it’s possible to totally master every topic in data science?
With data science covering such a broad range of areas — statistics, programming, optimization, experimental design, data storytelling, generative AI, to call a couple of — I personally don’t think so.
Here’s a narrower query. Is it possible to master a single topic inside data science? Sure, you’ll be able to grow to be an authority in some areas, but are you able to ever reach some extent where there’s nothing left to learn? Again, I actually don’t think so.
Every data scientist has something to learn, even those with extensive experience. The aim of my writing is to offer some insights from my learning journey that I hope will enable you in yours.
That is the primary part in a two-part series. In this text I’ll cover:
- Why you need to repeatedly learn as a knowledge scientist
- Learn how to provide you with topics to check
Let’s jump in!
1. Why repeatedly learn as a knowledge scientist?
Continuous learners differentiate themselves
After I was younger, I studied Spanish in a bunch setting. Something interesting happened after the group became conversational. Many students stopped studying, they were content with their level of proficiency. Others continued to do every day study and practice.
At first, there wasn’t much difference between the 2 groups. But over time, those that continued learning pulled ahead. Their fluency, vocabulary, and confidence compounded, while the others plateaued.
Unfortunately, the identical thing can occur to data scientists. Some stop learning after they’ve developed sufficient skills to do their jobs well. Just like the Spanish cohort, early in a profession, continuous learners and data scientists will look similar. But as time passes, those that continue learning begin to stand out. Their knowledge compounds, their judgment improves, and their ability to resolve complex problems deepens.
Continuous learners and data scientists will look similar early of their careers. But as time passes, those that continue learning will begin to stand out.
Continuous learners shine because they’ll use their knowledge to provide you with smarter solutions to problems. They are going to have a more mature understanding of knowledge science tools and learn how to use them accurately of their work.
Learning brings achievement (for many)
That is a bit of bit fluffy, so I’ll keep it short. But I actually do enjoy learning. I get loads of achievement and satisfaction from taking a while to speculate in myself and master latest topics. If you happen to like the concept of continuous learning, you’ll likely get loads of achievement from it as well!
2. Learn how to provide you with things to check
We’ve established the worth of career-long learning within the previous section, let’s speak about learn how to provide you with things to check.
The very best thing about studying on your individual is that The worst thing about studying on your individual is that
You’re not at school anymore, which is great. No more deadlines, no more exams and, perhaps most significantly, no more tuition. But you furthermore may lose the curated list of topics to check with corresponding materials, texts and lectures. Creating that’s your job now! The pliability of developing your individual study plan is amazing. However the ambiguous, undirected space may be daunting.
Through the years, I’ve developed three approaches to provide you with study subjects that work rather well for me. My goal is that they may be a very good starter so that you can develop your individual approach. Ultimately, you’ll have to search out what works best for you.
Let’s get into the three approaches.
Topics from projects at work
If you happen to are working as a knowledge scientist, your projects offers you a wealthy supply of ‘deep dive’ study topics. This approach is pretty simple – study techniques/subjects which might be pertinent to your work. Give special focus to areas where your understanding is the weakest.
For instance, if you happen to are designing an experiment, study experimental design. If you happen to are solving an optimization problem, study optimization.
One great good thing about this approach is that it makes you higher at your job immediately. You should have a deeper understanding of the issues you’re facing, and also you’ll have the option apply that understanding instantly.
Following a “web” of topics
Data science is such a wealthy field of study, you’ll be able to all the time go deeper on any given subject and so many topics are interrelated.
When studying, you’ll find many ‘tangent’ topics which might be related to the subject at hand. I often pay attention to those topics and are available back to them later. I call this the ‘web of topics.’ That is an incredible technique since you slowly construct up an online of understanding around groups or related topics. This provides a deep knowledge that can differentiate you.
Here is an example of a small web of topics around logistic regression. I only included a couple of topics for the illustration – I’m sure you could possibly provide you with many more. Each one in every of the topics in the online have their very own web, making a mega-web of related study topics.
I could keep going, but you get the purpose. Any individual topic could have an enormous web of related topics. Keep an inventory of those somewhere and when you’re done with the present subject you’ll all the time have a backlog of pertinent topics to dive into!
Note: Your web of topics needs to begin somewhere. If you happen to are having a tough time kicking it off, I like to recommend reading ‘The Elements of Statistical Learning’ or ‘Introduction to Statistical Learning’ by Hastie, Tibshirani and Friedman. These are foundational reads that can get you into an incredible web of study topics.
Discovery channels
Work projects and topic webs are two excellent approaches to curating an inventory of study subjects. Nonetheless, these two approaches have a serious blind spot. If you happen to only use these techniques, you won’t be exposed to topics that don’t show up at work or in your natural sequence of study. There are likely really essential topics that might be left untouched.
I take advantage of ‘discovery channels’ to assist catch essential topics that don’t come up organically. A discovery channel is any source of content that expose me to topics which might be independent from my other studies. My essential source of discovery channels are Towards Data Science, podcasts and YouTube channels.
When selecting a discovery channel, it is vital to decide on a source that covers a broad range of topics. If I, for instance, followed a podcast that focused on experimental design – I probably wouldn’t source a wide selection of topics to check from it. It may be an incredible resource for DOE study, nevertheless it wouldn’t be a very good discovery channel.
I spend a comparatively small percentage of my overall study effort on discovery channels, but they play the very essential role in my studies.
Wrapping it up
I hope that this text leaves you feeling motivated to begin independently studying if you happen to aren’t already or has given you extra motivation to maintain going if you happen to already are studying. I also hope that I’ve given you a couple of fresh ideas on learn how to provide you with things to check.
In a couple of weeks I’ll be posting part 2 of this text that can cover learn how to (1) avoid burnout, (2) select learning strategies and (3) leverage solitude to cement and deepen your knowledge – stay tuned!
