What
does a data scientist do?
Practically everything. Data scientists have a strong curiosity and a passion
for achieving practical business impact. In addition, they boast exceptional
judgment combined with an analytical mindset.
But what sets apart the best from the rest is a knack for creative problem
solving and willingness to learn new technologies and skills.
Why is that so important? Because data scientists must use machine learning
models to solve challenging problems in all business areas. Moreover, they are
real champions in utilizing statistical natural language processing to mine
unstructured data and extract insights. Which doesn’t mean they don’t also deal
with structured data. In fact, it’s quite the contrary. Data scientists model
structured data with advanced statistical methods and algorithms to perform
analyses. Then, they interpret the results and visualize the data to tell the
most compelling stories to management and stakeholders to achieve the company’s
business goals.
What’s the data scientist career path?
You can start as data architects or data analysts, and gradually work their way
up to this coveted job. In any case, if you’re aiming at the data scientist
position, here’s everything you need to know to get started on the right career
track.
How to become a data scientist?
Everybody wants to know how to be a data scientist. Well, maybe not literally
everybody, but this is undeniably the most sought-after career in the data
science field at the moment, as the demand for data scientists is constantly
growing. Data scientists are independent and impactful, and If you want to get
hired as one, you’re probably wondering what the data scientist duties are, how
to acquire the necessary skills to apply for data scientist positions and if
the salary will meet your expectations. Is data scientist a good career? To
save you browsing through job boards and career websites to find the various
information you need, we created this ‘data scientist snapshot’. We believe it
will give you the insights you need to decide if the data scientist role fits
your career needs and aspirations.
What education do you need to become a data scientist?
For starters, you don’t need a Master’s or a Ph.D. degree to become a data
scientist. If you already have it – great! It’s certainly a plus! However, a Bachelor’s
degree is good enough to get you on the data scientist path. According to our
extensive research on how to be a data scientist, a background in the following
disciplines increases your chances of landing a data scientist job:
Economics and social sciences, which includes economics, finance, business
studies, politics, psychology, philosophy, history, and marketing and
management;
Natural science, including physics, chemistry, and biology;
Statistics and mathematics;
Computer science;
Engineering;
Data science and analysis, which includes machine learning.
The good news
is that even students from entirely different areas of studies, hold a very
good chance of becoming data scientists. According to data from successful data
scientists’ LinkedIn profiles, 43% have completed at least one data science
online course with 3 certificates being the average.
So, if you’ve never written a line of code in your life, you can still make up
for it with determination and commitment to learning… And ultimately start a
career in data science.
What data scientist qualifications you should acquire?
Data scientists are famous for their robust skillset and competences. So, here
are the must-have qualifications you need to become a data scientist. But how
can a single person be outstanding in the technical domain, business acumen,
and interpersonal communication simultaneously? Well, it’s all about acquiring
the right skills
Technical skills
Excellent programming skills in R or Python and its data science libraries
(Pandas, scikit-learn);
Experience with relational databases and SQL;
Experience in MATLAB;
Advanced practical knowledge of data science and machine learning /AI
development frameworks;
Excellent analytical and learning skills;
Experience in Deep Learning frameworks (e.g. TensorFlow);
Experience with NLP algorithms is an advantage.
Practical skills
Ability to start initiatives;
Integrity and
confidentiality;
Desire to drive innovation and generate unique solutions;
Growth mindset;
Ability to track and share external trends, best practices or ideas;
Drive for performance;
Accountability;
Ability to thrive under pressure and fast pace;
Ability to take an unpopular stance.
Soft skills
Strong written and verbal communication skills
Leadership skills;
Ability to develop and leverage relationships with stakeholders to achieve a
company’s business goals;
Ability to inspire others and support others’ development to achieve full
potential;
Readiness to collaborate with engineering and BI teams.
Well, now that you’ve got a good idea of what it’s like to be a data scientist
and how to become one, you should feel more confident and determined on your
path.
Source : 365 Data Science
