How to Stay Productive & Motivated When Learning Data Science

Key Takeaways

hello everyone can hear as many of you know data science can take a significant amount of time and effort to learn you have to understand components of computer science of math and a bunch of different tools that are changing on a day to day basis this can seem like a daunting task and it can be really difficult to stay motivated in this video I share with you five insights that I have used to learn data science and to stay motivated in the work that I do all these insights come from various books that I've read the first insight comes from atomic habit by James clear the second and third insights come from the four disciplines of execution by Sean Covey and the last two insights come from two books by Cal Newport both deep work and so good they can't ignore you all these books are linked in the description below and if you're interested definitely check them out they've had a tremendous impact on my life and the way that I work as usual if you enjoy this video please hit that like button and if you want to see similar content of this please subscribe and turn on notifications to my channel so this first insight is related to creating habits you want to create good habits but you also want to start really small a habit only becomes a habit if you can repeatedly do it so if I want to learn data science if I want to make a component of data science a habit in my life I want to break it down into the most simple form possible so how I started is I said that I would learn and I would write one line of code each day writing a line of code takes maybe 30 seconds if I couldn't do that every day even if I was tired I felt terrible if I had a headache I probably wasn't meant to be a data scientist that bar is so low but after you do that for a long period of time to three weeks you can start ramping it up and actually coding a significant amount more a lot of the time also you'll sit down and you'll write a line of code and there's keep going this makes the barrier to entry of learning and starting extremely extremely low and that's one of the keys to actually gaining momentum is just setting one foot in front of the next one you can start with a couple of these habits for example again writing a line of code a day reading a data science article each day or watching a data science YouTube video each day these things again if you're repeating them and you're doing them every single day they will become legitimate habits and they will start the foundation of your your data science growth the second insight that comes from the four disciplines of execution is that we should set clear goals that are measurable but we should focus on the things that we can control what Sean Covey calls lead metrics so let's use an example of losing weight if I am trying to lose 15 pounds the clear measurable goal is is that I want to lose 15 pounds but I shouldn't necessarily focus on on a day-to-day basis on the weight I should be focusing on things that I can control that are highly correlated or causal of losing weight so I should focus on my ability to go to the gym how many times I go each week or if I go every day and I should also focus on what I eat what goes into my body if I do really well on those two things those lead metrics then I should lose weight over the time period that I'm focusing on this works the same with data science if I want to actually learn how to level up my coding I should code everyday if I want to learn about new techniques I should read data science articles every day and going forward those are the things that you should really focus on the process not the end result because the end result will come if you have a strong attention to the process goals tied to that second insight is this third insight that in order to actually succeed in a lot of these things you have to hold yourself accountable and you have to get other people to hold you accountable so the way that you hold yourself accountable is that you keep track of your progress let's go back to the initial habits that I talked about if you want to make sure you're doing them you should be tracking them every day again if we're using that losing weight metaphor you should be tracking if you're going to the gym if you are if you're eating the correct things in terms of data science you should be tracking am I actually coding every day am i working on projects on my finishing one every week or every couple weeks am i reading and keeping up-to-date with the new things on a day or week or month basis and keeping score is important but it's also really important to advertise that to show it to yourself to make it visible to yourself and so I keep a personal scorecard which I will show you at the end of the video that helps me stay in line with these things it's also important if if you're learning to have a buddy or someone else that can hold you accountable you guys can both commit to each other that you are doing things and it's one thing to kind of let yourself down but it's another thing to let one of your friends down or someone that's counting on you down so it's it's really valuable system to say hey can we be accountability partners on this learning the data science thing because you will be more likely to complete these tasks to to stay on top of this learning process if you have someone depending on you the fourth insight that I found useful is to schedule your entire day including your downtime and also to adjust this schedule if something changes so when you schedule your day ahead of time you eliminate the cognitive effort associated with the planning process so when you're going to study data science when you're going to go learn something new all you have to think about is actually executing on what you've already told yourself to do this makes it a lot easier to focus during that time period because the ancillary thoughts about what should I do next are non-existent for a computer science analogy you're looking at precompiled code versus code that has to compile and run when you pre compile the code it just executes it doesn't have to check any of the variable types or anything like that and it works a lot faster when pote code is not pre compiled in a language like Python it just takes a little bit longer to run in addition when you schedule downtime it makes it so that the time doesn't actually creep away from you everyone needs a break but we can set how much time we actually have for these breaks I know before I started doing this I would get a lot of work done and then I'd get on Instagram and then I get a lot of Instagram done and it would it would I lose control it would get away from me it's been 30 40 50 minutes before I knew it when I started actually scheduling that downtime I knew I had it to look forward to and I knew that I was able to actually like put a cap on it because I'd get another chance to look at those outlets the last part of insight 4 is actually pivoting if your schedule changes so sometimes all you know a call will be scheduled really late and my whole schedule will be ruined for the day and then I feel a little bit lost you know it's like that there was a bug in my pre compiled code and it it just didn't run correctly and I have to start from scratch so when you pivot it's important to actually take some time upfront reschedule what what's going to happen over the course of the day and then you can just go through and execute on it the last insect that I've found is also from counting ports books and it is to design your workplace to optimize for quality work one of the keys to learning is making everything as easy as possible for yourself and you can do that by environmental design you know for example if I wanted to stop eating junk food I can either try and use my willpower and you know have it around my place and just don't eat it or I can just not purchase junk food or have it around my house the the latter option where I don't have any junk food around is the easiest on my mind it's the easiest to actually execute on we're always fighting with ourselves and our willpower and that really your energy so if you want to actually do this for learning data science a couple of things that I would recommend is to close all of the other windows on your computer when you're learning and just only focus on the task at hand I also recommend putting your phone for example in another room and when you've scheduled your time you'll know that you're actually going to look at it again at a certain certain interval so you know those are a couple things you can also put your workplace your desk in a room where it's dedicated for work so you know your your bad or something like that doesn't look too tempting all the time in terms of Environmental Design you're really gonna have to figure out what works for you the things that I mentioned previously have really worked well for me and I know they've made it a lot more simple to actually get this work done the overarching theme here is to make everything across all these steps as easy as possible for you and to add in a little bit of gamification to make sure that you're staying motivated and that you're tracking your progress okay now that we've gotten through the entire list as promised I will show you the way that I keep track of my progress here is the spreadsheet that I use as you can see on the top growing my YouTube channel and the new business that I recently started are kind of my top priorities so they are my big important W G stands for wildly important goal metrics also Fitness and mental health are really important to me so as you can see I'm tracking those as well this has worked really well for me to meet a lot of my medium and longer-term goals but you can see that I've slacked off in a couple areas that I need to get back on track on you guys are welcome to copy this if there's a lot of demand I'd be happy to put you know put this on my github so other people can use it but again like this is how I structure my time yours might be a little bit different but I really recommend a system like this it keeps you accountable and it really keeps you efficient as you can imagine these insights can be used to improve the quality of your work outside of just data science I use this too improve the quality of my youtube-channel the medium articles are right in any of the other content that I create I really hope that these are helpful as helpful to you as they've been to me