Things move VERY quickly in the world of data science. In my career alone (the past ~4 years), I’ve had to learn several new, industry-leading tools like Prophet, dbt, and Dash Plotly.
Being a data scientist means that you have to continuously learn new skills and tools to broaden your arsenal of tools and also keep up with industry leading platforms.
In this article, I wanted to share with you all of the resources and hidden gems that I use to stay up-to-date with data science! With that said, let’s dive into it.
There are six main ways that I keep up with data science, and all of them help me keep up with data science in different ways:
- Data Science Platforms
- Learning Platforms
- Textbooks
- LinkedIn Influencers
- YouTube
- Conferences
There are a few data science-specific platforms that I find really valuable, and were actually a huge contribution to my development.
Analytics Vidhya and Kaggle are particularly useful when I want a step-by-step guide on how to perform a data related task, whether it be building a time-series model or using DBSCAN for a clustering problem.
What’s nice about both is that they both have their own communities where you can ask questions and learn from other people’s code.
How I use Data Science Platforms to Learn
There’s a few ways that I use Data Science Platforms to learn:
- When I want to see example code for a particular topic, I’ll look at these resources. For example, when I wanted to know how to implement K-fold cross validation, I used these resources to see how others implemented it.
- I also use these platforms to learn new tricks and methods that other programmers use that I find valuable.
- Lastly, these platforms have big communities that you can leverage to ask questions and learn from others!
My favourite resources
I’m not a huge fan of bootcamps, but there are a couple of websites that I find to be very strong, Vexpower and Coursera. These two are great if you want to step-by-step guides to learn technical topics. For example, Vexpower has a course that teaches you how to build a Marketing Mix Model step-by-step using Robyn.
I find these to be very helpful when I don’t need to learn an entire subject the way books do.
How I use Learning Platforms to Learn
If you think of a spectrum, where textbooks cover broad subjects and Stack Overflow covers very specific questions, learning platforms lie somewhere in the middle.
When I have a foundation in a particular subject but I want to extend my knowledge or dive a little deeper on a topic, that’s when I like to use learning platforms. For example, when I was focused on Marketing Analytics, I used Vexpower to learn how to build a Marketing Mix Model using Robyn.
My favourite resources
While not always the easiest or the quickest to digest, I find that reading books is the best way to learn a new subject for a few reasons.
- Authors put thought into the chronology of what information should be shared first. This avoids the situation where you’re learning a topic and have to recursively learn prerequisite topics/terms to understand the current topic.
- Books generally go in more depth than blogs or articles, which is useful when you’re trying to build a strong foundation for a particular subject that you’re trying to learn.
- From a technical perspective, I like textbooks because they go through how equations are derived and provide examples as to how to use different equations.
How I use Textbooks to Learn
When I’m trying to learn a relatively new subject, like experimental design, and I have little foundation on the subject, I tend to look for textbooks. Similar to school courses, I feel that textbooks are a good way to learn the basics of a topic relatively quickly.
Whether it be experimental design, deep learning, network analysis, or machine learning, I personally recommend starting with textbooks if you’re net new to the subject.
Here’s some textbooks that I love!
One of the most underrated sources to learn new things is LinkedIn. This is where I learn a bulk of new information and resources. There are a ton of Data Science Influencers that take the time to share very insightful coding tips and curate extremely valuable resources.
In the article below, you can see my personal top data science influencers.
How I Learn From LinkedIn Influencers
The beauty of this is that you can learn just from scrolling your LinkedIn home page after following several Data Science Influencers. You’ll see a lot of curated content and knowledge sharing on a weekly, or even daily, basis.
See here for my top data science influencers:
YouTube is great if you’re an audio or visual learner. I like YouTube because I can learn about small concepts very quickly, and I can also learn entire subjects.
What I personally love about YouTube is that there’s a lot of free lectures from some of the best universities that you can learn from (MIT, Stanford, Harvard, etc…), which is a great way to learn an entire subject fairly quickly.
Some links to get started:
What I love about conferences is that it gives you the opportunity to listen to some of the most pronounced data leaders in the world. And typically, conferences are where companies and leaders share novel ideas and new practices related to data science and AI.
Below are some of the most popular annual data science conferences:
Check out some of the biggest conferences:
Be sure to SUBSCRIBE here to never miss another article on data science guides, tricks and tips, life lessons, and more!
Not sure what to read next? I’ve picked another article for you:
and another one:
Terence Shin
Things move VERY quickly in the world of data science. In my career alone (the past ~4 years), I’ve had to learn several new, industry-leading tools like Prophet, dbt, and Dash Plotly.
Being a data scientist means that you have to continuously learn new skills and tools to broaden your arsenal of tools and also keep up with industry leading platforms.
In this article, I wanted to share with you all of the resources and hidden gems that I use to stay up-to-date with data science! With that said, let’s dive into it.
There are six main ways that I keep up with data science, and all of them help me keep up with data science in different ways:
- Data Science Platforms
- Learning Platforms
- Textbooks
- LinkedIn Influencers
- YouTube
- Conferences
There are a few data science-specific platforms that I find really valuable, and were actually a huge contribution to my development.
Analytics Vidhya and Kaggle are particularly useful when I want a step-by-step guide on how to perform a data related task, whether it be building a time-series model or using DBSCAN for a clustering problem.
What’s nice about both is that they both have their own communities where you can ask questions and learn from other people’s code.
How I use Data Science Platforms to Learn
There’s a few ways that I use Data Science Platforms to learn:
- When I want to see example code for a particular topic, I’ll look at these resources. For example, when I wanted to know how to implement K-fold cross validation, I used these resources to see how others implemented it.
- I also use these platforms to learn new tricks and methods that other programmers use that I find valuable.
- Lastly, these platforms have big communities that you can leverage to ask questions and learn from others!
My favourite resources
I’m not a huge fan of bootcamps, but there are a couple of websites that I find to be very strong, Vexpower and Coursera. These two are great if you want to step-by-step guides to learn technical topics. For example, Vexpower has a course that teaches you how to build a Marketing Mix Model step-by-step using Robyn.
I find these to be very helpful when I don’t need to learn an entire subject the way books do.
How I use Learning Platforms to Learn
If you think of a spectrum, where textbooks cover broad subjects and Stack Overflow covers very specific questions, learning platforms lie somewhere in the middle.
When I have a foundation in a particular subject but I want to extend my knowledge or dive a little deeper on a topic, that’s when I like to use learning platforms. For example, when I was focused on Marketing Analytics, I used Vexpower to learn how to build a Marketing Mix Model using Robyn.
My favourite resources
While not always the easiest or the quickest to digest, I find that reading books is the best way to learn a new subject for a few reasons.
- Authors put thought into the chronology of what information should be shared first. This avoids the situation where you’re learning a topic and have to recursively learn prerequisite topics/terms to understand the current topic.
- Books generally go in more depth than blogs or articles, which is useful when you’re trying to build a strong foundation for a particular subject that you’re trying to learn.
- From a technical perspective, I like textbooks because they go through how equations are derived and provide examples as to how to use different equations.
How I use Textbooks to Learn
When I’m trying to learn a relatively new subject, like experimental design, and I have little foundation on the subject, I tend to look for textbooks. Similar to school courses, I feel that textbooks are a good way to learn the basics of a topic relatively quickly.
Whether it be experimental design, deep learning, network analysis, or machine learning, I personally recommend starting with textbooks if you’re net new to the subject.
Here’s some textbooks that I love!
One of the most underrated sources to learn new things is LinkedIn. This is where I learn a bulk of new information and resources. There are a ton of Data Science Influencers that take the time to share very insightful coding tips and curate extremely valuable resources.
In the article below, you can see my personal top data science influencers.
How I Learn From LinkedIn Influencers
The beauty of this is that you can learn just from scrolling your LinkedIn home page after following several Data Science Influencers. You’ll see a lot of curated content and knowledge sharing on a weekly, or even daily, basis.
See here for my top data science influencers:
YouTube is great if you’re an audio or visual learner. I like YouTube because I can learn about small concepts very quickly, and I can also learn entire subjects.
What I personally love about YouTube is that there’s a lot of free lectures from some of the best universities that you can learn from (MIT, Stanford, Harvard, etc…), which is a great way to learn an entire subject fairly quickly.
Some links to get started:
What I love about conferences is that it gives you the opportunity to listen to some of the most pronounced data leaders in the world. And typically, conferences are where companies and leaders share novel ideas and new practices related to data science and AI.
Below are some of the most popular annual data science conferences:
Check out some of the biggest conferences:
Be sure to SUBSCRIBE here to never miss another article on data science guides, tricks and tips, life lessons, and more!
Not sure what to read next? I’ve picked another article for you:
and another one: