How to Become a Data Scientist?

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How to Become a Data Scientist?
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How to Become a Data Scientist?

• Data scientists are specialists responsible for
  working with big data and letting their clients
  know the correct responses to their questions,
  whether its to construct a marketing campaign or
  to target the right product demographics. While
  data scientists come from various educational
  backgrounds, most have some kind of technical
  education.

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Data Science and Data Scientists

• Data science is a diverse field that includes a
  plethora of skills needed. A data scientist is
  usually someone who collects and analyses
  information in order to draw any specific
  conclusions that will help their employer. Data
  scientists use many different methods. There is
  something known as visualizing the data in order
  to view the data in a visual way. Data
  visualization is a way of allowing a user to spot
  distinct patterns that would not otherwise be so
  noticeable if the data were to be viewed in the
  form of mere numbers.

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Essential Skills for Becoming a Data Scientist

• Programming adequacy The code needs to be used
  to analyze and process information. Therefore in
  at least one programming language, programming
  skill is essential. The more languages a data
  scientist is comfortable within programming, the
  easier it is.
• Clear Vision Data scientists are required to
  develop powerful and fast algorithms. Therefore,
  to do that, innovation is very important. Data
  science is not only about whether it should be
  done, but also about how to do it.
• Curious Job Approach Curiosity is perhaps a
  requisite skill for the data science career. In
  large data sets, it is the innate curiosity of
  data scientists that leads them to search for
  interesting patterns.
• Mathematical Skill Mathematical ability is a
  must-have as data science involves churning out
  raw data and numbers.
• Resoluteness It can often be frustrating to work
  with a constant flood of data. Therefore,
  possessing a strong resolve will help everyone
  make it through the ordeals provided by the
  career of a data scientist and reap hearty
  benefits from it.

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A step-by-step guide about how to become a data
scientist
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Step 1 Ensure Its Meant for You

• First stuff first! Its important to double-check
  that it is exactly what you want before you set
  out on the road to becoming a data scientist. A
  very extensive branch of general studies is data
  science. Hence before taking the heavy load on
  your shoulders, you need to be confident. The
  Internet is full of some preliminary data science
  courses that will ensure that if you finally want
  to go for it, whether or not what you are looking
  for is right for you, as well as what you will
  get by following the career path. Some of the
  courses are paid and some are available for free.
  You can also search YouTube for the piece, too.
  Its time to move to the next level once youre
  confident about pursuing data science.

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Step 2 Get a Relevant Bachelors/Higher Degree

• Although not impossible, without obtaining any
  relevant degree, it is very difficult to obtain
  all the skills needed for a particular job. It
  may be a masters degree, a bachelors degree, or
  even a Ph.D. degree. Such credentials that are
  helpful to data scientists areApplied
  Mathematics
• Computer Science
• Data Management
• Economics
• Information Technology
• Mathematics
• Physics
• Statistics

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Step 3 Pick an Area of Interest

• There are many distinct routes to a fruitful
  career in data science that intersect. In
  computer science, mathematics, statistics, etc.,
  data scientists usually start from the
  undergraduate level. They are ideal for bagging
  jobs such as those of a data visualization
  expert, analyst of management, and analyst of
  market research. Some of them are also seeking a
  further doctorate degree in business solutions
  and enterprise science analytics. It is
  necessary, therefore to pick an area of interest
  and get a relevant degree for it

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Step 4 Get Certification

• Certifications are an essential part of any
  current professionals resume, especially anyone
  who belongs to the IT field. In addition to
  making the pursuer a marketable applicant for
  specific job requirements for data scientists,
  certifications may help to learn new and improve
  existing skills. For those interested in data
  science, there are a number of certifications
  available. 

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Step 5 Gain a Role

• When youre finished collecting all the academic
  and educational criteria, its time to try and
  play a part in the lucrative field of data
  science with your skills gained so far. Data
  science is a very complex field today. There is
  therefore a multitude of specialized positions to
  choose from. In addition, without any previous
  knowledge, it is possible to become a data
  analyst and then develop from there. Online
  platforms such as iCrunchData and Kaggle are
  perfect for looking for the right kind of work in
  data science. Every now and then, with constant
  advancement in the field of IT and data science,
  new and better options arise.

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Advantages and Disadvantages of Becoming a Data
Scientist
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Advantages

• Unique and demanding.
• Offers a wide range of everyday activities to
  ensure that the practitioners involved maintain
  interest.
• Working opportunities for a wide variety of
  businesses from all fields of the industry.
• Effective strategies for customer retention,
  general business issues, the introduction of new
  products, marketing, and much more can be found.

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Disadvantages

• The downside to an extreme range of subjects is
  that the specialist can not go further into a
  particular topic.
• Technologies used in the data science sense are
  continuously changing. Tools that are useful
  today, therefore, might be obsolete by tomorrow.
  In order to deal with any form of transition,
  data science needs to be on its toes.

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Data Science is not as same as statistics

• Mistaking data science for statistics is very
  easy. Even though the two share several elements,
  each of them is a separate sector. Typically,
  statistics depend on proven theories and focus
  more on the testing of hypotheses. In comparison,
  relative to data science, it is an ancient
  discipline that has altered very little in the
  past few decades. Data Science is relatively
  recent, on the other hand. Data science depends
  heavily on computers and technology, unlike
  statistics. In addition, it is a constantly
  evolving field.

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Conclusion

• So how to become a data scientist was all about
  that. The area of data science is constantly
  increasing and there are no signs that it will
  subside any time sooner. At least until the world
  finds something better for doing anything that
  depends on them than data and knowledge, which
  is, of course, a very impractical possibility.
  Therefore its a good time to start a career in
  data science.