Today I'll give you the ultimate guide to build your career in data science. I'm Samanvay, a data
scientist living in Sydney, Australia. Getting into data science is a very confusing process.
Data science is so broad and there's so many ways of getting into it. So I'll break it down
as simply as I can and I'll try to give you a framework that you can use to build your career
in data science. Let's start from the very basics. Currently there's four different roles you can do
in data science. Being a data scientist, data engineer, machine learning engineer and a data
analyst. To be successful in any of the four professions you need a varying combination of
eight different skill categories. In my opinion, these eight skill categories are definitive
in being a data science practitioner. Here are the eight categories: statistics, machine learning,
data engineering, data analysis, data visualization, data leadership, stakeholder
management and communications. In terms of building your data science career there's three
phases to it. Let's start from the very beginning. Phase one is learning data science. Let's say that
you're someone that wants to get into data science but isn't really sure what step to take. You'll
fall into one of three categories. You'll either be a high school student, a bachelor student or
a mid-career transitioner. For the high school student, your best bet is to study a bachelor
of computer science with a specialization in data science. This gives you the best foundation to get
started in data science. If you're more curious of the bachelor's option then check out this video
I did recently reviewing all the bachelor's degree in Australia that prepares you well
for data science. For the bachelor student you've got two options. The first is to pursue a Master
of Data Science and the second is to do self-study of data science and try to get job in the field.
Now the first option of pursuing a Masters of Data Science is best for people that are coming from
a non-STEM background or are looking to immigrate into Australia or if you prefer a more structured
way of learning data science. The second way is to self-study data science. Now this works really
well if you've already got a STEM degree and you've got some mathematical and computer science
foundations. Then you can get away with learning data science on your own and then you can leverage
the skills you've learned to get a job in data science. For people that are looking
for a mid-career transition into data science, there's a couple of options available to you.
So the first option is clearly you can go off and do a Master of Data Science which is a good way of
starting from a clean slate and again this is applicable to you if you're coming from a field
that is very much non-STEM and very different to data science. This could be a good starting point.
The other option is if you're already working for a company which is maybe a tech company which has
a data science division. You could try to leverage your skills to get a position in that data science
team but this would mean that you're starting from a more junior position and in another case
if you're transitioning from a very much a STEM related field like engineering, then I think the
best approach is to self-study data science in your own time and try to leverage those
skills to try to secure a data science position because i know a lot of people that have switched
from software engineering into data science and their engineering skills is highly valuable when
they're making that switch. So try to figure out which of the eight skill categories you already
possess because potentially it means that you can leverage those existing skills to secure
a job in the data science field and if you'd like to go down the self study route I recently made a
three month roadmap to study data science. I'll post a link to the video here. Phase two of
building a career in data science involves getting a data job. The best way to get jobs is through
referrals. This means doing industry internships, doing research projects with your professors if
you're still studying and meeting as many data professionals as you can through your personal
networks or through industry networking events. Leverage your unique skills and try to stand out
as much as you can. Reach out to people you find interesting on Linkedin. The hardest part of phase
two is getting your foot in the door and it's okay if your first job is a little bit different to
what you thought you might work as. So for example if you wanted to be a data scientist it's okay
that your first job is a data analyst but once you get that first job you'll get more opportunities
to grow as you level up in your career. Phase three is really where it gets very interesting.
It's about career growth. So you finally got a job in data. Now the fun bit starts. Remember those
eight skill categories I talked about earlier. Now your task is to figure out which of the eight
categories you enjoy and the ones that you're good at. These eight skill categories will help
you figure out where you want to specialize in in your data science career when looking for new job
opportunities. You need to use these eight skill categories to figure out which job opportunity
to take. For example in my first job, I learned data engineering, statistics and communication and
for my second job I wanted to learn more machine learning. So I went ahead and found a job that
allowed me to upskill in that particular skill category. Then for my third job I wanted to
upskill my stakeholder management skills as well as my data leadership skills. So I went ahead
and found a job that helped me level up those skill categories. I call this approach the Skill
Maximization framework. This allows you to search for a job based on the skills you want to work on
rather than a salary or particular position. For me in the first half of my career I did quite a
lot of engineering. Whilst I really enjoyed doing the technical work and getting down in the weeds,
I quickly realized that I enjoyed talking to customers and doing stakeholder management a
little bit more. This phase is also about learning how to create opportunities for yourself within
a company. For example in my first job, I really wanted to learn a little bit more about how
customers used our products that I was working on. So I volunteered to go to customer meetings simply
by asking and through that process I quickly learned about my passion for helping customers and
talking to them. So these eight skill categories will help you become more aware of your strengths
and weaknesses. So as I mentioned, focus more on honing the skill sets that you really enjoy doing
and that you're good at. So focus on learning before maximizing your monetary outcomes. That's
it for this video. If you enjoyed it then smash that like button and I'll see you next time.