Can I switch my career to data science?

1
Guidel ines change to Data Science for
Are you at a stage in your life where you are
contemplating a switch to data science or
not? Several data analysts/scientists come to
this line and get stuck, often overthinking
about a mid-career change to data scientist. We
are to tell our audience about the smooth
transition to the brighter side of Data
Science. Since the explosion of data and the
ever-growing need for trouble less management
and utilization of data has massively become
imperative. The data industry has seen a steady
increase in the need for more and more data
analysts/scientists adapted to the hassle-free
storage and management of large data. For a
transition from a software engineer to a data
scientist, we are certain that our audience must
have several questions about the switch over to
the side of data science and how can this be
achieved with the least possible trouble or
glitches. With this article, we hope to solve all
the troubles and questions the operator may have
about the implication of data science in ones
professional career and the mid-career change to
data scientist. 1. What does it take to become a D
ata Scientist? Data Science is not a trade
mastered by all. It is an amalgamation of
multiple skills, reasoning, and preferences. The
big question How to become a data scientist?has
haunted thousands of skilled professionals for
many years which entails a path of learning,
planning consistency. So, the real
2
question to which a beginner should find the
answer is Should I become a Data Scientist? or
not. So to find an answer to this dilemma, sit
down and ask yourself some of these basic
revealing questions ? Do I enjoy big number
crunching rational problem-solving
scenarios? ? ? Do I relish working with
handling unstructured issues? ? ? Do I
appreciate deep research and spend hours
operating on data? ? ? Do I like constructin
g and representing evidence-based
stories? ? ? Do I consistently seize to quest
ioning, people s assumptions and am always
inquisitive to know Why? ? ? Do I love the
problem-solving aspect of a job and flourish on
intellectual challenges? ? The in-depth
penetration into these questions would help the
user to understand the need to change over to
Data Science as a professional calling or NOT.
3
2. Prospects of Data Science in the field of the
data Industry

• A lot of professionals entering the data industry
  often tend to get confused by the choices of job
  profiles that the data industry has to offer and
  often confuse a certain job with the definitive
  job description of a data scientist. The data
  industry is an amalgamation of several different
  kinds of job profiles, each with their defined
  job descriptions, because of which it becomes
  easy for beginners entering this vast domain of
  the data industry to get lost in the crowd and
  miss out on the once-in-a-lifetime opportunity.
• The following topic provides a brief on the job
  description of a data scientist in the data
  industry-
• Chart out the learning module
• Planning out the learning journey is the next
  step for evolving and transforming into a Data
  Scientist and sticking to the path is all the
  more important. Often many aspiring individuals
  tend to get stuck in this phase and often are
  unable to move ahead or deviate from the path
  altogether.
• Do you need a Ph.D. to become a Data Scientist?
• To get a better understanding of this concept,
  let us segregate the major job roles of a Data
  Scientist
• Applied data science role
• Research role
• The Applied Data Science role is fundamentally
  about working with pre- existing algorithms and
  understanding their implications in the normal
  world. In simpler words, it is the art of
  employing these distinct algorithms in your work
  profile. Which we firmly believe no one would
  require a Ph.D.
• The majority of the data science professionals
  that you would meet in the data industry field
  would fit in the applied data science category.

4
But If the user wishes to move ahead in the
research role then Ph.D. would become a
requirement. Fabricating research-based
algorithms from the base up, writing a
scientific thesis, etc are a perfect fit for the
job role of a research data scientist. It also
helps if the Ph.D. is in a similar field as the
job profile that the user is pursuing. E.g., a
Ph.D. course in linguistics will be extremely
helpful for a successful career in Natural
language processing. Is Data Science
certification a necessity? There are several
distinct means of reaching the destination that
the user seeks to reach. But Yes, a
certification in Data Science goes a long way the
skills that the user may have acquired are
because of the certification course and constant
practice and not because of the certificate
itself. Over the decade multiple courses have
been put up promising a certification in the art
of Data Science, but having a certificate does
not guarantee a job as a Data Scientist. Recruite
rs look into a lot more than just the certificate
which can be earned by sometimes just gliding
through the course. Recruiters while interviewing
pay special attention to the projects worked on
by the user and the skillsets employed whilst
completing the projects. In the end, the showdown
happens at the time of the interview where the
recruiter can come at the user from any angle
possible and query the user on how a particular
task was accomplished while working on the
project and how a certain skillset was utilized
to acquire the desired result. So, make certain
to practice multiple topic-related projects
while going through the course to capture and
gain clarity on the concepts being employed in
the project. Master Programming Several
languages may be utilized or the user might have
to use them while working in the job profile of
a Data Scientist, thus it is highly recommended
to master at least one programming language which
is the most popular and may be employed in
different scenarios of development. The most
versatile language extremely popular with
developers is the coding language of Python. It
is an extremely beginner-friendly language that
should
5

• be mastered by the user to employ its operations
  along with its basic machine-learning libraries
  like Pandas, NumPy, and SciKit Learn. The user
  should be well-versed and confident in writing
  custom functions, generators, etc. Even though
  the user might not be able to optimize the
  fabricated code, the user however should be able
  to transform well-thought operations into
  coding.
• Master the grammar of Data Science
• Statistics are by many profound data scientists
  and analysts is considered to be the grammar of
  data science. Statistics is the basics of acing
  the interview for a data science job.
• The user though may not require to have a
  statistical background but should be well versed
  in statistics topics related to data science as
  it is one of the prime requirements of being a
  data scientist. Some of the topics include
• Descriptive Analytics (median, mode, variance)
• Inferential Analytics (z-test, t-test, hypothesis
  testing)
• Statistical Analytics (forecasting, logistic
  regression)
• These are some of the basic statistical tools
  that the user might have to master and should
  not take much time to master regarding the user
  can find the appropriate resources.
• Hack the Hackathons
• Data Science is all about practical instincts
  rather than theoretical understanding. The user
  needs to have a knack for being able to choose
  the best algorithm or the best data cleaning
  methods. One look at the data and the user
  should be able to figure out the way to manage
  the data irrespective of the fact whether the
  user possesses an in-depth knowledge of the
  algorithms implied or not and the only place
  where the user might be able to hone these
  skills is Hackathon.
• Data Science Hackathons are the best stepping
  stone in the users path to perfection in the
  field of data science. The user may practice the
  skills on a dataset and win prizes whilst
  showcasing their skills to the world. These
  hackathon events and competitions have gained
  more popularity in the last

6
few years as several aspiring professionals wish
to take a bite of the data science cake. Taking
part in these events may also become a part of
the users portfolio and in turn, increase the
weightage of ones curriculum vitae. This can be
achieved via online platforms like Make Kaggle,
HackerEarth, Dare2Compete, etc. Polish soft
skills It would be completely inappropriate to
assume that just fabricating a module to analyze
and predict the future of the business would be
enough to become a world-class Data Scientist.
Several other soft skills surround and go a long
way in this domain that needs to be polished for
the effective fabrication of an analysis model
and its implication and employment across
multiple departments. Lets take a look at some
of these soft skills ? Communication
skills ? It cannot be stressed enough the
importance of this skill in the job profile of a
data scientist. Effective communication of the
insights being fabricated from the users model
to the stakeholders is of utmost imperative. No
matter how good a model it will never be able to
communicate the insights generated by it to the
non-technical management who are directly
involved in the firms decision-making
procedure. ? Storytelling Skills ? The way
the user communicates the insights generated by
the model determines the users ability as a
Data Scientist. One such example is the way the
user can show the rise in the sale of box office
cinema hall tickets on weekends over weekdays
which is the businesss highest
revenue-generating night.
7
? Systematic thinking ? Systematic
brainstorming of the issues that might arise from
several possible outcomes and looking for a
rundown of the imaginable solutions is the most
valuable possession of a data scientist. It
allows the data scientist to factor in the
different factors influencing the data and look
into it objectively from several points of
view. ? Being Curious ? As a Data Scientist,
the user needs to be curious at all times.
Curious about which algorithm, which issue, the
final objective from a certain point of view,
etc. This curiosity will help the user to
understand the matter at hand in a much more
detailed way and fabricate the model
accordingly. A successful transition to Data
Science is an upcoming trend in the data
industry and does not seem to be going anywhere
anytime soon. The mastery of this skill is
certain of creating a very vast and global impact
on the data management industry. The skilled
professionals will be handsomely compensated for
their mastery of this skill based on experience
and knowledge. Already being termed as the 21st
centurys sexiest job this profile has gained
accolades across several industries and the
demand for professionals who are good at this is
increasing more than ever before. So, we firmly
believe that in the coming 2 -3 decades the job
profile of a data scientist is here to
stay. Some useful links are Below To know more
about our Data Science certification Course visit
Data science certification To know more about
our Data Science visit - Data Science Guide must
visit our official youtube channel to know more
about data analytics data science and many
more visit - Analytics Training hub
8
(No Transcript)