I made a transition to data analytics and landed my job in 60 days. (Not) obvious tricks that helped me.
I remember these moments two years ago when I was browsing through guides on how to become a data analyst. I was terrified. Truly. I felt like I knew absolutely nothing. That changing careers would take me a year, two, or five. That I’d spend countless hours memorizing probability distributions or crafting projects that wouldn’t help since their quality would be mediocre at best. That I’d need to pay for some expensive, six-month course (which I couldn’t afford). That I genuinely wouldn’t find work, especially lacking experience. But you know what? I followed my own strategies and principles. These got me a job just two months after I decided — ‘I’ll become an analyst’.
One of the most demotivating concepts in the idea of transitioning is the word ‘transitioning’. I really don’t like it.
As you might know, or not, my journey into IT was quite accidental. Honestly, I never thought I’d work in IT. Throughout high school, it was drilled into me that because I loved writing and reading, social studies were my calling. With that mindset, I chose to study German linguistics and later worked at a translation company as a project manager. However, some aspects bugged me, and I kept searching for my place in the world.
IT crossed my mind several times, but it seemed so technical. So distant. It felt as though you’d have to be another Einstein to consider such a career. It felt that I wouldn’t be even capable of capturing all of the technical aspects. The tide turned when I heard that my colleague, with whom I worked at an investment firm, started studying data science. That’s when I began to read about the data world. I fell in love. I fell in love with the variety of roles that it offers. I fell in love with how many cool things I could do there.
Every guide on ‘How to quickly find a job in analytics?’ felt overwhelming, repetitive, and lacking effective transition methods.
I said, ‘Alright, I’m diving in’. And from that moment on, it took me only two months to land my first IT job.
Back then, every guide on ‘How to quickly find a job in analytics?’ felt overwhelming, repetitive, and lacking effective transition methods. And yes, changing career paths requires effort and time (which I don’t deny). But with the whole bunch of professional and family obligations, it’s often tough to find time and motivation. It was tough for me too. Today, I’ll share what helped.
Research essential technical skills; don’t learn everything.
You’ve probably read a ton of articles online about all the tech stack and tools you’ll need, and I bet you felt overwhelmed. The truth is that roles in data vary widely. When I say widely, I really mean it. The required technical competencies depend largely on the company’s location and type. They also depend on the company’s data needs. Some of my friends, who own the ‘Data Scientist’ title have never built a machine learning model. Nor used any advanced algorithms. In some companies that is a prerequisite though. Weird, huh? Please check out this article and familiarise yourself with the data science hierarchy of needs. The truth is that most of the companies never actually reach the top two levels, because simply they haven’t satisfied the most primary needs yet (and might never do). They may recruit a data scientist (it is sexy to say ‘we do data science’, let’s admit that), but chances are high that the tasks will never go beyond basic exploratory analysis.
Some of my friends, who own the ‘Data Scientist’ title have never built a machine learning model. Nor used any advanced algorithms.
Similarly, when it comes to data analytics, I saw not once, not twice a data analyst job ad requiring just Excel. Do not learn everything. Focus on essentials. I suggest a quick exercise. Create a table and list the most frequently appearing tools and skills mentioned in job offers (When I was doing it, I used LinkedIn). Focus on jobs you’d want. On jobs that you’d consider applying. This will help you map out your quick yet crisp learning path. Remember the Pareto Principle — 80% of outcomes result from 20% of causes
The change doesn’t have to be drastic.
Analyze your current responsibilities. You might find a space to practice your technical skills at your current position. After working hours, it’s hard to find time for additional learning. It was hard for me. It is hard for my friends. It is probably hard for you too.
It’s a mistake to abandon your past achievements .
Maybe your current company needs some automation. Even if this doesn’t lead to a promotion (because they don’t have a Data Analyst position), this is the knowledge and experience you can showcase in your CV and during interviews. I remember creating scripts out of necessity to process a significant amount of data in the investment firm, allowing me to not only learn Python but also streamline my work. Win-win!
Practice, practice, practice.
Dedicate just 30% of your time to theory. The rest should be hands-on. Even if a six-month course looks good on a CV, it won’t add value if you can’t apply in practice what you’ve learned. I recommend working on specific assignments. You can find lists of interview questions online to test yourself. Working through these boosted my confidence, as I became familiar with the format of recruitment interviews. There were not a lot of things that could surprise me. Most of the interviews are actually pretty similar (I know from experience, as I interview analysts on all levels of seniority).
Many people don’t even make it to the technical interview stage because they undermine their chances with often very basic mistakes.
Aside from that, focusing on hands-on activities can be a good opportunity for you to create a project and put it on your GitHub. Go beyond copy-pasting content from the course. Try to build one solid end-to-end project yourself. Quality over quantity. Try to state a problem and showcase how you would approach it with data. Show your competency in all the major data analytics tasks (data ingestion, data cleaning, data modeling, data visualization). That will give you a strong advantage over other candidates.
Think about what your future company can gain. Use it.
One of the most demotivating concepts in the idea of transitioning is the word ‘transitioning’. I really don’t like it. It emphasizes the need for a drastic change or starting from scratch, implying a waste of years spent. Now, while switching careers might push your existing skills to the background, and possibly mean a lower salary, it’s a mistake to abandon your past achievements. Suppose you used to be a technical Spanish translator. Sure, maybe you lack experience in data analysis or technical skills. But aren’t your communication skills, Spanish proficiency, and technical vocabulary valuable? Some companies will find your Spanish skills appealing, even if it’s not listed as a job requirement. Perhaps they collaborate with clients based in Spain? Having you could be a huge asset for them. Before applying or interviewing, do some research. Try to figure out how the company can benefit from your unique skills and experiences. Addressing these will make you stand out from other candidates.
Don’t neglect the basics.
I know you probably want to shine and present how you mastered regression or decorators in Python. However, many people don’t even make it to the technical interview stage because they undermine their chances with often very basic mistakes. Grammatical errors or a poorly crafted CV are perhaps the biggest sins. As an analyst, one of your primary responsibilities will be acting as a translator between business and technical people (Btw, this is my very strong link between linguistics and analytics. Something that I didn’t hesitate to emphasize during the interview.). The way you present data and your ability to tell a story using data are key here. By carelessly preparing your CV, you show that you might not handle such tasks well in your job. After all, if you didn’t care enough to polish your CV despite having unlimited access to the internet and time, it doesn’t bode well for the future. The topic is vast, but I must mention what I think is a very serious mistake: when a candidate for a technical interview (with clear information that the tasks will involve coding) connects via phone. Don’t do that. Ensure comfortable conditions for both yourself and the person recruiting you, out of respect for everyone’s time. I will soon want to mention the recruitment strategy from the perspective of a hiring manager in the data analytics field, so stay tuned!
I hope this helps. It helped me. Remember, it’s about showing passion, and dedication, but keep the journey fun and continuously test the waters in the job market. Don’t let rejections pull you down. They are your tools for improvement. Happy to help further if you have any questions!
All the best!
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So much fun to talk to other data geeks!
