JT986M2 wrote:Sprouty wrote:Godzilla wrote:That's really fantastic. I think I'll set up a linked in page, might as well see what's out there.
Thank you! Feel free to message me if you want any ideas or pointers (same goes for anyone really). My short advice is to stay focussed and add the skills relevant to your role.
I decided to start working in data about five years ago. My profile is now 100% data related. There's no need to provide detail on previous roles outside of data. Same goes for my cv! Likewise, I know the skills required in my role are SQL, Python, Requirements Gathering etc. These are on my list of skills, but irrelevant skills are not. Make yourself findable. Good luck!
If you don't mind me asking, what role did you do prior to going into data analysis full time? Also, what apprenticeship did you complete on your pathway into it?
I only ask as it is something that interests me. My current role (and others for the last 8+ years) has been in software application support and, to be honest, I am growing tired of it. As part of those roles there is a big reliance on data analysis - specifically, SQL skills - so a full-time data analysis role does seem like a good fit. My undergraduate degree was also in Software Engineering, so I do have (rusty, but good) foundations when it comes to programming languages.
I'm really just looking for pointers in terms of how to make the transition!
For me it was quite a departure from my previous role, whilst I think your experience already has some relevance, so that's a great start. Because of the industry which I work in, there is always a need for data. A lot of this is serviced by dedicated platforms, whilst there can be gaps which are filled within teams. I had a chat with my manager and advised that I was looking to progress in to a data role down the line and that I would be really pleased to take on any data challenges within the team. This started to get me some exposure and I combined it with a lot of online learning to develop my skills. I personally used Linkedin Learning for this purpose as membership is provided by my employer, but you could look for other sources online, either paid or free. I later took a short term secondment in a data team, before returning to my previous role and putting what I had learned in to action there for a while, before finally applying for full time roles. My apprenticeship was a Data Analyst Level 4 and was offered to my team about a year after I took on the full time role. Most people do not complete it, but I liked the structure of it and it's something I now always have to my name.
My recommendations for you are:
1) Seek out opportunities to use data in your current role. I felt quite comfortable having the conversation, but I appreciate not all employers are the same. This should be a mutually beneficial thing. You can gain some experience whilst providing value to your employer / manager. If you can't get this exposure, consider finding some public data sets and setting yourself some tasks.
2) Build upon your relevant knowledge. You mentioned SQL and whilst it wont be needed in every DA role, it will be used in a large percentage and can be a key skill - it's the most common Database Management System used. As a Data Analyst, you don't tend to manage a database (that's a Data Engineers role), but rather to query it, so focus on being able to write queries. I'd recommend SQL Bolt as a nice interactive site. Online learning is great, but better to do a few courses and put those skills in to action than fly through twice as many courses by just watching videos.
3) Build on your knowledge gap. Don't overlook the importance of Excel for a start - it's not as sexy as the more tailored tools, but everyone has it and it can tend to do pretty much everything until you start to move to big data or data science. Beyond that, different roles require different system knowledge. Python is a big one, but some businesses will use R, Power BI, Tableau... the list goes on and on. If you want to look for a role in your current business, figure out what tools they tend to use and focus on getting a bit of exposure and knowledge of those, but otherwise there is no real wrong answer as demand is high. You could end up learning Python and then taking a role where you use Tableau, but that's fine - lots of the key concepts will translate from one system to another.
4) Apply for job roles and interview for roles. Rejections are fine - they give you knowledge of what gaps you need to focus on. I took interviews for roles I wasn't overly keen on just to get some interview practice.
Good luck! Let me know how you get on and if you need any more specific pointers, I'm more than happy to discuss.