When learning this field I’ll also talk about how I got unstuck from one stage to the next these stages are loosely mapped to a learning model developed by no birch in the 1970s and revamped by Robert Greene in his book mastery when learning we generally go through five stages first unconscious incompetence we don’t know what we don’t know second conscious incompetence we know what we don’t know and it feels overwhelming next comes conscious competence and unconscious competence and the final stage is mastery data science follows this progression and is no different Stage one is unconscious incompetence or what I consider the complete novice level at this stage we have little to no code or statistics experience, for the most part, this is how I felt when I was coming out of college I didn’t know any programming at all and I’d taken you to know one or two very basic level statistics.
Courses at this point learning data science seemed like a practical next step at a high level the field seemed simple you were just finding trends in data and making insights really how hard could this be I recommend if you’re at this stage watching many youtube videos and learning some introductory programming you will quickly learn how big the worlds of both programming and math are once you start feeling overwhelmed by how much information there is you know you’ll be on to stage two a little inside tip smashing the like button and subscribing can also help you break through to the next stage Stage two is where most people get stuck in this conscious incompetent stage you’ll start to learn how deep the data science well goes there’s so much to learn and it can be really unclear where to start the key to getting through this stage is to break data science down into small steps and to just get started somewhere at the most basic level for data science you need to know programming either in Python or R and statistics find a place online to get introduced.
These fields I have some links below in the description and I’ve also linked a video where I talk about my favorite free data science courses above what erased my overwhelm feeling at this stage was thinking about why I wanted to learn this field I had a specific project in mind where I wanted to build a model that would improve the outcomes of the daily fantasy sports that I was playing if I only focused on the necessary skills to build that model I could make data science seem much smaller and much more manageable to get to the next stage I recommend honing in on a specific problem that makes your learning criteria smaller it’s easier to understand what’s needed for a small project than it is for learning the whole field of data science doing a small project is far less intimidating than learning Python or learning statistic because those are so big at first you just need enough of these two fields to be dangerous if you knock out a few of these small projects you’ll be well on your way to learning data.
Science as a whole at this stage I also recommend reviewing other people’s code on Kaggle comm you likely won’t understand it at all so don’t panic that’s normal over time you’ll begin to make sense of it and just seeing code a lot is a good start to this process make a list of all the terms and packages and algorithms that you don’t really understand and each day learn a few of them in a few weeks you’ll be shocked at how much progress that you’ve made and actually learning this field I call stage 3 slightly dangerous or conscious competence at this point you’ll have done a few projects where you’ve learned how to implement specific algorithms you now have some code that you can reference which is a huge step trust me at this stage I collected all the code snippets that I use routinely and I put them into one just big master Frankenstein document instead of trying to remember how to do everything I could just reference.
This document and this made it possible for me to go through significantly more fat more projects at a faster clip it may feel like cheating but I think it makes a lot more sense to focus on implementation at this point rather than remembering syntax at this stage I basically did a project on all of the relevant algorithms that I that I found through my research I’d also started grad school at this time and I was required to implement most of these algorithms that that I was using from scratch I think that at some point all data scientists should experience you know writing all these algorithms from scratch because it helps you to understand how to program these if you want to modify them later and it also helps you understand why we use these in practice based on complexity or the type or amount of data going into these models I would argue that most data scientists are actually still at stage 3 this field is constantly growing.
So data scientists are just constantly learning so stage 4 is characterized by unconscious competence basically you know what to do when you’re confronted with a problem you generally don’t have to reference your code snippet library as much anymore and you can start focusing on optimizing your problem solving what takes you from stage 3 to step four is reps and just constant practice there’s a true art of data science that really begins at this stage the focus now shifts from actually from just finding a solution to finding the best or most optimal or most efficient solution you start to focus more on feature engineering production ization and model tuning that would make your work as valuable as possible to the end-user I would say that I personally fluctuate between this stage and stage 3 quite frequently and I hope to continue to progress from here on out stage 5 is what I call the contribution stage or as close to mastery as possible in this field at this level you’ve come close to reaching mastery and at least one area of the field I again I don’t believe.
Anyone can master the whole field but you’re able to push the boundaries by discovering new algorithms or completely novel approaches to problem-solving this stage is elusive and I think that very few people have actually reached this summit I’d argue that most people at this stage have PhDs and are more focused on research and academia than actual business implementation I hope that this video helps you to think about your own learning journey in a new light I also hope that this gives you a roadmap to level up your data science knowledge from my experience learning data science is a long journey but it’s definitely worth.