Is it possible to fully cover all topics in data science?
With data science covering such broad areas – analytics, programming, performance, test design, news breaking, generative ai, to name a few – I personally don't think so.
Here's a little question. Is it possible to be in full Well one topic within data science? Sure, you can be an expert in some areas, but will you ever reach a point where there is nothing left to learn? Again, I don't think so.
Every scientist has something to learn, even the most experienced. The purpose of my writing is to provide some insight into my learning journey that I hope will help you in yours.
This is part one of a two part series. In this article I will cover:
- Why you should further study as a data scientist
- How to come up with topics to read about
Let's jump!
1. Why did you further study as a data scientist?
Progressive students isolate themselves
When I was young, I learned Spanish in a group setting. Something interesting happened after the group turned and responded. Many students drop out, satisfied with their level of expertise. Others continue to study every day and practice.
At first, there was no significant difference between the two groups. But over time, those who continue to study are pulled forward. Their fluency, vocabulary, and confidence, while others get carried away.
Unfortunately, the same thing can happen to data scientists. Some drop out after acquiring enough skills to do their jobs well. Similar to the Spanish Cohort, early career, continuing students and – gave up Data scientists will look similar. But as time goes on, those who continue to study become prominent. Their knowledge base, their judgment improves, and their ability to solve complex problems deepens.
Continuing students and – gave up Data scientists will look like him in their careers. But as time goes by, those who keep learning will start to stand out.
Advanced students shine because they can use their knowledge to come up with good solutions to problems. They will have a mature understanding of data science tools and how to best use them in their work.
Learning brings fulfillment (for many)
This is a bit short, so I'll keep it short. But I really enjoy reading. I get a lot of fulfillment and satisfaction from taking the time to invest in myself and new topics. If you like the idea of ​​continuous learning, you will probably find a lot of fulfillment in it!
2. How to come up with things to learn about
We established the importance of long-term study in the previous lesson, so let's discuss how to come up with things to study.
The good thing about learning it yourself No one tells you what to study. The worst thing is to learn that yourself No one tells you what to study.
You're not in school anymore, which is good. No more placements, no more tests and, perhaps most importantly, no more tutoring. But you also lose a curated list of topics you should read about related things, texts and speeches. Creating that is your job now! The flexibility of developing your curriculum is amazing. But a prominent, neglected space can be difficult.
Over the years, I have developed three methods of coming up with study topics that work well for me. My goal is that they can be a good starter for you to develop your way. Ultimately, you will have to find what works for you.
Let's go into three ways.
Articles from work projects
If you work as a data scientist, your projects will give you a rich supply of 'in-depth' study topics. This approach is straight forward – study strategies / courses related to your work. Give special focus to areas where your understanding is weak.
For example, if you are designing a test, it is a learning test design. If you solve the problem of efficiency, efficiency of study.
One big advantage of this method is that it makes you get better at your job faster. You will have a deep understanding of the problems you are dealing with, and you will be able to apply that understanding immediately.
Following the “Web” of Articles
Data science is a rich field of study, you can stay deep in any given subject and many topics are related.
When studying, you will find many 'tangent' topics related to the topic at hand. I usually pay attention to those topics and come back to them later. I call this the 'web of articles.' This is a good way to gradually build a web of understanding groups or related topics. This provides in-depth information that will differentiate you.
Here's an example of a small web of articles surrounding rational restoration. I'm only including a few parable topics – I'm sure you can come up with many more. Each of the topics on the web has its own Web, making a MEGA-WEB of study-related topics.
I could go on and on, but you get the point. Any individual topic will have a large web of related topics. Keep a list of this site and when you're done with the current topic you'll always have a backlog of topics worth checking out!
Note: Your web articles need to start somewhere. If you're having a hard time kicking it, I recommend reading 'Mathematics Learning Factors' or 'Introduction to Mathematical Learning' by Hastie, Tibshirani and Friedman. This is the basic reading you will find on a good website for reading articles.
Discovery channels
Work projects and topic Webs are two excellent ways to suggest a list of topics to study. However, these two methods have a big blind spot. If you use only these methods, you will not be exposed to topics that are not visible at work or in your natural study sequence. There are really important topics that will be left untouched.
I use 'discovery' channels to help catch important topics that don't come up naturally. A discovery channel is any source of content that has exposed me to topics independent of my other studies. My main source of discovery channels is towards data science, podcasts and youtube channels.
When choosing a channel to find, it is important to choose a source that covers a wide range of topics. If I, for example, follow a podcast that focuses on test design – I might even have more topics to learn from it. It would be a great resource for doe learning, but it wouldn't be a great discovery channel.
I spend a small percentage of my overall learning effort on discovery channels, but they play a very important role in my studies.
Wrap it up
I hope this article leaves you feeling motivated to start independent reading if you haven't already and gives you more motivation to keep reading. I also hope to give you a few new ideas on how to come up with things to read.
In a few weeks I will be posting part 2 of this article which will cover (1) avoiding burnout, (2) Select study strategies and (3) increasing soulitude to cement and deepen your knowledge – stay tuned!
