My advice after more than a year of studying and practicing Data Science while working full time
There are few certainties in this world, but one is becoming very clear: the career path is anything but a straight line. Forget the “study, get a job, get married, and retire” approach: it won’t be like that for our generation (and, maybe, for the future ones). This may be due to the fast-growing digital professions: many people were stuck in their jobs while they have seen a world growing around them; and, maybe because of the pandemic, they even started thinking about making a career change.
Some days ago, I wrote an article on how to study Python for Data Science. Now, I want to give you my advice on how to study Data Science even if you are working or studying full time.
If you are thinking about Data Science, but you feel you don’t have time to study and practice because you work or study full time, then this is the right article for you: I’ve started studying and practicing Data Science while working full-time (and with two little daughters), and I want to share with you my recommendations on how you can do it too.
This is the hardest part, and I want to treat it in the beginning.
If you are working or studying full time you don’t have much time to spend on other stuff, but, saying the truth, you’ll be surprised to see how much time we waste, during the day, if we just use a time-tracker as an app on our smartphones.
Anyway, I believe you only have two chances to find some time for yourself:
- in the morning before you go to work/start studying
- in the evening, after work/university
Here there is one piece of advice I want to give you: you have to try, for some time (days/weeks) to understand when you are most productive. For example, I found myself most productive in the morning, so I get up at 5.30 a.m., have some breakfast, and have a 5-minutes meditation session and then I start studying and practicing.
Also, I even practice in the evening, after dinner, but since in this time window I’m typically very tired because of the whole working day, I do some tasks that “are not hard”; like, for example, beginning to write an article such as this one.
Instead, in the most productive time window, you have to perform “complicated” tasks, like studying (understanding concepts) or developing projects.
An agenda must become your best friend, in my opinion.
The most important thing you have to learn is to subdivide every task into smaller tasks and to plan them. For example, I know I have about 40–50 minutes every morning before preparing to go to work, and I have learned to schedule my tasks so that I know I’ll be busy for about 20–30 minutes; you’ll often have troubles with your code to solve, as well as some concepts may be more difficult than other to understand, so, in my opinion, it’s a good habit to schedule your time less than the full capacity you have.
The idea is to schedule your time, writing the stuff to do on an agenda. I usually take 5 minutes in the evening to schedule my time for the next day.
An important thing to understand from now is that you have to be very specific. You can’t write:
I have to study
This is for a couple of reasons:
- if you wake up early in the morning — as I do — your brain will remember that you wrote “I have to study”, and since our brain doesn’t love obligations and, moreover, since we — as humans — are programmed to consume less energy as we can, you will immediately turn in your bed, postponing your alarm clock.
- you haven’t set a specific goal, which is fundamental for tracking your progress.
Instead, you have to write something like that:
- understand lists in Python
- make an exercise on dictionaries
- install skicit-learn
Whit the time goes by, you will understand how much time you need to get things done and you will be even more specific in scheduling yourself. For example, if tomorrow you want to do an exercise on dictionaries, and you know you have 20 minutes but you believe you’ll need more than that, then schedule it like that:
- MORNING: begin the exercise on dictionaries (the evening before you decide on the exercise and you leave it ready on your PC)
- EVENING: conclude the exercise on dictionaries
This way, at the end of the day, you’ll have a sense of completion that will make you progress in your learning path and you will feel very fulfilled.
As your day is filled with work/study and other stuff (family, friends, some shopping, etc…), time management is your best friend. You can not waste your time anymore.
Please, stop reading for a second. I said “you can not waste your time anymore”; I haven’t said: do not take your rest (we’ll talk about it in the next section).
For example, if you’ll study in the morning when you get up you can not open your Facebook app “just for a second”, because you’ll get into a rabbit hole and…goodbye to your 40–50 study minutes!
Also, I wank to give you a couple of more pieces of advice:
- it is important to prepare and decide everything in advance. Schedule your tomorrow today evening, so that tomorrow you won’t lose time deciding what to do (even spending mental resources). Get everything prepared and decided (at least) the day before, and you’ll see things will go very easy: try to believe.
- use your breaks wisely. Everyone takes breaks when working or studying; here the difference is how you use them. If you are curious and passionate about Data Science and Machine Learning, you’ll come up with a lot of questions and you’ll search for answers. My advice here is to leave the search-on-the-Internet phase during your breaks (when possible), otherwise, if you search on the internet during the time you should study and practice (i.e, early in the morning) you’ll end up wasting your time; also, not accomplishing something in the day (as said in the previous section) may be the starting point to stop this marvelous journey. If a question arises in your mind, tickling your curiosity, write it on a note on your smartphone and search for answers on the internet during your work/study breaks.
We are not all the same; there are people who feel trapped having a day scheduled in advantage; also, sometimes if you miss some of your goals (for every reason: maybe you get the flu and you simply can not study!) you may end procrastinating for the next few days and, eventually, you will end your journey into Data Science.
So, one of the most important things I’ve learned is to be flexible with my agenda. Some nights my daughters simply do not make me sleep, and I can’t wake up a 5.30 a.m to study; so, I simply reschedule the tasks I had to do in the morning, to the evening, or to the day after.
As simple as that.
Flexibility is your ace in the hole when you learn to be flexible. You won’t get hungry with yourself; you won’t blame yourself for not getting up (or for watching a tv show for an evening!).
Also, the most important thing to learn is to listen to yourself. If you’re feeling tired because you spend days working, studying, playing with your children, and so on…your body will ask you to get some rest, and you have to listen to it. Otherwise, you’ll pay a hefty bill; believe me.
If you don’t take some rest when your body asks for it, it will eventually crash and will oblige you to a longer rest; for example, I’ve learned on my skin — when I was at the University — that if I do not take some rest when my body asks for it, I’ll spark a fever; this way, my body needs 2–3 days to rest: a much longer time than a couple of mornings when I stay in bed because I’m (really) tired!
I perfectly know the mental dialog:” if you do not study today, it will take longer to get to your result of a career change”; but, believe me: your brain is tricking you. This is a marathon, and you need to preserve your energies for the long run. When you are tired just relax and get some rest: you’ll thank me!
These are my recommendations on how to develop your career in Data Science while working or studying full-time.
Flexibility is the most important one to me because you have to learn how to manage your brain; so, this is a work “inside you”; meditation may help you (as it helps me) on this.
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