Hello everyone, ken here.
The most common challenge I see with people
new to data science is figuring out where
to start.
There are countless different courses, certifications,
degrees and bootcamps that you can take.
Most people are too overwhelmed by all the
options so they simply don’t pursue this
awesome career path.
In this video I give you my best tips on getting
started in the field.
I bet you’re expecting me to give you the
holy grail of learning.
The step by step process for learning this
field.
Unfortunately, what works well for you may
not work well for other people.
For example, I know that formal education
works really well for me, other people can
self motivate and learn on their own far better
than I can.
The first part of learning is understanding
yourself.
If you know the style of teaching that you
like, it is a lot easier to find a starting
point.
To be completely honest, I think all of the
courses out there are pretty good.
The course that you take is less important
than actually just getting started learning
the material.
If you get started and don’t like one of
the courses, no one is preventing you from
trying another one.
One of my subscribers found that he learned
far more by doing his own projects than from
following along with a course.
This is totally fine, but he never would have
known if he didn’t experiment with it.
I personally like the free Kaggle micro courses,
and I have made a video about the free resources
that I like best.
Again, all of these are good options and you
can start anywhere.
If you are looking for a more formalized option,
I have a good relationship with the 365 data
science team, and have a link in the description
that will give you a little discount.
I can’t stress this enough, just because
you take one course, doesn’t mean that you
can’t take another.
This especially true for the free courses.
You don’t just learn data science once,
you are constantly reinforcing your knowledge
and absorbing new resources.
Don’t think of a single course as “how
you are going to learn data science” it
is just one of the many contributors to your
data science body of knowledge.
If you think of it this way, starting is less
scary because you aren’t missing out on
anything, you are reserving time in the future
to be able to check out other resources.
The next thing that I would recommend when
starting out is to immerse yourself in other
people’s code.
I would go on Kaggle.com and just look through
the different notebooks there.
Kaggle is a place where people share the code
that they used to analyze different datasets.
You shouldn’t be discouraged if you don’t
understand anything.
This is an exercise in pattern matching.
What tools do most people use?
What visuals do you see a lot?
What words are coming up repeatedly?
You should take notes on what you see.
When you see these things coming up in a course
you are taking or in other places, everything
will start tying itself together.
The next phase of learning is starting your
own projects.
In my opinion, starting a project is as simple
as writing a single line of code.
Sometimes it is enough to just do that.
Just coding and going through examples and
getting the code to run yourself counts as
well.
After you get your code to run, you should
experiment, what breaks it?
Can you change your outputs?
As you get more comfortable, you can start
integrating the tools that you are using.
Finally, you should begin to understand documentation.
For all the packages you found in your notes,
go through the documentation for them.
This will be one of your most valuable skills
as a data scientist.
To learn even more about data science and
to eventually become one, you basically just
rinse and repeat these steps at a larger scale.
Data science is a process or a journey that
doesn’t necessarily have a destination.
I think that almost everyone is capable of
learning this field if they get out of their
own way.
Thank you for watching and good luck on your
data science journey.
