So, you want to become a data
scientist but have no prior experience? Employers, on the other hand, are only
interested in candidates with working experience. Most of us have been there, so don’t worry!
The good news is that you’ve clicked on this video, and we will now share
our secret formula for overcoming this seemingly insurmountable challenge.
Over the years, 365’s team has trained hundreds of thousands of people.
We’ve heard many
stories about what has worked for our students and what hasn’t. And, in this video,
we’ll gladly share our findings with you. One of the aspects many individuals underestimate
is having a clear roadmap in mind. So first, we’ll talk about the learning journey you need to
embark on to become a data scientist. Then, we’ll focus on the things you can do to put the odds in
your favor and land your first data science job. Right. Learning journey.
What
steps do you need to take? We recommend you start by gaining a broader
understanding of the data science field and how it adds value to businesses.
• Study various data science terms and their application.
• Find out why data plays such an important role in company management and profitability.
• Determine how a company can be positioned in order to thrive in a world of data. Other things you can learn include:
• what types of data analysis techniques are there,
• as well as how and why do we apply machine and deep learning algorithms,
• and so on… You might even want to take a few business
courses to ensure you understand how a company creates value and what its current
strategic positioning is within its industry. Our courses on Intro to Data Science, Data
Strategy, Data Literacy, Intro to Business Analytics, and Data-driven growth can be the
perfect gateway to obtaining this knowledge. Once you’re past this initial stage,
it’s time to learn the fundamentals. Start with Statistics, Mathematics, and
Probability to build a solid foundation that would allow you to understand technical topics
later on.
In this way, you would not only know how to apply existing frameworks in blindly, but
will also recognize how they were constructed and understand their limitations in each situation.
This is precisely the reason why our program covers the fundamentals first and only then
introduces you to coding with programming languages such as SQL and the application
of advanced statistical concepts in Python. But eventually, you will need to learn how to
code. SQL and Python are the two most popular coding languages a data scientist needs. SQL
allows you to work with structured databases, while Python is the tool you use to manipulate
data and perform analysis. Certainly, one of the best tips we have for you if you are a beginner
in programming is to learn the basics, then apply what you’ve learned by working on simple projects.
Our video on the ‘4 essential Python projects for beginners’ is a great source of inspiration if
you’re looking for beginner-friendly projects. At this point, you will be ready to hone
advanced topics such as supervised and unsupervised machine learning algorithms,
potentially even deep learning… But to be able to showcase your skills properly, you’ll
need great data visualization skills too. So, don’t forget to work on this aspect.
Right.
