What is a Data Scientist?

What is a Data Scientist?
Imed Bouchrika, Phd by Imed Bouchrika, Phd
Chief Data Scientist & Head of Content

The explosion of data science in the past decades has given rise to the new breed of professionals—the so-called data scientists. These experts have deep knowledge of data, algorithms, and data visualization (Chojecki, 2020), and have already taken root in various industries. 

Unfortunately, the “amorphous” nature of data science as a discipline has caused confusion as to what exactly data scientists are. Further, the diversity of data and continued recognition of the benefits of data analytics have led to the emergence of multiple job titles in the data science universe (Voulgaris, 2014). As a result, people cannot quite unravel the specific roles of each position in this field. 

In brief, data scientists are tasked with collating the right kind and amount of data to be subjected to analytics, from which insights can later be drawn. But, that is superficially what data scientists do, there is a whole lot more to their roles. To shed more light, we will dig deeper into the data science world to reveal the history of the profession, its job descriptions, requirements for becoming one, and current career prospects.

What is Data Scientists Table of Contents

  1. Brief History of Data Science
  2. What is a Data Scientist and What are His Roles?
  3. Types of Data Scientists
  4. How to Become a Data Scientist
  5. Required Career Background
  6. Data Science Careers

Brief History of Data Science

Data science is not an entirely new field. It has been around for decades now, but over the last 50 years, the discipline has gained immense popularity.

John Tukey and Peter Naur Predictions

A trip down the memory lane takes us back to 1962 when John W. Tukey, a renowned mathematician, first predicted the impact of modern-day computers on empirical science and data analysis. Regarding the imminent shift in the world of statistics, Tukey wrote in The Future of Data Analysis “… Data analysis, and the parts of statistics which adhere to it, must…take on the characteristics of science rather than those of mathematics… data analysis is intrinsically an empirical science…” (Tukey, 1962).

To back Tukey’s predictions, in 1974, Peter Naur⁠—a pioneer in computer science⁠—described the basic concepts of the approaches and techniques used in data science in his book “Concise Survey of Computer Methods.” Naur presented his definition of data science saying “… data science is the science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences…” (Naur, 1974).

Development of Computers

The data science of today is not the perfect match of Naur’s definition and is a far cry from what Tukey envisioned. After all, before the development of computers, any analysis that was performed was far more rudimentary than those that are possible today. The advent of computers cleared the way for efficient data collection and intricate and large-scale data analyses akin to what people witness today.

In particular, in 1981, IBM developed the first personal computer, trailblazing what panned out to be a well-trodden avenue. In 1983, Apple created Lisa, the developer’s first personal computer with a graphical user interface (Computer History, 2020). With two tech behemoths leading the way, and as other companies jumped on the bandwagon, computing gathered pace and throughout that decade,  evolved more rapidly.

The evolution of computing enabled companies to collect data more easily, but converting it into knowledge and information was not feasible. It took nearly two decades, since the development of personal computers, to successfully gather insights from data collected through computers (University of Wisconsin, 2017).

The Emergence of Data Science as a Discipline

2000 was a new dawn for data science. Throughout the 2000s multiple academic journals started to recognize data science as a discipline on the rise. For example, in 2005, the National Science Board (NSB) recommended a definitive career path for data science to impart knowledge and ensure aspiring data scientists could collect and manage data more effectively (University of Wisconsin, 2017). As companies began to view data as a precious commodity upon which they could gather insights to inform better decisions, the role of data professionals became more prominent. 

In 2010, the tremendous advancement in computing technology brought data science to the fore. The discipline took a turn for the better and the advent of mobile devices, such as Apple’s iPad and iPhone further gave the evolution a much-needed impetus (University of Wisconsin, 2017). From this point on, data science became a mainstay and it began to permeate nearly every industry. 

Over the past decade, data science has evolved into one of the most popular niches. Academic institutions, companies, and governments are constantly striving to hire data science professionals (National Academies Press, 2018). Still, data science is not formally delineated and the concepts, main tenets, skills, knowledge, and ethics that govern this discipline are points of active, polarized discussion. Its evolution is on high gear, but people in this niche continue to play by the present rules (National Academies Press, 2018).

What is a Data Scientist and What are His Roles?

Data science is, without a doubt, a varied field. But, basically, it deals with the methodologies, principles, tools, techniques, and processes to gather insights or knowledge from data—structured or unstructured (Sarkar, Bali, & Sharma, 2017). In a nutshell, it is an umbrella term that encompasses data mining, data analytics, Machine Learning (ML), and other specific domain expertise (Chinnamgari, 2019).

In this regard, a data scientist is a person tasked with performing the aforementioned roles pertaining to the field of data science. More generally, data scientists handle the entire soup-to-nuts analytics process to mine meaning from data and communicate those insights to other teams in an organization to be acted upon. The role entails identifying the specific data-analytics problem(s) and then collecting, cleaning, orchestrating, and crunching relevant data to prepare it for robust analytics.

Once the data is in shape, the data scientist performs statistical analysis to find patterns, trends, and pertinent answers to specific business questions. The exploratory data analysis combines data sense and visualization, allowing users who are not well-versed in the data to understand its implication. Even more, data scientists design machine learning models and algorithms to make accurate projections about the future based on the available data.

Data scientists play a significant role in corporate growth, especially in the new data-driven world. They enable organizations to better understand their customers and business in general, to make accurate, data-backed decisions.

The specific roles of data scientists are listed as follows:

  • Pinpointing the specific data analytics problem with the greatest opportunities to the company.
  • Identifying the pertinent data sets and variables.
  • Collecting data—both structured and unstructured—from disparate sources.
  • Cleaning and authenticating data to ensure uniformity, accuracy, and completeness.
  • Building and implementing machine learning models and algorithms to reveal data patterns and insights in order to devise intelligent systems that are able to make predictions and decisions from future measurements.
  • Analyzing the data to unearth trends and patterns.
  • Extract meaning from data to find solutions and opportunities.
  • Visualizing data to communicate findings in an easy-to-understand way to stakeholders.

Types of Data Scientists

Data science is a diverse, evolving field, and the role of data scientists has yet to be fully delineated. It draws from multiple disciplines, including mathematics, statistics, signal processing, operational research, database and storage, scientific computing, and machine learning (Fontama, Barga, & Tok, 2015). Moreover, data science has a wide swath of applications in different verticals, such as sales, marketing, products, and engineering to anchor decision-making.

As a result, it is only expected that the role of a data scientist will vary based on the relative importance of the discipline to a particular position. Therefore, even though people working in this field claim to be “data scientists,” they obviously do not belong to the same category. According to the O’Reilly report “Analyzing the Analyzers’’ data scientist can be classified into four types as follows (Harris, Murphy, & Vaisman, 2013):

  • Data developers: They handle the technical issues that are associated with data management—how to source data, store it, and learn from it. Data developers tend to be superior in machine learning and programming but possess weak statistics and business skills.
  • Data researchers: On the other hand, data researchers often come from academic backgrounds, primarily in physical or social sciences or in statistics. In fact, more than 50% of data researchers have a PhD and 75% have been published in peer-reviewed journals (Harris, et al., 2013).  On the contrary, they possess weak programming and business skills.
  • Data creatives: They are the quintessential Jack-of-all-trades of data scientists who excel at using a wide range of technologies and tools to derive meaning from large sets of data. Data creatives can perform statistical and other advanced analyses, and create compelling visualizations to help stakeholders make sense of data. Their skillset combines excellent programming, machine learning, big data, and other skills necessary to handle heaps of data.
  • Data business people: As the name suggests, data business people are responsible for analyzing and interpreting data from a business perspective. They have an eclectic blend of technical and business skills, which, when coupled with analytical proficiency helps mine meaning from data to aid organizational decision-making.

How to Become a Data Scientist

Data scientists are not born, they are made. In this sense, people from different backgrounds can join this field. That being said, to become a data scientist, one has to gain a blend of requisite professional skills and personal traits.

However, as data science is a hybrid of multiple disciplines, it can be overly confusing for people looking to jump into a career in this field. Traditionally, people who wanted to work as data scientists had to endure the sophistication of the learning process and rely on constricted resources to hone the right skills.

Nowadays, however, the learning curve is not that steep anymore. It has been flattened, but people are confronting the exact opposite side of an issue. Apparently, there is an overabundance of learning resources and numerous academic paths to pick. As such, each individual can choose an entirely unique path, depending on one’s work experience and educational background.

Here are some steps on how to become a data scientist:

Preparation: Is This Career Really for You?

As mentioned, people from different areas of expertise can be groomed as a data scientist. This, however, does not mean this career is for everyone, and so before you take the plunge you have to find out if it is for you.

A good candidate must have a curious nature that nudges a constant pursuit of knowledge. This is primarily because data science has so many areas that a data scientist needs to get a hang of. Without inherent curiosity, it is not possible to grasp all these principles, let alone analyze large sets of data points.

In addition, solid organizational skills are required to keep every piece of data tucked away in a secure and easy-to-access place. Remember, data scientists work with millions of data points, and keeping them organized is essential. Besides, organizational skills come in handy when you are making conclusions and presentations of results.

Moreover, the career path is not smooth sailing and you will need a hearty dose of stubbornness to deal with the frequent frustrations that will arise along the way. Other rudimentary traits include attention to detail, creativity, and the ability to remain focused.

Pick an Area of Focus

There are many roles in data science that one could go into, plus there are numerous opportunities for specialization. Depending on an individual’s work experience and/or academic background, getting into one job would be easier than other roles. For example, it will be easier for a software developer to shift into data engineering than working as a business intelligence analyst. Therefore, until and unless one clearly understands what they want to become, their career path will be mottled with confusion. 

Personal evaluation is a good start point when picking an area of focus. This means a person should figure out what he/she is good at and what he/she wants out of their career. Taking mentorship from people in the industry can also help figure out which role is best for people with particular academic background and experience.

Choose an Academic Path

There is no doubt, the paths to a career in data science are many, but for all intents and purposes, it is impossible to start a career in this field without a strong educational background. At the very least, you will need a degree, or a high-level degree for that matter, to launch a career as a data scientist. In fact, more than 42% of data science jobs require candidates to have a graduate degree (IBM, 2017).

Interested individuals are obliging, and according to a study from Burtch Works, today, 6% of data scientists hold a bachelor’s degree. Besides, 47% hold a master’s degree, and another 47% hold a doctorate degree (Burtch, 2019).

You can earn the degree via the normal route—taking a full-time bachelor’s degree course. Additionally, you can gain or supplement your knowledge of data science by enrolling for an internet-based massive open online course (MOOCs). At the end of the degree, you will gain a wide range of basic data science skills, including coding, experimentation, handling of big data, quantitative problem solving, and more.

Source: Burtch Works

Obtain an Entry-Level Job

In today’s competitive labor markets, organizations are on the lookout for fresh talent. Fortunately, with only a bachelor’s degree in a relevant field, it is easy to get an entry-level data science job, such as junior data scientist or junior data analyst. With a bachelor’s degree in data science or a relevant field, you can take jobs such as a market research analyst, management analyst, or data visualization specialist.

On the other hand, people with a master’s degree can focus on more specific jobs such as data engineer, machine learning algorithm developers, or statisticians. Even more, upon completion of a doctorate degree, data scientists qualify to hold higher roles like an enterprise science analytics manager or business solution scientists.

Get a Certification

It is possible to acquire all the skills needed to get a job as a data scientist straight out of college. But continuing education and earning a certification is a hedge against shifts in the ever-evolving career market. Endeavor to complete relevant certifications such as EMC: data science associate, Cloudera certified professional: data scientist, and SAS certificate predictive modeler using SAS Enterprise Miner 7. Constant reskilling and upskilling chart a course for significant career opportunities and lucrative compensation.

Never Stop Learning

Technologies change rapidly. This reality requires individuals in any field, data science included, to continuously learn and master new skills that will enable them to prepare for the not-yet-arrived jobs of the future. For this reason, one should continue to look for professional development and educational opportunities to keep pace with the rapidly changing industry.

Required Career Background

According to a study from IBM, data scientists is one of the fastest-growing jobs and is expected to see a demand spike of 28% by 2020 (IBM, 2017). This simply means more than three million new jobs in the data science field will be up for grabs in 2020. To make the cut and work in data science, one has to meet the bare minimum requirements. In other words, to qualify for a role in data science, a person should have the perfect blend of academic qualification, experience, programming/coding skills, hard skills, and soft skills.

Academic Requirements

As previously mentioned, data scientists require a sound experiential and educational background to accomplish complex analytical and planning tasks in real-time. The academic trajectory to a data science role, however, is not clearly defined and neither is it universal. There are multiple jobs in this field as you will find out in the next section and some roles call for specific academic qualifications.

Interestingly though, data science jobs require a bachelor’s degree in data science or related technical field. Common bachelor’s degrees that will help you land a job in data science include:

  • Statistics
  • Data science
  • Computer science
  • Mathematics
  • Economics
  • Natural science
  • Physics
  • Social science
  • Operational research, or
  • Another quantitative field

In 2019, the distribution of data science professionals in the US by area of study was as follows: mathematics/statistics (25%), computer science (21%), engineering (18%), natural science (16%), business (12%), social science (5%), and economics (4%) (Butch Works, 2019).


A bachelor’s or high-level degree gives data scientists the basic skills they require to get their career up and running. But, to gain an edge and stand out from the competition, certification is key. Data scientist certifications allow individuals to develop relevant skills not gained during college education.

Besides, they validate people’s skills and knowledge, enabling them to get industry credibility. In this way, they help hiring managers or recruiters precisely understand what they are getting if they hire a candidate. According to a study by Microsoft, 91% of hiring managers consider certification as part of their hiring criteria (Microsoft, 2012).

Some of the top data science certifications include:

  • Cloudera Certified Associate: Data Analyst. Valid for two years, the certification demonstrates foundational knowledge as a data analyst, developer, and administrator of Cloudera’s software. The certification is offered online by Cloudera and costs and upward of $295 per exam specialty and per attempt.
  • Cloudera Certified Professional: CCP Data Engineer. Open-source developers who attain this certification can perform core competencies required to transform, store, ingest, and analyze data in Cloudera’s CDH environment. The certification, which is earned online, is valid for three years and costs $400 per attempt (three exams).
  • Certified Analytics Professional (CAP). A vendor-neutral certification offered to help data professionals transform complex data into actionable insights and actions. Exams are done in computer-based testing centers around the world and cost $495 for INFORMS members and $695 for non-members, plus there is customized pricing for teams and organizations.
  • Dell EMC Data Science Track. Entails two programs—Data Science Specialist (DCS-DS) and the Data Science Associate v2 (DCS-DS) certification—which can be completed online or in person. The certification does not expire and costs $230 per examination.
  • Google Certified Professional Data Engineer. The certification helps professionals demonstrate their ability to design data processing systems, build and operationalize data systems, and implement machine learning models. The exam is offered in two languages (English and Japanese), costs $200, and takes two hours to complete.
  • Google Data and Machine Learning. The certification enables data professionals to implement foundation data engineering, machine learning, and artificial intelligence tasks in the Google Cloud Platform. It costs $200 and the credentials do not expire.
  • IBM Data Science Professional Certificate. Helps data professionals hone their skills in data science methodology, open-source tools, databases, programming languages, and machine learning. The courses are offered online and cost $39 per month for a subscription on Coursera.
  • SAS Certified Big Data Professional: A global certification program for data professionals who want to validate their ability to use SAS Data Management tools to prepare data for statistical analysis. The program, which is completed online, costs $180 per exam and the credentials earned do not expire.
  • SAS Certified Data Scientist. Demonstrates the data professional’s ability to manipulate and mine insights from data using a variety of SAS and open tools and make vital recommendations using complex machine learning models. The program cost an upward of $4,400 and the credentials earned do not expire.
  • Open Certified Data Scientist (Open CDS). Offered on-site by The Open Group, the certification validates the qualities and abilities required to perform effective data analysis for the overall improvement of a business. The provider offers customized pricing, which is available upon request. The good news is that credentials earned do not expire.

Recommended Programming Skills/Courses

In addition to academic qualification, people looking for a career in data science need to demonstrate proficiency in programming and coding. To gain a real-world programming ability, would-be data scientists should think about mastering the top programming languages.

According to a worldwide survey by Kaggle (An open platform for data scientists and machine learning professionals), 83% of data scientists use Python on a regular basis. Other programming languages that data scientists use regularly include SQL (44%), R (36%), C/C++ (23%), Java (21%), Javascript (17%), and Bash (14%) (Kaggle, 2018). The same survey asked data scientists as to which programming they would recommend to aspiring data scientists to learn first. A whopping 75% recommended Python, R (12%), SQL (5%), C++ (2%), and MATLAB (1%).

Source: Kaggle

It is to be noted that, although programming skills are essential in data science, the level of required proficiency will vary depending on the role. For example, while data analysts need to have intermediate proficiency in Python and R, data scientists require advanced skills in these programming languages. The demand for programming skills may vary based on the data software used by companies. For example, companies that use BI tools such as Power BI and Tableau may prefer their data scientists to be highly proficient in SQL (365 DataScience, 2020).

Where to Study

First of all, to become a data scientist, a degree is the bare minimum. As such, data science will definitely begin in college and the area of study will be predetermined by the role you want to assume. Where you study your bachelor’s, Master’s, or Doctorate degree is your own discretion. For starters, for people taking bachelor’s degrees, there is an option to enroll for a full-time or part-time course or online degree programs.  

Secondly, as we mentioned earlier, data scientists need to have enough basics in programming and coding. This means they ought to master data science-related programming languages. Again, to pick up the necessary skills, one can enroll for a full-time course or work with tutors using elearning technology.

Soft Skills

Data science is not all about mathematical, statistical, and technical work. The nature of activities and tasks in this field also requires an individual to have a knack for imagination, creativity, and vision. These skills enable data science professionals to diligently explore business problems at hand and find answers to questions.

Needless to say, hard skills (machine learning, data mining, deep learning, and others) are of significant importance. Unfortunately, it is impossible to forge a concrete career foundation based on these skills alone. For this reason, people who want to succeed in data science should seek to gain a firm grasp on the key data-science-related software skills.

Some of the top soft skills for data scientists include:

  • Effective communication
  • Intellectual curiosity
  • Storytelling
  • Business acumen
  • Critical thinking
  • Organizational skills
  • Adaptability
  • Team player
  • Product understanding
  • Proactive problem solving

Data Science Careers

Searching for the right job to pursue in data science can get complicated quickly for two main reasons:

  1. There are a plethora of widely-accepted job titles in data science that might be hard to find when searching for “data scientist” or  “data analyst” roles.
  2. There is no universal definition of the term “data analyst” or ‘data scientist” agreed upon by companies across the globe. For this reason, data science jobs with the same title may require completely different skill sets.

This section aims to take a look at some of the major job titles in data science, their descriptions, skills needed, and average annual salary. The goal is to help job seekers probe deeper and explore all potential options in their data science job journey:

Note: The average salary data presented below is culled from PayScale.com for each position, based on US data for employees with less than one year of experience, as of June 2020. The data represent tentative guidelines and may vary based on company, location, and the applicant skillset.

The Big Three Data Science Jobs: Data Analyst, Data Scientist, and Data Engineer

The data science universe is diverse and for this reason, there are numerous job titles that might be used by organizations. Obviously, it is not possible to talk about all of these jobs, but we can mention the major roles namely, Data analyst, data scientist, and data engineer.

Data Analyst

Average entry-level salary: $54,990

Job description: As the name suggests, data analysts harness company data and analyze it to answer business questions. They derive helpful insights from available data and communicate them to stakeholders and other teams to be acted upon. Instead of finding interesting trends and patterns on their own, data analysts are given specific business questions to answer.

Required skills:

  • Data cleaning
  • Probability and statistics
  • Data visualization
  • Intermediate programming in R or Python.
  • Intermediate SQL queries expertise
  • Ability to communicate complex analytical results to people with no programming or statistics background.

Career prospect: Data analyst is considered an entry-level job. Hence, it acts as a gateway career to a wide variety of positions. In this regard, data analysts have a fairly open-ended career that can take multiple directions.

First, people who hone their skills and decided to stick with data analysis have the opportunity to work as senior data analysts or hold management roles in larger companies. Second, data analysts who acquire advanced data science skills such as machine learning progress towards a career as data scientists. Lastly, data analysts with an innate passion for data infrastructure, data pipeline development, and software development can ultimately work as data engineers if they gain the relevant skills.

Data Scientist

Average entry-level salary: $85,711

Job description: This role was the gist of this post and it’s clearly defined in one of the sections above. But for purposes of clarification, data scientists are typically data analysts who can build machine learning models and algorithms, and predict the future of a business based on available data.

Unlike data analysts, data scientists have unfettered authority to implement their own experiments and ideas to unearth trends and patterns in data. They combine the skills of data analysts, infographic designers, software programmers, and storyteller (Mayer-Schönberger, & Cukier, 2013).

Required skills:

  • All data analyst skills, plus
  • Sound understanding of machine learning methods
  • Advanced programming skills in R or Python
  • Familiarity with other data-science-related tools such as Apache Spark.
  • A solid understanding of statistics and the ability to analyze statistical models.

Career prospect: Data scientist is the ultimate role in the data science field. As such, people working in this career might not have plenty of options to deviate from it. Interestingly, data scientists have options to advance their career in this line and earn a pay rise.

People who amass valuable experience in this field may work as senior data scientists. Plus, those that opt to specialize further in machine learning may get an opportunity to work as machine learning engineers. Even better, data scientists who climb the career ladder to the top and acquire relevant management skills may work as lead data scientists or chief data officers.

Data Engineer

Average entry-level salary: $77,361

Job description: The roles and skills of data engineers and data scientists overlap significantly; however, the crux of their difference is the specific area of focus of each. Data engineers are primarily focused on developing data infrastructure or architecture. They create, test, and maintain data architectures, such as large databases and processing systems for data scientists (Firouzi, Chakrabarty, Nassif, 2020). In essence, the role of data engineers entails a lot more programming and software development and requires a lot less statistical analysis.

Required skills:

  • Advanced programming skills
  • Advanced SQL skills

Career prospect: Like data scientists, data engineers have limited options to pursue a different career path in the data science field. With continued experience and specialization, however, data engineers can transition into senior engineering roles. They can sharpen their skills to move into a variety of software development specialties. Moreover, outside specialization, data engineers can be groomed as leaders of data or engineering teams.

Source: PayScale.com

Other Notable Data Science Jobs

Quantitative Analyst

Average entry-level salary: $84,317

Job description: Quantitative analysts, popularly known as “quants” leverage their advanced statistical analysis skills to find answers to business questions and make accurate finance and risk predictions. Put simply, quants evaluate the relationship between various economic factors and the company’s returns, to inform sound decision making (Kissell, 2013).

Required Skills

  • A solid understanding of statistics.
  • A sound understanding of machine learning models and their application in solving financial problems and predicting markets.

Machine Learning Engineer

Average entry-level salary: $93,449

Job description: Machine learning engineers collaborate with data engineers, data scientists, and developers to build innovative programs that enable machines to work without direct human assistance. Their roles overlap with that of data scientists and in some companies, machine learning engineers are data scientists with advanced skills in machine learning.

Required skills:

  • A high level of proficiency in data science
  • A solid understanding of statistical models
  • Well-versed in software engineering.

Business Intelligence Analyst

Average entry-level salary: $58,660

Job description: A business intelligence analyst is an experienced data analyst who specializes in analyzing operational data and market trends to help organizations make sound decisions. In other words, business intelligence analysts dig actionable insights from data and communicate them to stakeholders to smoothen out the decision-making process.

Required skills:

  • Data-science-related programming skills
  • Familiarity with data analysis tools such as Microsoft Power BI

In addition to the above job titles, the data science field has plenty of other positions to offer, including marketing analysts, statisticians, data warehouse architects, business analysts, systems analysts, marketing analysts, and operational analysts.

Build a Career in the Data Science Field

There is no doubt, the demand for data science professionals will continue to increase (IBM, 2017). As new job openings crop up in the data science field, people who will acquire the right qualifications will enjoy a bright career outlook. For people who are already working in the field, there will be opportunities to take up higher roles and earn lucrative compensations. And for those that are training to join the field, the arena will be overflowing with data-science-related jobs. 

That being said, data science has not fully evolved and a lot is poised to change as experts continue to carve the discipline. Further, with technological advancement, the future of data science looks bright and it is mesmerizing to behold. But, professionals have nothing to celebrate. This is because, as things change and data science becomes clearly defined, the skills, academic qualifications, and personal traits will also be tweaked. 

Besides, new programming languages will come up, while others will become obsolete. In a similar fashion new job titles will most probably surface and the requirements for some roles will change significantly. To be on the safe side, aspiring data scientists and those who are already in the field will have to stay abreast of every development. Most importantly, there is a need for constant upskilling and reskilling to create a well-rounded curriculum vitae that will make the cut in spite of the projected changes.


  1. Burtch, L. (2019). The Burtch Works Study: Salaries of Data Scientists and Predictive Analytics Professionals. Retrieved from https://www.burtchworks.com/wp-content/uploads/2019/06/Burtch-Works-Study_DS-PAP-2019.pdf
  2. Chinnamgari, S. K. (2019). R Machine Learning Projects: Implement Supervised, Unsupervised, and Reinforcement Learning Techniques Using R 3.5. Packt Publishing Ltd, pp.11.
  3. Chojecki, p. (2020). Data Science Job: How to Become a Data Scientist.
  4. Computer History. (2020). Timeline of Computer History. Retrieved from https://www.computerhistory.org/timeline/computers/
  5. 356 DataScience. (2020). How to Become a Data Scientist in 2020 – Top Skills, Education, and Experience. Retrieved from https://365datascience.com/become-data-scientist-2020/
  6. Firouzi, F., Chakrabarty, K., & Nassif, S. (2020). Intelligent Internet of Things: From Device to Fog and Cloud, Springer, pp. 246.
  7. Fontana, V., Barga, R., & Tok, W. H. (2015). Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition, Apress, pp.3.
  8. Harris, H. D., Murphy, S. P., & Vaisman, M. (2013). Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work, O’Reilly. Retrieved from http://www.stat.wvu.edu/~jharner/courses/dsci503/docs/Analyzing_the_Analyzers.pdf
  9. IBM. (2017). The Quant Crunch: How The Demand For Data Science Skills is Disrupting The Job Market. Retrieved from https://www.ibm.com/downloads/cas/3RL3VXGA
  10. Kaggle. (2018). 2018 Machine Learning and Data Science Survey. Retrieved from https://www.kaggle.com/kaggle/kaggle-survey-2018
  11. Kissell, R. L. (2013). The science of Algorithmic Trading and Portfolio Management, Academic Press, pp. 27.
  12. Linkedin. (2018, August). LinkedIn Workforce Report: United States. Retrieved from https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018
  13. Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think, pp. 125.
  14. Microsoft. (2012). Microsoft Certification Program Satisfaction Study April 2012. Retrieved from https://mindhubpro.pearsonvue.com/v/vspfiles/documents/Provusion_VOC_Microsoft.pdf
  15. National Academies Press. (2018). Data Science for Undergraduates: Opportunities and options (2018). Retrieved from https://www.nap.edu/read/25104/chapter/4 
  16. Naur, P. (1974). Concise Survey of Computer Methods.
  17. O’Neil, C., & Schutt, R. (2013). Doing Data Science: Straight Talk from the Frontline, O’Reilly Media.
  18. Sarkar, D., Bali, R., Sharma, T. (2017). Practical Machine Learning with Python: A problem Solver’s Guide to Building Real-World Intelligent Systems, Apress, pp.16.
  19. Tukey, J. W. (1962). The Future of Data Analysis, vol 33.
  20. The University of Wisconsin. (2017, March 22). A Modern History of Data Science. Retrieved from https://datasciencedegree.wisconsin.edu/blog/history-of-data-science/#:~:text=A%20trip%20into%20the%20history,the%20one%20that%20Tukey%20imagined
  21. Violino, B. (2019). 6 Ways to Deal With the Great Data Scientist Shortage. Retrieved from https://www.cio.com/article/3397137/6-ways-to-deal-with-the-great-data-scientist-shortage.html
  22. Voulgaris, Z. (2014). Data Scientist: The Definitive Guide to Becoming a Data Scientist, Technics Publications.

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