Self Study Traps to Avoid in 2023 (stop self-sabotaging!)

thank you fabulous for sponsoring today's video Hello friends welcome back to another video so in this video we're going to talk about the biggest self-studying traps that I have noticed people fall into and myself as well I think people don't really think about self-study until they get to a point generally in their adult life when they decide that they actually want to learn something again they want us to do it I don't know that way why would they change math because when you were a student all the way from Like Preschool to elementary school middle school high school college or maybe even grad school there's always been like a system in place for you and all you had to do was basically show up and do the work but as an adult when you start studying things by yourself all of this goes away you're completely by yourself so what ends up happening is that you start falling into these traps because I mean it's not your fault right like you've never actually learned how to study by yourself suddenly you're thinking about things like how do you design what the material is going to be like what's supposed to be on the syllabus how do you assess yourself how do you even know you're studying the right thing and that is what we're going to be addressing in this video today so what I'm going to talk about today is five of the biggest self-study straps that I've noticed both in myself and also in the lonely octopus program which in case you guys are not familiar with it's a program that currently just finished its beta and it's a self-learning platform for people who are self-learning data science I'm not going to go into too much detail about lonely octopus but if you're interested in a program you can check it out at this website over here also linked in description all right let's get started the number one trap that I have noticed people fall into is the breath versus depth chop for example you're like oh yes I would like to study data science so you do some research and you realize that data science is composed of the skills of coding math and business sets what may be the natural thing to do is that you're like okay I need to learn how to code okay it looks like the recommendation for coding language is to start with python okay because I'm a final course that's to do with python and you know you're like learning python learning Python and you're like hmm you run into a concept called object-oriented programming and you're like hmm this object-oriented programming it kind of makes sense to me but I feel like I don't really really really get it so in order for me to really really really get it I think I need to take a full-on introduction of computer science course by then you run into something else like say a memory and you're like hmm I kind of get how memory works but I don't exactly understand how it works well I better learn how that works too so then you put aside that course and you go to another course so I think you guys get the point this is called a depth first approach to learning it's about starting with one thing and then drilling it down really really deeply trying to understand exactly everything about this topic and the issue with doing something like this is not necessarily that it's bad it's the fact that you end up getting into a rabbit hole like super deep to the point where you're like 10 layers deep and you forgot what your you know original goal was which was to learn data science and on top of this I feel like it's also much easier to give up because you feel like very overwhelmed by all these different things that you don't know in contrast to this is the breath first approach this is what breadth first approach to learning data science would look like assuming you don't know anything we would start off with learning basic Python and you might hit something like object-oriented programming which you kind of understand but you don't exactly understand but you're okay with this then you learn some math probably some statistics and you learn some introductory things like hypothesis testing distribution Central limit theorem things like that you might end up coming across the topic that you don't understand that belongs in a domain of calculus for example if you're trying to prove to Central limit theorem then you're like you know what that's okay like you know I don't understand exactly how that works but I understand a high level of it we learned just enough of these things in order to start a data science project on it like a real data science project probably on exploratory data analysis and the business component something like analyzing stock prices and how stocks correlate with each other and the market you have enough skills out to complete that project and after you do that then you're like okay like maybe I want to dive deeper into statistics right like I want to look more into samplings and distributions and learn more about that and then after you do that maybe you do a project that has a little bit more to do with the math portions of it and that's a little bit more advanced in this way it's a lot less overwhelming a lot less anxiety causing and you get to use the skills that you learn without trying to like exactly understand every single thing let me know in the comments if you fall into this trap before from my experience this is like the biggest trap the second trap of self learning is being obsessed with finding the best resources for things sometimes people don't even start are working on the thing that they're trying to self-study because they're so obsessed with just making a study plan finding the best resources out there in order to learn the thing for example if say if you're going to learn data science you might do some research I can just Google like best resources to learn data science and you'll probably come across a variety of them for example with 365 data science data Camp data Quest udemy some YouTube courses some books it's a overwhelming selection of resources so you might be spending a lot of time just trying to figure out like which one is the best course for this which one suits me the best how can I should I like get this course and combine it with that course but then like supplement it with this book this ends up taking so much time and you fall into the Paradox of choice where it's like so many things that you don't know which one is the best which causes a lot of anxiety and then maybe you just don't even bother doing it in the first place not to mention that it's a huge waste of money now a word from our sponsor fabulous a really big part of self-studying is developed just the discipline and the habit of studying like when you were in school there was a lot of systems that were already in place that gets you to keep studying like for example you kind of like have to go to school and there's going to be tests and exams and all these different things right like this holds you accountable but when you're self studying that has to come from you and the best way of doing that is by designing habits into your life in order to keep pursuing yourself studying now fabulous is the number one self-care app to help you build better habits and to achieve your goals like self-study fabulous is like a digital coach that uses Behavioral Sciences to develop great habits that will enable you to live the life you want through positive reinforcement fabulous offers two approaches habit tracking where you can pick among more than 100 recommended habits or create your own there's also dedicated programs which personally I've been more focused on the behavior change programs are designed specifically to help you achieve your well-being goals what I've been working on more recently I guess is more like the soft skill side of things like I'm trying to make myself into a more resilient person because um now that I've quit my job my life there's a lot more fluctuations within my life and I think by building up resilience it's going to be something that's going to help a lot in the long run so yeah I'm trying to establish habits that will make me into a more resilient person it's all about small steps that lead to big and long-lasting changes I really enjoyed using fabulous because of the science back Techniques plus the art and storytelling is just super cute with fabulous premium you can build and improve an unlimited number of habits into your routines and take part in all programs and exercises start building your ideal daily routine the first 100 people who click on this link will get 25 off of family subscription also linked in description now back to the video so the first question you should ask yourself is what is the best way for me to learn like very generally for me for example I learned the best through video courses for someone else it may be through like text courses for other people it might be reading a book and then within that learning style you should just choose one of the top ranked resources out there and I highly recommend just choosing one resource like as a beginner you don't know enough to exactly know how these things will mesh together with each other and also it's not really necessary like usually things like resources are well ranked for a reason so you should just trust the fact that they are well ranked by other people and realize that it doesn't need to be perfect you're just getting into this right like if you hit a point in that resource where you don't feel like it's telling you that much anymore you can just find another resource if you have fallen into this trap before good job it means you don't have commitment issues but in this situation it's really okay you know to do something you feel like it doesn't work just try something else don't don't stress out so much about it third self-study trap that is holding you back having unrealistic expectations say you're working a full-time job then you're like I'm also going to study 30 hours a week for data science and I am going to do this because I'm going to stop doing self-care stuff I'm going to not cook anymore I'm going to go home from work and then work until 1am every single day yeah you can probably like manage this for a week where like two or three weeks if you're you know a real go-getter but life is gonna happen you can't just cut out all your self-care stuff cut out all your hobbies cut out everything and then just like study until 1am every day that just seems like a recipe for Burnout and this is actually a really common thing to the point that there's a term for it in Psychology called planning fallacy and that's when people underestimate the amount of time for you to complete a task so what's the solution for this so I was reading this book called finished which is a book about why it is that people can't finish the things that they start and what you can do about it I believe that by the time this video comes out I should have done a review of this book as part of my video called I think it's going to be called like self-help books smash or past I don't know I'll link over here I'll link him here but yes anyways in that book one of the techniques that they recommend is to you know if you think that you're going to have a certain amount of time for example you're gonna be like I'm gonna do this data science thing and I'm gonna commit like 50 hours a week right the book recommends that you cut that in half and make that into your goal I also do this for the lonely octopus study plans so in the beginning we have a survey asking people how long it is that they think they can commit per week and when I'm designing these study plans and putting in courses and projects I actually also cut the time commitment to a third of what is being reported so some people ended up overshooting but I actually feel like most people you know that was the correct amount of time like If you overachieve that's amazing all you have to do is just like increase your time for for the next week right but if you underachieve then you start falling behind so not only is this bad because that means you have to like kind of readjusting things it also makes you feel worse when you underachieve and that leads to the next trap that people fall into when they self-study which is over compensating this goes hand to hand with unrealistic expectations let me explain say you dedicate 20 hours per week but life happens there was a work emergency that happened this week and you were called in overtime and you had to like work 10 hours a day for the next three days and over the weekends you just didn't have time to commit to 20 hours that week and you end up only doing 10 hours per week now because you fell behind you feel bad about yourself and the week afterwards you're like I gotta make up for that 10 hours so next week I'm gonna do 30 hours per week and this is really really bad like you can't just expect that life is not going to happen the next week and that you're magically gonna have like way more free time so yeah you're like driven by guilt when you're unable to meet your goals and then you start over compensating which then you burn yourself out and oftentimes you would just give up and feel terrible about yourself you can probably see why that goes hand in hand with unrealistic expectations right because if you have already unrealistic expectations and do too many hours just combine that together with over compensating it's just like a recipe for disaster all right next trap that people fall into when they're self-studying focusing on the wrong metric Indie lonely octopus program as people are self-studying data science something that came up quite a few times is statistics statistics is hard you feel like you're like turning in so much effort and so much energy but it's super frustrating because it feels like you're not making any progress and oftentimes that's when the sneaky feelings of self-doubt start creeping in thoughts start popping up like you know maybe I'm just too stupid to do this data science thing like do the statistics thing if you're having these thoughts that's a good indication that you're focusing on the raw metric I want to tell you guys about input-based metrics versus output based metrics input-based metric is something that you have full control over well I'll put based metrics is the result for example if you're trying to write an essay the input-based metric is the amount of time that you're spending in writing that essay while the output-based metric is more like completion of that completion of a paragraph something like that our natural inclination is usually to measure ourselves of this output-based metric but this is a trap because you don't have full control over your output of something while you do have full control over the input of something and in most cases the input based metric of time is really really good because you have full control over the amount of time that you spend on something so going back to the case of Statistics instead of focusing on your ability to grasp the concepts of Statistics you should be instead focusing on just putting in the effort putting into work putting in the time into statistics even if you feel like you're not making progress like oftentimes when I'm trying to learn something like that I feel like I just have no idea what's happening but then I start working on a like actual problem or an actual project and all like the different parts of it just click for me and I realize I actually do have a decent grasp so just trust the process and trust your brain cells alright so that's all I have for you guys today I hope this video was helpful for you let me know in the comments below if you fall into any of these props before and if there's any other traps that you feel like should be long on this list sure I'll see you guys in the next video live stream [Music]

Channel: Tina Huang

Views: 490,999

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Id: uzCTHdm0T_Q

Length: 12min 45sec (765 seconds)

Published: Sat Dec 31 2022