Advice on how to be more consistent in your educational journey for data science.
Over the last few weeks, I've taken a break from writing to focus on applying to internships. But as I was driving to class today, a question began to bother me.
With all the resources available to us on the internet, why is it so hard to hold on to our motivation to learn data science?
Over the last year, I've had to work very hard to maintain my motivation to learn data science. I made my own curriculum entirely from (free) web resources, and each day, no matter my level of motivation, I set aside at least four hours towards expanding my skillset.
However, this last year has also seen really hard days. Days where I struggled with my motivation and almost gave up despite all I have accomplished.
In my experience, no matter what path you take towards data science, you will face challenges that threaten to extinguish your motivation. You will take on tasks that may make you feel small and incapable, or demons that whisper in your ear, "This is too hard. You should give up".
But you don't have to go alone. In this post, I will describe some of the pitfalls that can threaten your motivation to learn data science. My hope is that by being aware of these obstacles, your path towards a career in data science will be easier than mine was. So let's begin.
Make sure data science is a fit before you begin.
Data science is a sexy job. The salaries are high, the work is interesting, and there's significant prestige that comes with the title. As a result, MANY people want to be data scientists.
Unfortunately, even if you are passionate about data science, and can do the work really well, you can't force yourself to love the process. And this leads to some unsettling advice for aspiring data scientists.
You can be fascinated by data science, but if you hate the day-to-day tasks and feelings that come with it, it will be very difficult to maintain your motivation.
For most people starting out, it may take months before you actually get to the "doing" phase of your education. (Where you actually build full-scale data science projects.) And at that point, realizing that you hate the process can be a heavy personal blow. To save you the trouble, I have compiled a list of some common frustrations that data scientists face.
These are some of the tasks and feelings you will need to be comfortable with if you choose to pursue a career in data science:
The feeling of never being done with learning.
The feeling of being out of your depth/overwhelmed.
The feeling of failing many, many times to get one success.
The feeling of having something you spent weeks on, fail or be ignored.
Teaching yourself a skill that you have zero knowledge of.
Spending dozens of hours to answer a single, seemingly simple, question.
Comparing yourself to (seemingly) more successful people.
Communicating with people who don't understand (or care about) data science.
Doing work that is 95% preparation, and 5% execution.
Doing a lot of work that is not "sexy". (Database work, data munging,‚?¶)
Coding‚?¶. a lot of coding.
My advice: Do your research before you jump in.
Data science is an awesome profession, but there are definitely some serious frustrations that come with it.
For the aspiring data scientist, I encourage you to learn more about the tools and tasks a normal data scientist does (this article, this video) before you commit to a career in data science.
A little bit of research, in the beginning, will drastically increase your chances of following through on your educational journey.
Learn how to Deal with Anxiety.
When you start researching how to become a data scientist, you will discover an unfortunate fact about the profession. Namely, that becoming a data scientist requires knowledge of a broad and deep set of tools, technologies, and skills. All of which makes the prospect of becoming a data scientist VERY intimidating.
You might start asking questions like Do I have to go back to school and get a Ph.D.? How do I get these skills without work experience? Am I even capable of learning all of this?
When you set out on your journey towards data science, you will feel a lot of anxiety and stress. This is a totally normal reaction and everyone who has been in your shoes has felt the same way.
Just know that this time at the beginning of your educational journey is absolutely crucial to your long-term success. The actions that you take in these first few weeks will determine what habits you set for yourself, and as a result, will determine how you will deal with the negative effects of stress and anxiety throughout your journey.
If you can find healthy ways to deal with stress and anxiety from the very start, your confidence and motivation will become unshakeable as time goes on.
However, there is a danger here that needs to be addressed. It is absolutely critical that during this time you avoid unhealthy ways of coping with negative stressors.
Unfortunately what is healthy and unhealthy can depend a lot on who you are as a person. But in my own journey, I have recognized a few unhealthy coping mechanisms that have damaged my motivation in the past.
Here are a few of the unhealthy ways to deal with stress that you should avoid.
Avoiding being overwhelmed: Buying an all-inclusive course or textbook.
This one has really tripped me up in the past. Whenever I start to feel overwhelmed by the sheer volume of things I need to learn, I feel a strong urge to give up on teaching myself, and buy into someone else's lesson-plan.
If I do end up buying the all-inclusive course or textbook, learning starts to feel like a chore that someone else has tasked me with. Worse still, because I haven't had to put effort into planning what I need to learn, I become disconnected from why I am learning a particular skill or concept in the first place. The result? Whenever I buy an online course, my motivation to learn quickly tanks.
Why you should avoid this: Even if you can learn well from someone else's curriculum, I would still advise against it. Why? Because the most important skill you can learn on your journey towards data science is being able to teach yourself.
Teaching yourself is the process of identifying holes in your skillset, researching new technologies to close that gap, and making an actionable plan to acquire that skill. If you have relied solely on hand-holdy courses during your journey, you will have much less experience with this process.
That can be really bad as time goes on. When you actually get the job, you may be tasked with a very unique, domain-specific, problem that you have little experience with. And if there isn't a MOOC or textbook that can teach you the skills you need, you will have a VERY hard time.
Avoiding Stress: Putting it off, or designating a single day for study.
The worst mistake you can possibly make during the beginning of your journey is to put off learning. It doesn't matter if you feel like you are too busy, if you are going to be successful on your journey you will need to set aside time each day to learn.
If you don't practice and learn daily your motivation will quickly fade and you will inevitably lose interest in a potentially fulfilling career.
Why you should avoid this: Pursuing data science is a marathon, not a sprint. The skill set required for the job is so diverse, it can only really be acquired through consistent effort over a long period of time. If you try to learn in short bursts, you will eventually exhaust yourself and extinguish your motivation to go on.
Worse still, if you put off your learning until you feel like you have time, you will probably never even embark on your journey. And, if you do, you will have cemented in your mind the idea that learning is something that you do when time permits. In data science, that mindset is a quick end to a career.
My advice: Set healthy habits, early on.
Whether you are working full-time and want to make a career pivot, or are studying at a university and want to follow a career path that excites you; Either way you need to find healthy habits that allow you to overcome stress.
A few healthy habits you can set from the beginning:
Set time aside every day to learn something new.
Try to get connected in the data science community. You'd be surprised how many people will relate to your feelings of anxiety.
If you feel exhausted and anxious from learning, take some time off and build a project with something you have learned recently. This is a great way to de-stress and reconnect with why you are learning in the first place!
Learn how to deal with being overwhelmed.
When you actually start learning data science skills, you will notice that there are a LOT of things to focus on. At this very moment, I could probably list 8‚??12 skills that I want to acquire in the next 6 months.
Unfortunately, this isn't just me. Having a long list of skills you want to learn or practice will be a daily reality on your journey towards data science. Just look at Swami Chandrasekaran's data science roadmap and you will see what I am talking about.
When I first saw this roadmap, my gut response was to feel overwhelmed by the sheer enormity of that list. How could one person possibly expect to learn all that on their own? What I had learned so far was only a small fraction of the list, had I really made any progress at all?
Unfortunately, this feeling of being overwhelmed will stick with you throughout your journey towards data science.
One day you might feel that you have achieved mastery over a concept/skill, just to realize that five new things need to be added to your list of things to learn. Over time this problem only gets worse as the technologies you will need to learn become newer and more intricate.
If you don't come up with a plan to deal with the feeling of being overwhelmed, two things will happen to you.
First, you will start to feel very anxious by the breadth of skills that you need to master. If this feeling is allowed to fester, you will find it harder and harder to stay focused on one thing. As a result, you might spend months flip-flopping between skills, trying desperately to learn them all at once, then finally feeling frustrated when you haven't achieved mastery over any of them.
Second, you will start to feel weighted down by the number of skills you have yet to learn. If this continues, your motivation to learn will be in jeopardy. Each day that passes will make you more frustrated, as you will compare the progress you have made to the enormity of what you have yet to accomplish. At some point, you will start to believe that any progress you have made thus far is worthless, and the task of becoming a data scientist is impossible.
If either of these feelings is allowed to gain weight in your mind, they will slowly crush your motivation to learn data science. But don't worry, these burdens can be treated early-on with a thoughtful plan and strategic focus.
My advice: Stay organized, stay short-sighted.
Data science is an inherently broad field, and the only way to master such a diverse set of skills is to chip away at them, learning one skill at a time. Regardless of how you choose to pursue data science, you will need to make some kind of linear, organized learning plan. This will help you tackle the breadth of the data science skillset by focusing your efforts on one skill at a time.
If you want to build your own curriculum, you might choose to build an end-to-end roadmap like Swami. However, you may start to feel overwhelmed by the depth of the list.
This is where being little short-sighted can really help.
In my experience, the best way to deal with feelings of being overwhelmed is to set short-term goals that focus your attention on the skills that are most important. My rule is that, at any moment in time, I should have a list of the three most important skills that I should master within the next month.
Throughout the month my attention is focused solely on learning these three skills, and any progress I make feels very substantial and worthwhile. When I feel like I am making progress, it becomes much easier to motivate myself to learn each day. At the end of the month, I reevaluate my skill set and try to determine three more valuable skills that I should focus on.
If you want to do this yourself, just do the following. Ask yourself, "Given what I am learning now, what are the next three most crucial skill I need under my belt?" Write them down and hold yourself to learning those next.
If you are wondering where to start, or in what order you should learn data science skills, check out my previous post linked below. In this story, I give guidelines and advice so you can execute your own data science curriculum.
When it comes down to it, our motivation to learn data science is the most precious resource that we have. If we are going to consistently master our craft, we need to protect our motivation from the challenges that threaten to extinguish it.
Specifically, we need to be vigilant for feelings of anxiousness or being overwhelmed. These burdens can be addressed early-on with healthy learning habits as well as organized and deliberate focus.
If you can protect and cultivate your motivation to learn data science, then nothing can stand in your way. You will develop an unshakeable resolve that will push you to become the best data scientist that you can possibly be.