What is data science? A very simplified explanation.

 What is data science? A very simplified explanation.

What is data science? A very simplified explanation.
What is data science? A very simplified explanation.

What is data science? A very simplified explanation.

Overview of Data Science:

Harvard Business Review defined data science as one of the most exciting and prevalent fields in the 21st century. Many data science experts have pointed out that the excitement surrounding data science has added an intriguing touch to other fields, such as statistics, for example. Did you know that the title "Oil of the 21st Century" is given to the field of data science to highlight its importance and scientific value in our lives?

Data science is considered a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data, whether it is organized or unorganized, just like data mining. The specialization was formerly called "Datalogy" and is now known as "Data Science."

In 2012, when Harvard Business Review referred to data science as the "sexiest job of the 21st century," it gained widespread recognition. It is now used interchangeably with previous concepts such as business analytics, business intelligence, predictive analytics, and statistics. Hans Rosling even reframed the notion of data science in a 2011 BBC documentary, stating, "Statistics has now become the sexiest subject around us." Nate Silver also pointed out that data science has made statistics more exciting.

 In many cases, various methods and solutions have been rebranded as data science to make them more appealing. While some universities currently offer data science degrees, there is no consensus on the definition or appropriate curriculum.

Definition of Data Science:

Data science is an interdisciplinary field that relies on scientific methods, processing techniques, algorithms, and systems to extract knowledge and insights from data in its various forms, whether structured or unstructured, similar to data mining. Data science also relies on machine learning techniques, artificial intelligence, and big data processing software.

Data science can be defined as a concept that unifies statistics, data analysis, machine learning, and related methodologies to understand and analyze real-world phenomena using data.

Data science involves the use of computer hardware, equipment, programming systems, and algorithms to solve problems and interpret real-world phenomena. It draws on theories derived from other fields, including mathematics, information systems, statistics, and computer science.

History of Data Science:

The term "data science" (originally used interchangeably with "Datalogy") has been around for over thirty years. It was initially used as an alternative to computer science by Peter Naur in 1960. In 1974, Peter Naur published a concise survey of computer methods, in which he freely used the term "data science" in his study of contemporary data processing methods used in a wide range of applications.

Personal Traits of Data Science Practitioners:

  • Creativity, curiosity, and a love for exploration.
  • Communication, analysis, planning, organization, and problem-solving skills.
  • Proficiency in mathematics and quantitative techniques.
  • Ability to simplify and explain complex matters.
  • Desire to adapt and develop skills in systems, software, and applications, and keep up with technological updates and changes.
  • Skills in understanding through inference.
  • Data and information gathering skills and their interconnection.
  • Ability to store and learn a lot of new information as this field constantly introduces new knowledge.
  • Decision-making skills.
  • Passion and patience.
  • Self-improvement and personal development.
  • Proficiency in computer and electronic device handling.
  • Persistence and not getting bored with routine and desk work.
  • Willingness to learn the English language since many materials in this field are presented in English, in addition to technical terms that need to be learned, understood, and used.
  • Ability to formulate problems and work with numbers, which can only be achieved through analytical and logical thinking skills.
  • Strong skills in science, technology, engineering, and mathematics, are often referred to as STEM skills.
  • Readiness to pursue a degree in computer science.
  • Continuous pursuit of growth and development.
  • Professional work ethics.
  • Engineering skills for the applications in civil engineering that this field encompasses.
  • Knowledge of work strategies and core knowledge.
  • Familiarity with programming languages.
  • Familiarity with statistics.

Specializations and branches of Data Science:

 Data science is not classified under other specialties because it is originally a branch of computer science. However, this field covers related topics such as big data and data analysis.

 The main challenge with computer science specializations, in general, is that they are constantly evolving at a rapid pace. Not all countries and universities can keep up with these advancements. For example, computer science is taught in Arab countries, but specializations like artificial intelligence and data science are not commonly offered.

Subjects in Data Science:

 Please note that specific course names and structures cannot be mentioned as data science is not currently taught in the Arab world. However, we can provide a general overview of the subjects, topics, and courses typically covered in this field, which include:

  • Mathematics: This includes the study of linear algebra and statistics in the context of data science.
  • Programming Languages: Programming languages are highly important in data science. Some notable examples include Python, C++, Matlab, and Go.
  • Data Management: These courses cover data mining, data extraction, and information retrieval.
  • Data Analysis: Data analysis is a central focus in data science.
  • Machine Learning: Machine learning covers various topics related to artificial intelligence, robotics, and enhancing the learning process.
  • Algorithms: Algorithms are a set of mathematical and logical steps used to solve problems.
  • Big Data: Big data refers to extremely large datasets that continue to grow and cannot be processed or handled using traditional methods. Innovative techniques are required to deal with such data.

 It's important to note that these subjects, their names, and courses may vary from one university to another. Some may offer more advanced courses or different pathways. However, they all contribute to the study of data science and aim to achieve the same goals.

Duration of Data Science Studies:

The typical duration to obtain a degree in data science or related specializations is around 4 years.

The recession rate and demand rate for the data science specialization:

 Most technological specializations have become leading fields in the list of future specialties and in-demand professions. Students are now rushing and fiercely competing to pursue studies in computer science disciplines, including data science, software engineering, robotics, and artificial intelligence, to secure a desirable and promising career.

 We now live in a global village due to technology, and we cannot ignore its importance. It has facilitated many aspects of our lives to the point where we can't do without it for most of our daily activities.

 It is difficult to classify data science as a specialization that can become stagnant or saturated, as it is a highly sought-after field for the future.

What does the demand for data science specialization mean?

The demand for data science specialization means that the job market needs it, and as a result, graduates can find employment.

What about the recession and saturation in the field of data science?

Recession and saturation refer to the point at which the data science field reaches a saturation level in the job market of a particular country. Consequently, it becomes challenging for graduates to find employment.

Based on this, and considering technological advancements, data science is considered one of the most widely spread specializations at present, and its demand is expected to increase in the future. This is due to the need of large multinational companies for graduates and experts in these fields, as these companies always require specialists to analyze the vast amounts of data they acquire.

Advantages of studying data science:

Studying data science has several advantages, including:

  • Becoming a specialist or expert in the field of data science.
  • Using a variety of techniques, visualization, analysis, and statistics.
  • Gaining experience in global and important software, such as Microsoft Data Science.
  • Being able to solve many problems people face using data.
  • Being one of the most developed and widespread fields in the current era.
  • Working in a team-oriented environment.
  • Thinking logically.
  • Developing skills in the field of computer science, particularly in data extraction, mining, and information retrieval, which are among the most important topics in recent scientific research.
  • Opening doors to exciting and engaging professional, academic, and scientific career paths due to its novelty, development, and relevance.
  • Harnessing and utilizing technology correctly.
  • Expanding knowledge and culture, starting from data science and extending to calculus, mathematics, and physics.
  • Job opportunities in major global companies.
  • High salaries.

Negatives of studying data science:

Data science specialization also has some negatives, but they are relatively few compared to the positives. Some of the negatives of data science include:
  • Occasionally feeling bored due to routine tasks.
  • Mental exhaustion from constant thinking and putting in a lot of mental effort.
  • High costs of studying the specialization.
  • Limited number of graduates and experts in this field.
  • Fatigue and working long hours.
  • Considered a somewhat difficult and complex field of study.
  • Requires continuous monitoring as lack of updates can lead to challenging problems.
  • Lack of availability of specialization in the Arab world.
  • Lack of privacy as it is difficult to hide the identity of data owners.

Job fields in data science specialization:

Some of the prominent and important job fields in data science specialization include:

  • Specialization in programming.
  • Data scientist.
  • Technical support specialist.
  • Systems analysis, design, and development.
  • Data analysis.
  • Supervision of computer and robotics work.
  • Specialization in databases.
  • Working in the education field.
  • Computer engineering.
  • Specialization in machine learning.
  • Working in research fields in computer science.
  • Working in network management and security.
  • Technical project management.

Sectors that data science specialization qualifies for:

Some of the prominent sectors where graduates of data science specialization can work are:

  • Ministries.
  • Schools.
  • Universities.
  • Libraries.
  • Research centers.
  • Companies.
  • Industrial institutions.
  • Technical institutions.
  • Professional institutions.

Additionally, one can also work in the field of teaching and training others on software usage, artificial intelligence, and development.

Average income for data science graduates:

The job market for data science specialization is characterized by high salaries, although the salaries earned by graduates depend on the company, experience, academic level, and personal skills. However, salaries generally start as follows, according to statistics compiled by the University of Wisconsin Data Science:

  • Data Scientist: $50,000 - USD 75,000.
  • Data Analyst: $90,000 - USD 140,000.

In addition, it is possible to work in major international companies that offer high and rewarding salaries for data science graduates, such as:

  • The annual salary at Google is USD 152,856.
  • The annual salary at Apple reaches USD 145,974.
  • Employees at Twitter receive a salary of USD 135,360.
  • Microsoft offers salaries of USD 123,328.

Top universities teaching data science specialization:

Data science is considered a very recent specialization in the Arab world, where no university offers this major at the undergraduate level. However, a master's degree in data science has been added at Princess Sumaya University in Jordan, which is considered the first step towards developing data science in an Arab country.

It is unfortunate that most universities in the Arab world do not keep up with this specialization and do not offer it. However, there are some introductory and training courses in this field.

Here is a list of top universities teaching computer science, data science, and data analytics:

  • Carnegie Mellon University.
  • Stanford University.
  • Santa Clara University.
  • University of Michigan.
  • University of Texas at Dallas.
  • University of Virginia.
  • University of Florida.
  • Purdue University.
  • University of Maryland.
  • Georgia Institute of Technology.

If this specialization is not available in your country, you can study some related majors that serve as a foundation for data science, such as:

  • Mathematics.
  • Statistics.
  • Engineering.
  • Physics.

Prominent figures in data science:

Many scientists, experts, and prominent figures in all sciences, and specifically data science, share many goals and tasks. Some of the most famous figures in the field of data science include:

Jim Gray: He is an American computer scientist who won the Turing Award, which is given to people who have made a lasting and significant technical contribution to computer science. This award is similar to the Nobel Prize, but the Turing Award is only given in the field of computing.

  • Nate Silver: He is an American statistical analyst who is known for developing the PECOTA system, which predicts the performance of baseball players.
  • Peter Naur: He is a renowned computer scientist who won the Turing Award for his contributions to the field, in which he used data science as a substitute for computer science.

The importance of data science in our lives:

The value of data science is increasing day by day, and everything is affected by the impact of information technology and the enormous amount of data that is multiplying. There is now a never-ending sea of data and information, and it is growing and multiplying at an enormous rate. Data has become something that cannot be removed or eliminated from our lives, and this is what makes data science one of the most important fields in our current time.

In conclusion, dear reader, you should know that data science is a field that must exist in the future, due to its importance. We always need this science due to the developments and updates that our current era is still reaching.


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