I've made a lot of mistakes especially early on in my career as a data scientist. Today
I'll talk about the five biggest data science beginner mistakes that you can avoid to enjoy
a successful career as a data scientist. And make sure to watch till the very end for the
most common mistake that you can easily avoid. I'm Samanvay and I'm a data scientist in Sydney,
Australia. The first mistake has to be trying to find the best data science course. There's
no one-size-fits-all. Your data science journey will continue long after your course
and it's going to be a long one. Data science tends to be a very broad field. So there's lots of
different technical and soft skills to learn and it's very hard to find a course that encompasses
all of them. So instead just get started on your journey. Don't deliberate too much on what course
to study. Pick a resource like Stanford's machine learning course on Coursera or a
basic python programming course. Because once you get started you'll figure out what to learn next.
Learning data science is a little bit like putting together a jigsaw puzzle. Every single piece is a
new skill that you need to know and in my honest opinion your willingness to constantly be learning
will take you very far in being a successful data scientist. The second mistake has to be learning
a lot of theory without any application. So this is something I've seen a lot of people do and I've
also done that myself. At a glance data science seems like a very academic field and it's hard
not to think like that when you're learning all the statistics and working with colleagues that
have PHDs. But in reality in the commercial world it couldn't be further away from the truth. I've
said this many times in my videos but the majority of data science that's conducted in a commercial
environment is all about solving business problems. I mean it's great to learn about the
mathematical intricacies of generative adversarial networks but always make sure to take a step back
and look at the big picture and try to figure out how this cool AI technique could be applied for a
practical solution. If you're enjoying the video so far then consider subscribing I make weekly
videos about demystifying data science. The third mistake has to be diving into the code without
a clear problem definition and plan. Early on in your data science career it's all very tempting to
start coding and build new things and it's also very tempting to dive straight into the code
without formulating a plan to solve the problem. I did this many times early on in my career and it
never ends well. You start writing the code and a few days later you realise that you didn't account
for a very obvious use case and now you have to rewrite the code in order to accommodate for it.
Formulating a clear problem definition allows you to take a step back and frame the problem in many
different ways. This will allow you to come up with the best solution to that problem and create
a code and model design according to it. The fourth mistake I often see is seeking a complex
solution rather than a simple one. When you study computer science or engineering at university,
you learn about all of the cutting edge algorithms along with very complicated maths and this gets
you in a habit of value and complexity as though it's a badge of honor and a mark of intelligence.
In the commercial data science world and the business world you're tasked with finding
a solution to the problem in the simplest way possible and not the most complex way. Because
that's a lot easier to explain to different stakeholders and it gives them more confidence
in the solution. So in practice this might mean opting for logistic regression when it solves
the problem adequately as opposed to a neural network which is inherently a black box algorithm.
So really as you become a data scientist get into the habit of seeking simple solutions first
instead of complicated ones. Now the fifth mistake that I see often is not asking for help enough.
Early on in your data science journey like Ken Jee says in his videos struggling with data science
problems early on in your career is a valuable part of the process and this is something I
definitely agree with and it really applies when you're trying to learn new things but sometimes
in the work environment as a practicing data scientist you might get stuck in a rabbit hole
crawling through multiple stack overflow pages. This can be a great way of wasting your entire day
trying to find a solution to a problem that your colleague could have helped you figure out in 5
minutes. I think Newton said it best that standing up on the shoulder of giants helped him see
further. The same analogy applies to learning and applying data signs. As a rule of thumb that I've
typically applied is if it takes me more than 30 minutes to an hour to solve a particular problem
which could be a code debug or a code error, I always make sure to ask one of my colleagues in
case that faced the issue before or if they've got a quick solution. But when you're trying to
learn new concepts it can be beneficial to struggle a little bit initially. That's it
for today if you enjoyed the video then smash that like button and I'll see you next time.