I'm a data analyst and would like to ask a question about what should I learn in order to become better at my profession. The "Tour" page says "Don't ask what to do". Is my question like this? I also wondered about asking this question on Data Science Stack Exchange but it seems quite off-topic to me and Workplace seems a better site to ask such a question.

All in all: will my question about my career development be closed/ deleted?

Edit. This is what I would like to write.

I'm a data analyst, I studied maths and statistics and I've been working as an analyst for a year and a half. The major tools I use are R language and SQL. At the beginning I though learning Excel would be beneficial to me, so I learnt some basics of data analysis in Excel, but you know what? I find this tool useful only for these "basics". Things that are more complicated usually can be done in R more efficiently. So now I treat Excel as a little helper in glancing at the data. Then I took up Python for analysis. What happened? I got to the point where I realised that there's no thing I should do in Python instead of R, because everything doable in Python can be done in R and I can do it much faster in R, because I'm more proficient. People say Python is recommendable for machine learning stuff, but in fact it is all possible in R too.

All in all, tools are just tools. If you know what to do, you should use the tools in which you can accomplish the task in the fastest and the best way. This is a sentence which I find true and it quite discourages me from learning Python and Excel. I hardly can find anything in my job which I can't do in R.

What do I do in my job? The most of times I must answer a question about the business process of my company. I must collect data and then take insight into it - what caused the problem or when the problem existed. Unfortunately, I'm inexperienced and I can't answer these questions on the fly, without any prompt. I must know what to look for.

What I'm interested in the most is this type of task. I would like to get better at it, be more proficient in answering to such questions + convey the data better, create better plots and write more on point conclusions. But how can I do it if not by continuing working and by accomplishing new tasks? I believe it's a thing gained with experience.

What is more, there are tools which I've never seen in action. But even if I learn them, I won't become a proficient user because the only way of becoming really good is spend hours with the tool every day. I know it because I've been using R at the university for 3 years but only after first months of working I've encountered problems which were common, but totally non-existent in a theoretic world of R. What's my second problem with other tools? That in my job I don't feel I need a new tool. It's been quite proven by my Excel and Python experience too.

Of course I work with other data analysts and I could ask them. The thing is that when it comes to analysing data, they use the same tools (sometimes with lower skills in tools themselves) and I see that the only thing that distinguish our abilities is experience and intuition. As I said before, I see no way to become better at it other than just working.

My problem in a nutshell: I want to explain data better but don't know what I should study. I don't feel that any new programming language will help me do it.

  • Hey there. Why not include in your post the draft of your question so we can better guide you? If it's not quite on-topic perhaps we can help you edit it to shape.
    – DarkCygnus Mod
    Commented Mar 30, 2020 at 18:49
  • you are welcome to use Meta for "sandboxing" the question. People won't answer it here but will help you write a better question Commented Mar 30, 2020 at 23:17
  • @DarkCygnus , aaaaa says reinstate Monica, I inserted a message I would like to post. Thank you.
    – musialmi
    Commented Mar 31, 2020 at 5:44

2 Answers 2


Is a question about what to learn in order to develop my career OK for Workplace Stack Exchange?

Generally speaking no. It's actually one of our main close reasons:

Questions asking for advice on a specific choice, such as what job to take or what skills to learn, are difficult to answer objectively and are rarely useful for anyone else. Instead of asking which decision to make, try asking how to make the decision, or for more specific details about one element of the decision. (More information)

As explained there, we don't answers questions that are this specific. What we can cover are questions that re more general in nature. It would depend on what specific problem you're facing but something like "How do I more effectively communicate X to Y?" or "Should I be able to answer detailed questions instantly?" would be practically answerable and generally useful.

I would assume that questions about how to effectively translate data science findings to a non-technical audience would be more appropriate for Data Science


I think Lilienthal answered this question in context of W@SE guidelines. I will comment on the writing of the question itself, as it will be useful for any StackExchange site.

The question right now is very verbose for any stack. Main issues:

  • A lot of rhetorical questions, like but you know what? and What do I do in my job?
  • Hiding important details: What I'm interested in the most is this type of task instead of I am interested in better answering questions about the business process of my company based on data or What do I do in my job? instead of In my job I analyze business data to optimize business processes
  • The questions doesn't appear until the last paragraph
  • Every paragraph starts with rambling. Try to start each paragraph with a topic sentence. Imagine readers are only capable of understanding first sentence of each paragraph.
  • Missing "summary". I suggest adding on top of the question something like

Summary: I know programming languages (R, Python, Excel), but what should I learn to be able to build models based on data? I can only think of learning another language, which seems wrong.

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