Preparing for a career in data analysis
Data analysis is a great and growing career, increasingly able to command impressive salaries as more and more companies realize the many ways in which data analysis can make a significant difference to their business operations and their bottom lines. But what should you do if you feel the call of data analysis and mean to make it your career? What steps should you take on the way to your chosen career?
Machine Learning vs. Data Science
First – be sure. One of the main reasons data analysis is starting to attract the big money is because it’s not in any sense an easy ride. It involves intensive study of complex subjects, and you will need an aptitude in mathematics and particularly statistics, so if those things turn you off, it’s as wise – and as kind to everyone – to think again and choose a different path.
If you’re still convinced that that data analysis is the path for you, then:
Master the basics.
Statistics will be your friends, your companions, and your pathway to progress in data analysis. Learn to master them, interrogate them, and be comfortable wrangling them – start with statistics in Excel, in BI, in whatever takes your particular fancy, but start, and get comfortable in the world of statistical analysis and manipulation.
That involves a range of disciplines, like measuring spreads, determining probability distributions, testing hypotheses and so on. It’s also good to get at least a basic grounding in SQL, as a lot of time in your career will probably be spent querying databases, so the sooner you get that under your belt, the better – and very possibly, the faster your career will progress.
Pick a language.
While this may take some time, and some sampling of the range of languages out there, it’s wise to find either one core language, or a handful of languages with which you’re really comfortable. That’s because data analysis roles frequently ask for specialization in one particular language – like Python – or another. By narrowing your focus, you’ll stand a better chance of getting your foot on the career ladder in data analysis, and there should always be time as your career progresses to add on other languages as they either appeal to you, or prove necessary for the progression of your career path as it develops.
The education pathway.
As with most career paths – especially those that lead to higher salaries and more complex, rewarding work opportunities – you’re frequently likely to need to flash your certificates to get in the door. That usually means getting at least a bachelor’s degree, and possibly even a master’s degree in data analysis at a college.
Do you remember the part where we told you to be sure you wanted to go down this route? Getting a bachelor’s degree will usually take you 2-3 years working full-time, and a master’s degree at least another year on top, and these days, you can explain to pay at least five figures, and possibly up to six, for the training that will set you up for a life and a career in data analysis.
That means you need to carefully consider your finances before you embark on your data analysis degree.
You can do some training and certification online, which will take a fraction of the time and cost you significantly less initial outlay. But there are a couple of things to keep in mind if you decide to go that way. Firstly, the degree to which employers will take your certification seriously will likely depend on the level and the “weight” of your qualification. Study data analysis at Harvard for three years, and the qualification with which you leave is likely to open some serious, interesting doors almost as soon as you’re done. Get your qualification at CheapDataAnalysisCourses.com and you’re likely to spend longer in smaller, more run-of-the-mill jobs, building up your portfolio of project work to show to bigger and better employers.
Like many modern disciplines, data analysis is part of the knowledge economy. The different routes you can take into it are equally valid, but give you different journeys. The balancing act of getting your qualifications depends on matching what you can afford out of pocket, and how much time you’re prepared to spend slogging away to build your post-qualification reputation.
Build your portfolio.
Wherever you get it from, once you have your shiny new diploma, technically, you’re a data analyst, and can call yourself one. But a lot of companies will also want to see your skills in practical application. That means building your portfolio. Work on some projects, either solo or in teams, that you think are good stages for showing off how you wrangled data to good effect.
As a way of boosting your portfolio, there are freely available datasets that you can use to create your own projects in your spare time, so you have additional projects to show potential employers that illuminate both the creative ways in which you deal with data, any elegance you’re developing in your wrangling and storytelling skills, and the end-to-end skills you have at your fingertips in your chosen language or languages.
Be prepared for the long climb.
Like all jobs in the knowledge economy, you’re likely to start out in entry-level data analysis roles, likely in a team. Be realistic in your initial post-qualification job search, and focus on roles that both make use of your skills and qualifications, and that excite or interest you.
Above all, be prepared to work your way up through the ranks – whatever you think you’ve learned and achieved before you start actually doing your job in data analysis, the likelihood is that it’s hardly anything compared to the experience of working to the demands and deadlines of data analysis work in the real world. Learn, keep practicing and expanding your skillset, and you should have an enjoyable and profitable career in data analysis.
26 May 2023
26 May 2023