How to Stay Motivated Learning Data Science

Key Takeaways

so recently a subscriber on the youtube channel asked how can one maintain motivation to learn the science and so in order to answer that question we must first ask ourselves why do we learn data science in the first place so if the answer is to become a data scientist to become a data analyst or a data professional and so if we have a strong desire to become a data professional then we're going to have to be consistent in the learning journey be consistent in translating knowledge to practical skills by actually doing the projects and also building a growing and expanding portfolio of projects so becoming a data professional is not an easy task because there's so much skills and concepts to master and even for working professionals it is also impossible for them to know all of the topics or subtopics in the domain of data science and practically it would take a whole lifetime to master all of the topics in data science or data engineering and so be realistic and don't put too much pressure on yourself and as i already mentioned in a prior video on how to build your own data science learning curriculum you must first create a learning curriculum that is special to you that is customized to your own intention your own journey as i have already mentioned data science or data engineering is such a vast field and so do your homework by studying the potential roles that you want to land like for example if you want to land a position as a bioinformatics researcher and if you go to the job description you're going to see the keywords of the potential tools that the employers would like for you to have as an employee and so make a note of what tools are there and spend your time learning about those tools mastering it by actually using it to do projects and if you could share it in your portfolio on github or in a portfolio website that would give you the extra boost so let's hop on to the second reason for learning data science so if your reason is to learn data science so that you could apply it to gain insight from your data in your current job and if you're able to get only 10 or 20 of the theoretically maximum learning potential or practical application of that and if you're able to get only a small minute amount of insights from the data this is better than not doing data science at all right because if you start everyone's first project will not be perfect your second third fourth fifth projects will get better and better over time and so in order to be better you have to start somewhere anywhere it doesn't matter and let's say that you want to use no code or coding it doesn't matter if you want to start and if you're comfortable with no code then go for no code solutions and there are ample software such as weka or orange or nime which you could just click and click and get some machine learning models built if you know a little python or r then there are low code solution for you such as pi carats and if you're into coding python or r then you could make use of scikit-learn matplotlib or streamlit to prototype your data applications and also to build machine learning models and also to perform exploratory data analysis and also data visualization so whichever path that you choose either no code low code or coding there's always potential or path for you to embark upon so it doesn't matter where you start just start because if you start you will improve over time and so the third reason for you to learn data science let's say that you want to learn data science as a hobby you're perhaps interested in the concept of using machine learning artificial intelligence to gain insight from the data to make cool awesome visualization from the data then in this situation you're the most less stressed because the worst case scenario would be you would produce nothing but frankly speaking if you learn something you could at least produce or reproduce 10 of what you've seen so if you're following a tutorial you at least make what the tutorial has provided as an example and if you make a minor tweak to the example then you already have your own creation right and so getting started in learning data science is not that difficult and most importantly to maintain the consistency of learning data science will require good habits and that could be done by the awesome initiative that kenji has started which is the 66 days of data where he mentioned that if you could spend at least five minutes a day to learn data science over a period of 66 days where you constantly publish on twitter or linkedin as a form of social accountability so consistently doing that will give you good habits of learning so no matter how hard your day is you could spend five minutes just watching a youtube video from this channel would satisfy the commitment of constantly learning and constantly posting your progress of learning data science and so as the stoic says don't overthink it don't overthink the situation don't procrastinate about learning data science just do it just spend five minutes or ten minutes and if you enjoy what you're doing for five minutes or ten minutes then you want to continue that if not then you can take a break and do something else and so if you make data science learning journey a fun endeavor then you won't have any issue with maintaining motivation because if you're doing something fun it is effortless and so let me know in the comments down below what are your tips and tricks for maintaining your consistency and your motivation in learning data science and so if you're finding value in the video please support the channel by smashing the like button subscribing if you haven't already and also hitting on the notification button in order to be notified of the next video and as always the best way to learn data science is to do data science and please enjoy the journey