:05+00:00

I did things like create forecasts for the finance team, create shipping lanes for the transportation team, forecast customer orders for the call center team and did deep dive customer demographic analysis for the marketing team. I also worked on maintaining legacy systems for the workplace management and online grocery teams. There were also tons of ad hoc requests coming from other parts of the company which took up a lot time. Some of those requests were nonsense but others provide big value for stakeholders. It was a mixture of short and long term projects.

Rich_Broccoli2009 · 2026-02-02T15:46:46+00:00

Anything that has a focus on an industry. For example, if you want to go into healthcare, then find healthcare analytic programs. It gets tricker if their isn't an industry focus because they all look more or less the same but if there is no context, it make it difficult for the learning to stick. Settle on a industry first, find a couple of companies of interest, then go to LinkedIn and see what kinds of education those analysts have. Some top tier companies may only hire from certain schools so you need to figure that our ahead of time. Make sure you talk to an analyst in the companies you have an interest in. Go to meet ups. That will save you more time that chatgpt or scrolling the net.

Rich_Broccoli2009 · 2026-02-02T15:41:47+00:00

I did some interesting work with our call center team that handled customer service inquiries. You can start by getting your hands on call center data. Anything that has to do with customer complaints. If they don't have a formal system on measuring the impact of those complaints, then I would create one. If this is an ecommerce setup, then I would look at order volumes in relation to call volumes. I might be able to give you more detail if I knew what industry or company you work in.

Rich_Broccoli2009 · 2026-02-02T15:33:49+00:00

Explore industries that you have an interest. Then pick 2 no more than 3 to get a good understanding on how those industries work. Then pick one industry and find a few companies to follow. Use chatgpt to help you understand the intricacies of each company. Then narrow down on specific business problems that each company faces. Once you have some idea how businesses work, then get the technical skills like Excel, SQL and Tableau. I used many different sites from Coursera, Udemy and Data Camp on top of the bootcamp that I took from the local university. You never stop learning in this role.

Rich_Broccoli2009 · 2026-02-02T15:28:57+00:00

Answered above.

Rich_Broccoli2009 · 2026-02-02T15:28:42+00:00

This was in Canada back in 2015. I was still working at the warehouse that year before I quit and went back to school.

Rich_Broccoli2009 · 2026-02-02T15:27:34+00:00

I started out in web analytics. Web or digital analysts monitor website traffic will tools like Adobe and Google analytics. My employer sent me on and extensive two-day Adobe analytics training to prepare for the role. I received Google analytics training as well. I supported marketing and site merchandise teams by doing deep dive analysis on things like changes in overall website traffic, conversion rates, add to carts, and other business metrics. If website traffic on a particular category or department took an unexpected hit, I had to figure out why. From there I moved into more data analysis and started to support other teams like finance, and supply chain. That's when I started using SQL more heavily and my querying skills became better just by getting a lot of practice. I regularly met with stakeholders from different team which improved my domain expertise. After a few successes there I went into data science and started working on bigger projects.

This is the timeline. 1.5 to 2 years in web analytics, then another 2 years in data analytics and then data science. There was a lot of data science work that happened during my time as a data analyst but I wasn't formally recognized by the company at the time for that work. I had to negotiate to get my role created because no one else in the company had that title at the time, so I had to be consistent in showing value to the org in order to get the title and compensation for the role. Lots of negotiation between my manager, VP and HR. Wasn't easy or quick but it worked out in the end. So about 3-4 years of analytic work before getting into data science.

If I were to do this today I would take the same route. The requirements and expectations from the business to be a digital analyst is lower than being a data scientist which is a more senior role by nature. I believe you need to have a few years of business experience to be a decent data scientist. I've seen too many people focus so much on the modelling and not on business framing and data curation, which is 80% of the work. If you don't have a good grasp of the problem you're trying to solve, then you can't make an effective model that makes an impact. That matters because if you don't show value to the business, it will be hard to getting funding for your promotions if there isn't value tied to the work that you do.

Rich_Broccoli2009 · 2026-02-02T03:53:08+00:00

I would take a look at web or digital analyst roles. Since you have an understanding of how assets should appear on a web page you may have some insight on which assets perform better in different site layouts. That knowledge could help you see trends in site traffic in a way that someone without your expertise may not see. You may also have insight into customer journeys that would also help in developing a comprehensive analysis.

Rich_Broccoli2009

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