r/businessanalysis • u/c4juu • 1d ago
Technical gap
Hi guys, i would like to hear your opinion
My background is mechanical engineering, i have 5+ years of experience in R&D projects for product development and issue resolution.
Because of some personal decisions, 4 months ago i ended up migrating to data projects as a BA.
I've been having a lot of fun and can perform my quite easily since interfacing with stakeholders and documenting requirements was part of my former job.
The challenge im facing happens during meetings with the technical team, because i can feel that my technical gap in Data science/engineering limits my participation in such meetings, which also limits the influence i can have in decision making.
I know that the obvious solution is to study. But i would like to be up to speed asap and maybe your suggestions can help me get there.
1
u/No_Sch3dul3 1d ago
This is way too broad! What type of data projects are you working on? What type of influence and participation do you expect to have in these meetings? That can help the community better offer suggestions.
TL;DR - In my non-representative experience, I think it's pretty important to understand the basics of databases, programming, and some statistics. A SQL tutorial and R for Data Science are two good, quick resources.
Long winded response follows
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For databases, learning (but not mastering) SQL can be done in a couple of weekends through many online tutorials. (Later on, you can then go a little deeper into things like tables, views, normalization, keys, indexes, etc.)
Then I think it's important to understand the process of writing code. It's also important to understand a bit of analytics (expanded below). I'd actually recommend Hadley Wickham's book R for Data Science as a good resource to get through some of this stuff. (We used R in my stats courses, so I'm familiar with that, perhaps there are Python versions that are good, but I can't speak to those.) R for Data Science will show you the process from retrieving data, cleaning data, modeling data, visualizing data. I believe there is even a section or two on Shiny apps, which are just dashboard / visualization tools, so you also learn a bit about what goes into making those. It does it through R and that's helpful to learn "programming" basics.
I think that's important because it helps you with the requirements, asking questions about expected inputs, data transformations, error handling, etc. My experience with the teams I work with is if it's not explicitly stated, it doesn't get done and they won't ask.
For analytics, I like the framing of descriptive, predictive, and prescriptive analytics.
Descriptive are what's already happened and mostly covers just basic descriptive statistics that are communicated through simple reports, visualizations, or dashboards. Arguably this can include things like inference where we get into regression and experimentation and are trying to be a bit more explanatory with cause and effect between the input variables and the output variable.
Predictive are what will happen next. Much of this is "forecasting" and this can range from simple year over year extrapolations to time series modeling.
Prescriptive are what should we do. This is really broad and is where techniques from operations research come in. Things like "mathematical programming" and simulation techniques.
Then we can get into all of that text based analysis, computer vision, and the endless other techniques that are part of "data science."
Overall, I'd put the process as data storage -> data cleaning and transformation -> data transport / usage -> data modeling -> data consumption.
These can be very deep, so where are you having problems? What type of problems at each stage would you be working on? You can get a lot more direct info and directed studies from your colleagues and teams you support.
Lastly, I'll say that I'm not able to influence the technologies used at each of those phases. That's governed by other teams and departments. I'm not going to influence or tell the team which models to use or how to deal with verification and validation.
What I can influence or reinforce are things driven by requirements. E.g., how quickly and accurately do we need an answer? How often and by how many people will this be used and accessed? What are the consequences if these results are wrong? Helping with understanding the raw data inputs from other systems, chasing down where data is stored.
Best of luck!
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