You are curious by nature, enjoy analysing and exploring data, and have always
looked for a promising career path… But your background is in Business,
Finance, Medicine, Economics, Engineering, or any non-technical field and you don’t
know if it’s possible to transition into data science.
Don’t worry! You’re certainly not the first person to go through this process. If you’re in doubt
whether this is possible to achieve, the short answer is ‘Yes, it can be done.’’ Even if you
don’t have a computer science degree and you are just getting started in data science. Thousands
of people have transitioned successfully, so if this is an opportunity you would like to pursue,
make sure you watch the entire video because we will share some invaluable advice.
But before diving into the topic of how you can switch career paths
and transition to data science, we would like to ask you a favour - subscribe to our channel and
like this video so you can be in the loop whenever we post relevant career content!
Ok. Great! So, transitioning to data science - there are plenty of reasons why you would want to
do that. Here are some of the most important ones: - This field is among the best paid in the IT
industry. And this is valid for any country around the world.
- The journey into data science can be very rewarding from a personal
development perspective - you will learn mathematics, statistics, computer science,
and will develop solid business understanding. - Moreover, your work will have significant impact
on the company that hires you – by uncovering hidden patterns and providing valuable insights
your actions can result in revenue growth, cost optimization, improved management decision making,
and executive strategic choices – making you a vital asset to the organization.
- Another important reason why transitioning to a career in data science is a very smart move
is the high demand for skilled data science professionals. Businesses are nowhere near
bridging the skill gap, so job security won’t be a big issue once you gain some practical experience.
We can agree that there are plenty of good reasons why transitioning to a data science
career can be a good choice. But which is the best way to do it and how to start?
The key to a successful career switch is adding some structure to the process and having clear
goals and milestones in mind. Here’s our suggestion on how to approach this journey.
The first step in the process is to get an idea of how the data science field works – make sure
you understand terms like ‘traditional data’ ‘big data,’, ‘structured data’, ‘unstructured data’,
‘business intelligence,’ ‘business analytics,’ ‘data analytics,’ ‘data science,’ and ‘machine
learning.’ Our Introduction to Data and Data Science course is the perfect way to acquire this
knowledge. We have provided a link to the course in the description of the video. It certainly pays
off to start with a high-level understanding of who does what in the world of data science.
Another way to improve your initial understanding is to study job descriptions. This is the second
step we would like to recommend. Make sure you go through various job postings and identify
the type of role you would like to apply for. This will help you grasp the difference between
a data scientist generalist, a data analyst, a BI analyst, an ML engineer, an applied analyst,
and so on. There is a wide range of positions within the data science job family.
Not all data scientists do machine learning modeling. Depending on the employer and the
needs of the organization, the responsibilities related to the role may vary. In other words,
in some companies, it might be the focus of your work , but other times the main responsibilities
will revolve around data cleaning and analytical tasks or deployment and operational tasks.
The goal at this stage is to decide on the type of role you would like to prepare for.
Perfect. Now you know
a bit about the field of data, and you’ve studied job descriptions,
so you’re aware of the different types of roles and responsibilities.
The third step is to evaluate your skills based on your previous education and work experience.
Knowing this will help you define your choices and points of strengths and weaknesses,
which will lead to a better transition plan. For example, if you have a software engineering
background, it will be better to target data science positions that revolve around the
deployment and the operational part as you will have good experience in this. Similarly, if you
have a finance background, you can target data science positions in the fintech industry. You
are a medicine graduate? There are many companies interested in hiring data science professionals
to work with clinical data. In this way, you can leverage your domain knowledge and turn
your potential ‘shortcomings’ into an advantage that gives you an edge over other candidates.
Makes sense, right? Step number four is to build
a learning plan that is coherent with the job role you would like to pursue. Don’t risk getting
lost along the way by engaging with materials that are not part of a structured curriculum.
We highly recommend following a step-by-step program, such as the one provided by 365 Data
Science because it organizes the subjects you need to learn in a structured curriculum
with sequential order. There are so many tutorials and trainings online,
but very few have been built from the ground up teaching everything a beginner needs to learn
in a time-efficient manner. Here’s what Meredyth Hein who successfully transitioned from pageantry
to business intelligence has to say about it: “I was overwhelmed with many resources I had
found on the internet and in person. I chose to study with 365 Data Science because it provides
a well-curated platform for my career needs that was also beginner-friendly,
accommodating to my day job, and allowed for flexibility to manage my time.”
Lastly, step number 5 is to work on projects that are suitable for your target industry
and desired job position. You did your analysis in step 3 and you know what kind of opportunities
you would like to pursue – now is the time to showcase your abilities and build an impressive
portfolio, which will ensure a successful transition in the field of data science.
If you’ve enjoyed this video, make sure to subscribe to our channel for more insightful
content. And don’t forget to spread the word so more people can take advantage of our content!