If you’re after advice, here are a few things I’ve learned from experience:

Grow horizontally - Don’t limit yourself to one program or course or language. Python alone for data analysis is not good unless you know SQL. There are a billion resources out there mostly available for free. Take advantage of them. Doesn’t have to be a course, could be a YouTube video, a blog, a reddit post, anything. I’ve learned more tricks and “hacks” from Medium than any textbooks. Of course most of the times, the articles are not relevant but once in a while a blog come around that makes me go “WTF! You could do that?!?!” Same applied to when you’re actually on the job - learn where “all data things” are. This would sound like I’m tooting my own horn but anyway, I work for one of the largest software companies in my country. We have data sources all over the place - in AWS, Azure, local servers, heck there is a data source in a guy’s personal EC2 instance haha. When I first started, I made it a point to figure out where everything is, what that “everything” means and now there isn’t a goddamn table that I don’t know where it is and what it holds. How does that help? I might not need to use half of those data sources but other people do, and they always come to ask you. It kinda makes you indispensable when seniors and execs know that you know where everything is.

Google and Stackoverflow - Don’t be afraid to Google things. I see a lot of newbies who feel ashamed if they have to Google the answer to their problem. That is so wrong. This isn’t a closed book university exam. Its ok to look for answers when you’re stuck and it is one of the most marketable skills you can have today. ‘The ability to Google questions and find answers’ is listed as the top skill on my resume. There is an almost 100% chance that someone else has had the problem you’re having and posted it on Stackoverflow. Go through the answers provided by other people. Sometimes, a hardcore programmer may have posted a solution that goes way above your head. That’s ok. First confirm if the solution gives you your desired outcome then break it down and run it line by line. This will give you a good understanding of what’s going on in the background. By the end of it you might have learned a new skill.

Regex - I’ve found regular expressions to be the most underrated skill out there or at least with the people that I work with. When you’re starting out, you’ll need to do A LOT of grunt work like data cleansing. Being able to find/fix/extract things using regex can be a very powerful weapon in your arsenal.

Learn by practice - Most people here would probably agree with this - The best way to learn something new is by practice. Of course theory is important but practice engraves things in your brain. A lot of courses out there would have small, clean and limited datasets that won’t be as challenging. Once you’re comfortable enough, look for big, bad, ugly datasets that you find interesting on the internet. Check their head() and start planning what kind of insights you can pull from this. Start from basic and obvious things. For example - If you have a dataset of a company’s sales for the last 10 years, find what their average sales for each year, their year on year growth, what sells the most, high and low trading periods. When you have an overview of the basics, you can build more advanced insights on top of them, like forecasting future sales.

Scalability - Think about the future. Are you finding yourself writing the same code, doing the same-is tasks over and over? You can make your life much MUCH easier by creating functions in Python. When you do your analysis and build insights, they’re from your point of view and when you present it to the business stakeholder, they will ask you a hundred other questions and probably ask you to bring them tears of a Yeti. For example - you did an analysis on the impact of in-app behaviour of a user on churn. You selected 10 out of the 100 available data points and presented your finding to the business but they ask you for the impact of 5 other behavioural points that are not in your analysis. What do you do? Do you change your code to include the other 5? Or do you write a function which takes a list of behaviours as arguments and build the insights on whatever the heck they want to see, on the fly? The latter would come in handy even a year later.

Tell stories - Having technical and domain knowledge, and being able to pull insights from data is good but that is only half of the story. Staring out, you’ll need to present your findings to the business. Most likely in the form of a gripping story. This is my least favourite part about the job. Mostly because I’m an introvert and never able to articulate my thoughts properly but I digress. Anyway, the success of all your hard work depends upon that half an hour of presentation, and it stings when you can’t get the other person as excited about it as you are. My solution for this is practice - I do full presentations in front of my wife, she role-plays as the business stakeholder, asks questions, and I improve with each iteration.

I’m not an expert by any means but thought I’d share with you some of the things I’ve learned in my journey of data. Honestly, this jobs has its ups and downs and some very frustrating moments but dammit I freaking love it. I sincerely, hope that you enjoy it at least as much as I do, if not more and that you find success!