How I Self-Study Data Science
Have you ever felt overwhelmed by the size of Data Science, wondering where to start or how to make your learning stick?
I used to dabble aimlessly when learning data science topics, but I now have a more systematic approach that has transformed my understanding and intuition behind the subject.
In this post, I want to share my techniques, advice that has proven effective in my learning journey, and tips for staying consistent.
Deciding What To Learn
Naturally, the first step in learning something is to decide what you will learn about. Most people will already have this somewhat figured out, but simply saying, "I want to learn data science," is probably insufficient as it's quite vague. Data science encapsulates many areas, such as maths, statistics, and coding, to state the obvious ones, but these can be broken down even further.
While it may sound mundane and a bit boring, a structured roadmap or syllabus can be a game-changer in your learning journey. It's highly likely that someone in the field you're interested in has shared their knowledge in a video, blog post or any other form of content. In just 10 minutes, you can have a comprehensive list of all the areas you need to study, thanks to that single piece of content. Truly amazing!
You can find these roadmaps on YouTube, Medium or a simple search engine search.
If you google something like "data science roadmap" or "software engineering roadmap," you will get many results. Look at the top three or five and pick the one you like the look of the most.
Another method I used was while learning statistics; I visited my old university website and looked at everything they teach in the undergrad maths and physics BSc. This knowledge is open to anyone, giving me my list of statistics topics I should learn.
Getting Started with your learning journey is simpler than you think. You need a reasonable roadmap or syllabus, which should take maximum an hour to get. If it takes any more than that, you might be overcomplicating it.
If you are interested in learning statistics, machine learning, or Python, I have written and created videos with detailed roadmaps for each of these topics. Links are here.
Choosing Learning Material
After figuring out exactly what I need to learn, I need to find the material that will teach me these things that I am after.
I vary the type of resources I use. Sometimes, it will be a course; other times, a video tutorial or even plain old Wikipedia!
I often have one core resource but supplement it with other areas to enhance learning. For example, while studying deep learning, I mainly worked through the Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow textbook. Still, I used YouTube videos and online blogs to help build more context over the concepts I was learning.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Mixing learning resources helps me and gives me multiple explanations and views from different angles. Plus, presenting a topic visually and via text allows my brain to learn using different senses, which benefits me.
In terms of choosing the "right" resource, I don't worry too much about this. I pick ones that are popular and have good reviews. I browse over their content to ensure it contains what I am after, but I don't spend much time pondering whether this is the "best" resource as such a thing doesn't exist!
Trying to find the "best" resource is just one example beginner data scientists make; I have a full article detailing some of the pitfalls to avoid if you are at the start of your journey!
Learning Efficiently
Now, I am in the position to ready to begin my roadmap and study. However, I make sure I make my time as productive as possible and properly utilise it.
Scheduling time in your day is the best way I found to ensure you stick to learning. As a hybrid worker, I've turned my commute time into a learning opportunity. I dedicate myself to studying every morning from 8 to 9 a.m and 5:30 to 6:30 pm in the evenings. I think everyone can find pockets of time in their day for learning, even for just half an hour. It's all about making the most of what you have, but I appreciate it's more challenging for some people than others.
I wrote a previous newsletter article about how reading the book Stolen Focus changed my perception of "attention." You can check out my post for a full breakdown, but it made me realise how important focus is to in-depth understanding and how focus has slowly degraded over the past few decades.
I highly recommend you check the book out. It's a real eye opener and will improve your life in many ways if you take action from some of the suggestions.
