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Data Scientists: Why high on demand?

During 2021-22 the most popular and sought-after topic in the market among young people was Data Science Career. In 2012, Thomas Davenport and former Chief Data Scientist of America D.J. Patil dubbed the Data Scientist portfolio "The Sexiest Job of the Twenty-First Century" in Harvard Business Review. Consider a career in data science if you want to work in a high-demand field. According to the U.S. Bureau of Labor Statistics, data scientist jobs rank sixth among the 30 fastest-growing occupations (from 2021 to 2031), with a median annual pay of $100,910. In European countries, this career offers an average annual salary of €139,840.

History of Data Science at glance:

The term "Data Science" is not a new field, it has grown significantly over the last 50 years. A journey through data science history reveals a long and winding route that began in 1962 when mathematician John W. Tukey foresaw the effect of modern-day electronic computing on data analysis as an empirical discipline. According to “Statistics.com” statistician John Tukey is regarded by some as the father, or at least one of the fathers, of data science. The growing subject of data science is highlighted here:

(1) The phrase "data science" was coined in 1974 by Peter Naur as an alternative term for computer science.
(2) In 1981 IBM and Apple in 1983 had released their first personal computers respectively.
(3) In 1985, C. F. Jeff Wu used the term "data science".
(4) The International Federation of Classification Societies became the first conference to include data science as a theme in 1996.
(5) Further to his 1985 talk at the Chinese Academy of Sciences in Beijing, C. F. Jeff Wu proposed in 1997 that statistics be renamed “data science” (see serial # 3 above).
(6) Hayashi Chikio proposed for data science as a new, interdisciplinary idea in 1998, with three components: data design, data collecting, and data analysis.
(7) In 1999, Jacob Zahavi stressed the need for new tools to handle the massive….pointing out that Special data mining tools may have to be developed to address website decisions.
(8) In the year 2000, a new era of data science begins, and numerous academic journals start to recognize data science as an emerging discipline. The National Science Board proposed a data science job path in 2005 to assure that there would be professionals capable of managing digital data collecting.
(9) In 2012, technologists Thomas H. Davenport and DJ Patil declared "Data Scientists. DJ Patil was also appointed by President Obama to be the first U.S. Chief Data Scientist.
(10) Data Science (DS) is becoming an integral component of both corporate and academic research. Common examples include artificial intelligence, machine learning/translation, robots, speech recognition, the digital economy, and search engines. Data Science research has expanded to include biological sciences, health care, medical informatics, humanities, and social sciences, as well as economics, government, business, and finance.

Scope of Data Science:

Banks, insurance corporations, merchants, healthcare professionals, and even state agencies all have extensive data science teams; big financial institutions may engage hundreds of data scientists. Data science has also been useful in tackling societal catastrophes, such as monitoring and projecting Covid-19 cases and fatalities, assisting in the response to natural disasters, and combating falsehoods and cyber intrusions.

Data Scientists are currently having an impact on almost every organization around the globe, particularly in developing nations, and there is still opportunity for large data-related activities such as data scientists, big data engineers/architects, and data analytics. There are numerous job opportunities available in various sectors like as:

  • Aerospace
  • Media
  • Image & Speech recognition
  • eCommerce
  • Law enforcement
  • Marketing/advertising
  • Transportation
  • Sports/Gaming

Data Scientist Education:

There are a lot of definitions of data science and what it covers. The problem with Data Science and traditional college education is that no one agrees on what constitutes the finest data science degree. Experience with computer programs, statistics, probability, commerce, and communications is most often required in this sector. However, there is broad consensus that data scientists require three key competencies:

(1) Mathematics/statistics (to understand what types of analysis is possible and the techniques involved)
(2) Databases/programming (to write code that produces the algorithms that accomplish the task)
(3) Domain expertise/business insight (to optimize the technical work referenced above to the unique conditions of the organization you are working for and the business domain you are operating in)

College/University Admissions & learning:

  1. (According to Data Science Program. Org. a number of universities that provide committed data science degree programs, even Online Data Science Degrees. The Master of Information and Data Science (MIDS) is an online degree program designed for individuals seeking to further their careers in data science.
  2. (As per FAQ page of “Coursera” an individual can learn data science anytime and anywhere in the world with the availability of an internet connection.
  3. Study online or on Campus at IU International University of Applied Sciences and LSBU (London South Bank University) provides the facility of 100% online study from home, with an option of whether full-time or part-time. You can also take your examinations online whenever it is convenient for you. The degree is recognized anywhere in the EU. Hear a student can earn a British degree together with a German certificate.

Data Scientist Expertise:

  1. Computer science
  2. Math
  3. Statistics
  4. Machine learning
  5. Domain expertise
  6. Communication and presentation skills
  7. Data visualization

Branches of Data Science:

DS is an interdisciplinary field that extracts insights from organized and unstructured data using scientific approaches. The field of data science includes many sub-disciplines, such as: 
  1. Data Mining and Statistical Analysis.
  2. Business Intelligence & Strategy-Making.
  3. Data Engineering and Data Warehousing.
  4. Database Management and Data Architecture.
  5. Operations-Related Data Analytics.
  6. Machine Learning and Cognitive Specialist.
  7. Market Data Analytics.
  8. Cybersecurity Data Analysis.

Categories of Data Scientists:

A) Entry-Level Data Scientist - who have earned any of the following degrees:
  • Bachelor in Computer Science
  • Bachelor in Information Technology
  • Bachelor in Data Science
  • Software Engineering Plus Diplomas and short courses in data science
 B) Mid-Level Data Scientist: who have earned MS/BS Degree in Computer Science, Engineering Plus experience in a relevant field is preferred

C) Senior Data Scientist usually MS/Ph.D. in Data Science.

 D) With sponsorship serving Database developers and Software programmers, as well as traditional Scientists and other professionals in specific subjects, can also become data scientists.

A career in Data Science:

Data Science is a discipline that uses computer science and statistical methodology to produce meaningful predictions and obtain insights into a variety of industries. While Data Science is utilized in fields such as astronomy and medicine, it is also used in business to assist in making better decisions. Data scientists can work in a wide range of fields, including business intelligence and statistical analysis. Some jobs to become a data scientist include:
  • Data Scientist
  • Data Analyst.
  • Data Engineers.
  • Database Administrator.
  • Machine Learning Engineer.
  • Cybersecurity analysis
  • Data warehousing
  • Data visualization
  • Cloud computing
  • Data Architect
  • Statistician
  • Business intelligence/Business Analyst.

Issues with data scientists:

  • It is an ambiguous term.
  • It is quite difficult to become a Data Science master.
  • The issue of Data Privacy.
  • Unexpected outcomes may result from arbitrary data.
  • Data scientists cannot conduct observational research; they may identify a few corrections but cannot speculate on the fundamental causes.
  • A huge chunk of Domain knowledge is required.
  • Data Science will mostly focus on the analytics area.

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