Building your skillset with a twist: A student’s guide to the future
And my 3 data-driven book recommendations
Thinking about what skills to learn to become a better professional can be a real head-scratcher, right? We’re constantly bombarded with new technologies and skills to master, and it’s easy to feel overwhelmed. In those moments, we often find ourselves asking, “What should I learn next?”
Well, here’s the thing — there’s no one-size-fits-all answer to that question. Even if you and I both work as data engineers, our skill sets and needs can be as different as night and day. And that’s perfectly normal. Some data engineers might need to dive deep into setting up a Hadoop environment, but for others, it’s just a nice-to-know skill. Same could happen with parallel programming, for example.
You see, it all boils down to our unique contexts. My work environment is like a distant galaxy compared to yours, so naturally, the skills I need will be different. And that’s okay. Context matters not only for ensuring our data analysis makes sense but also for charting our learning journey.
When I joined the team at Volvo, I realized I needed to boost my knowledge of engines. Let me tell you, my mechanical know-how could fit on a tiny post-it note, maybe even just a sticky tab! So, I embarked on a months-long journey to learn everything about engines, trucks, and how data plays a pivotal role in their development. Why? Because context matters. I can’t whip up a fantastic ETL pipeline for truck data and ensure the results are spot-on if I don’t understand where the data comes from and what it signifies. Values for environmental temperature are a world away from coolant temperature, and that distinction can be a game-changer when writing unit tests for the ETL pipeline.
Now, you might be thinking, “Does that mean technical skills don’t matter?” Not at all! Technical skills are our trusty companions on the path to bettering ourselves in our daily work and dreaming up new projects and solutions. In fact, the World Economic Forum even lists technical skills as one of the top 10 skills to develop for a promising professional for the future.
I’ve been pouring my heart and soul into boosting my technological literacy over the past few years. And you know what? It’s been quite the journey! If you’re looking to embark on a similar path, I’ve got a treasure trove of resources that have been my trusty companions along the way. Let’s dive into a couple of my all-time favorites:
1. “Data Science from Scratch” 📚
Ah, this one’s a gem! It was my very first dive into the data-centric world, and if you’re a newbie navigating the vast ocean of data and Python programming, you’re in for a treat. “Data Science from Scratch” is like the friendly guide who welcomes you into this exciting realm, making even the most complex concepts feel like a walk in the park.
2. “The Poenix Project” 📚
Ever had one of those moments when you make a seemingly harmless change in your code, and suddenly, it all goes up in flames? If you work with software, you’ve probably been there, done that — just like the rest of us. Don’t fret; you’re not alone in this struggle. In fact, I’ve been there too. Sometimes I still do.
Enter this fantastic book that not only reassures us that we’re not the sole crash-test dummies in the coding universe but also offers a profound reflection on our work. It’s a beacon of hope that guides us through the tumultuous sea of DevOps concepts, showing us the way to improve and thrive in our software endeavors.
3. “Mathematics for Machine Learning” 📚
Are you that pro who’s always on the hunt for scikit-learn code snippets just to spice up your work? Well, I’ve got news for you — this book might just be your next coffee-break companion. Grab your coffee, find a comfy spot, and let’s dive headfirst into the mathematical world that powers machine learning.
You see, sometimes we get so caught up in applying a myriad of machine learning methods that we forget the “why” behind the “what.” This book is your secret portal to the mathematical depths of machine learning. It’ll take you on a journey where equations and theories become your trusted allies.
And guess what? After this deep-dive, you might find yourself thinking twice before using that trusty F1 score as your one-size-fits-all method analyzer.
(Link is on the icon)
And other tools to success
As we come to the end of this article, let’s remember something important. We’ve talked a lot about technical skills, like coding and data analysis, which are like the gears of a machine. They help us do specific tasks. But there’s another side to success: soft skills. These are things like communication, teamwork, and understanding others. They’re like the friendly smiles that make the machine run smoothly. And I’m not the only one talking about it. The World Economic Forum agrees. 9 of 10 skills for the professional of the future are more related to skills we built when talking, working and improving ourselves with other people and leadering.
Leadership isn’t just about those in managerial roles or high-ranking VIPs within large corporations. It extends far beyond that. When you dive into the realm of engineering projects, you become a pivotal player in shaping the world. Your technology choices have the potential to impact people’s lives in profound ways.
As you take on these critical roles, the importance of ethics principles and responsibility cannot be overstated. They become the compass guiding your decisions, ensuring that your innovations benefit society while minimizing harm.
It means that the secret sauce is using both technical and soft skills together. When we do, we not only solve problems but also work well with others, inspire people, and lead effectively. So, as we get ready for the future, let’s not forget to develop both our technical and soft skills. They’re like a superpower combo that will help us succeed in the exciting world ahead.
With this knowledge in hand, let’s confidently embrace the future, knowing that our mix of technical know-how and people skills will guide us toward success.
And coffee, obviously.
