Introduction to Data Science

Executive Summary of Introduction to Data Science

Data Science has arisen as an emerging discipline out of the increased scope of Information Technology. The radically grown Data Science domain has been lucrative to almost all the sectors of businesses. It has been a critical field of work and hence required Data Scientists as professionals of the field for the knowledgeable growth and development of it.

However, Data Scientists have also witnessed numerous major challenges in performance. The study has highlighted some of the major issues and challenges faced by the Data Scientists along with the required skills as solutions to these challenges.


Executive Summary..


Impact of Data Science on Daily Businesses.

Roles and Responsibilities of Data Scientists.

Challenges for Data Scientists. 

Data Relevance and Usability.

Data Quality Enhancement

Technical Complexities.

Ethical Issues.

Data Storage.


Educational Skills.

Programming Skills.

Domain Expertise.

Business Awareness.

Soft Skills.


Introduction to Introduction to Data Science

The current world is surrounded by data-driven applications and technologies which can be viewed as a composition of branches of knowledge and applications such as statistics, data mining, machine learning, databases, and various such applications (Van Der Aalst, 2016).

Data science can be defined as an integrative or multifaceted field of knowledge that utilizes scientific applications and mathematical algorithms to extract the information out of the available structured and unstructured data to obtain the artifacts with even more increased knowledge value.

Impact of Data Science on Daily Businesses

Data science is not a certain domain or realm but constitutes multiple pieces of it in the form of data mining, big data, deep learning, internet of things (IoT), etc and the combination of these pieces creates a scope in performance of routine activities such as in entertainment, banking services, hospitality, education, eCommerce, digital marketing and many other industries. Data is an all-round thing as it can be collected on everything at any time from any place (Van Der Aalst, 2017).

  • From information to knowledge, and finally, into practice, data science has made its way towards impact in the medical and health industry (Sim, 2016) with its involvement in laboratories, tests, diagnosis, medical imaging, bio-banks management, etc (Zhang et al., 2017).
  • The advent of data science in the eCommerce field has made its impact positively by providing transformational services such as making a call to action process, tracking user behaviour (Jao, 2013), increasing conversion rates, after-sales monitoring, etc. (Miller, 2013).

Roles and Responsibilities of Data Scientists

In the era of digitalized businesses, the creation of knowledgeable insights out of the raw, unstructured data is a critical job that requires the intervention of high skilled Data Scientists (Kim, 2016). Following are the roles and responsibilities of Data Scientist:

  • Development of database architectures and processing system
  • Ensure the derived architecture meets the expected architecture.
  • Search for data acquisition opportunities.
  • Develop a process for data modelling, data mining, etc.
  • Improve data reliability, quality, and efficiency.

Challenges for Data Scientists

Data Relevance and Usability

One of the most challenging part for Data Scientist is to identify, specify, quantify the value and utility of domain-specific data from the view of the requirements (Cao, 2017).

Data Quality Enhancement

Driving data quality issues such as noise, missing values, uncertainties, etc.

Technical Complexities

Data Scientists have to confront situations such as inaccessibility of data, elimination of data bias, data privacy concerns, and whatnot.

Ethical Issues

As per Saltz, (2019) the lack or hindrance in the data privacy, insufficient mitigation of malicious attacks, lack of transparency can put the data scientists in a challenging face.

Data Storage

It includes certain challenges like data security, UI and accessibility, and compatibility.

Conclusion on Introduction to Data Science

In the era of an informational explosion, data science contributes to a great extent as the drivers of next-generation information innovation. The field of data science has been positioned in a highly evolving data world that seamlessly connects to our daily life.

However, to overcome the above-discussed challenges, the field of data science requires professionals with the following skills to minimize the risks in the data science industry.

Educational Skills

Data science is all about the knowledge of mathematical and statistical data commands. The Data Scientist must be skilled in regression, time-series, multivariate calculus, linear algebra, etc. However, these skills work for a data scientist in addition to programming skills.

Programming Skills

Programming languages such as R, Python, and so on are useful for most of the statistical solutions in data science. Thus, scaling oneself in such areas is a boon for a data scientist.

Domain Expertise

A data scientist has to coherently engage in work with the technical and the non-technical teams. Hence the requirement of domain expertise is vital for a data scientist to understand the problem statement as well as the technical infeasibility if occurs.

Business Awareness

Data cleaning and getting insights from it is irrelevant if the business problem is not identified by the data scientists. A high skilled data scientist would easily rectify the business issues proficiently.

Soft Skills

Technical and domain skills are the basic elements of a job but the communication and soft skills are the bridges of the interdepartmental communication and development.

References for Introduction to Data Science

Cao, L. (2017). Data science: A comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 1-42.

Jao, J. (2013). Why big data is a must in eCommerce. Retrieved from:

Kim, M., DeLine, R. & Begel, A. (2016). The emerging role of data scientists on software development teams. IEEE/ACM 38th International Conference on Software Engineering (ICSE), 96-107.

Miller, G. (2013). Six ways to use big data: T increase operating margins by 60%. Retrieved from:

Saltz, J. S. & Dewar, N. (2019). Data science ethical considerations: A systematic literature review and proposed project framework. Ethics and Information Technology, 21(3), 197-208.

Sim, I. (2016). Two ways of knowing: Big data and evidence-based medicine. Annals of Internal Medicine, 164(8), 562-563.

Van Der Aalst, W. (2016). Data science in action. In Process Mining, 3-23, Springer, Berlin, Heidelberg.

Van Der Aalst, W. M., Bichler, M. & Heinzl, A.(2017). Responsible data science. Business & Information Systems Engineering, 59, 311-313.

Zhang, X., Perez-Stable, E. J., Bourne, P. E., Peprah, E., Duru, O. K., Breen, N., Berrigan, D., Wood, F., Jackson, J. S., Wong, D. W. S. & Denny, J. (2017). Big data science: Opportunities and challenges to address minority health and health disparities in the 21st century. Ethnicity & Disease, 27(2), 95-106.

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