IST 8101 Literature Review and Proposal on Data Scientist Role

Literature Review

 Data science is about turning data into actions. That can be accomplished via the creation of data products that offer actionable information minus exposure of the decision makers to the analytics or the underlying data. The data scientist should extract timely and actionable information from various sources of data so as to drive the data products (Davenport & Kim, 2013). Some of the examples of data products could include answers to the questions like, “Which promotion criteria can I leverage so as to attract more customers? How can I reduce the expenses while increasing profits? Which products should I advertise more so as to increase profit? What changes in the manufacturing process can help to build a better product?” One cannot answer these questions minus understand the data that he/she has at their disposal and what that data tells them.

The Background of Data Science

The term “Data Science” first appeared in the literature about computer science in the years between 1960 and 1980. Although it appeared that early, it was until the late 1980s that data science practice began to take ground in the statistics and data mining realms.  At first, data science was taken to be an independent discipline in the year 2001 (Cleveland, 2001). From that time onwards, countless articles have been advancing the discipline resulting in the declaration of the Data Scientist as the sexiest job in the 21 century (Scientist, 2012).  Data science fosters and encourages the shift between the deductive and inductive reasoning. That is an indispensable transition from the traditional analytics approaches. As a matter of fact to discover the insights that are the foundation for Data Science, there must be a tradecraft and an association or interplay between the inductive and deductive reasoning (Swan & Brown, 2008).

What Makes Data Science be Different?

Data Science combines the ability to reason inductively and deductively thereby creating an environment where the real models are no longer are empirically or statically based (Loukides, 2011). They rather have to be constantly tested, improved and updated until we find better models.  The distinction between the traditional analytic approaches and Data Science does end with the transition between deductive and inductive reasoning.  It should be clearly understood that Data Science does not replace business intelligence within an enterprise. Data Science and business intelligence function within an organization are complementary and additive because each one provides a necessary view of the business operations as well as the operating environment (Provost & Fawcett, 2013). For instance, while business intelligence just asks questions, Data Science works on discovering the question that should be asked. Also, while Data Science is concerned with discovering the actionable information from a set of data, business intelligence is only focused on reporting the historical facts.

The Role of Data Scientists and the Impact of Data Science

As we shift to the data economy, the Data Science field is the competitive advantage for the companies that are interested in achieving high performance and winning in the competition (Davenport & Patil, 2012). This advantage is defined through the improvement in decision-making. It should be clear that whether the information is available or not, organizations must make decisions.  The way companies are making decisions has been changing for half a century.  Before business intelligence was introduced, organizational decision makers just made decisions by gut instinct, best argument, or by loudest voice (Few, 2009). It is sad to say that the method still exists even today in many organizations whereby in some it is even a predominant method by which those organizations act.

Fortunately for our economy, many companies started to inform their decisions using real information via the application of simple statistics. The ones that did it well became successful whereas the ones that did not do it well they failed.  We should be aware that we are outgrowing the capability of simple stats to meet the ever growing market demands (Swan & Brown, 2008). That is where the data scientists come in. The businesses are having large volumes of data that exists in many formats and is spread across several systems in such a way that if simple stats were used, they could not help an organization to adequately meet the needs of its clients (Zikopoulos et al., 2012). The data scientists are needed so as to help companies maintain competitiveness. The data scientist can skillfully make maximum use of then available data to make the organization to succeed (Loukides, 2011). They do by integrating talent, tools, and techniques since Data Science is a sophisticated field.

Proposal

I n this research the researcher intends to carry out an investigation with the aim of understanding and elaborating the role of a data scientist as well as getting the necessary training so as to become a competent data scientist.  The research will go through some iterations with a continuous learning and improvement until the researcher achieves the objectives of the research. There will be a transition from the first iteration to the last iteration whereby the output of one iteration will be used as input for the subsequent iteration.  In the following section, there is a brief summary of the four iterations through which the research project will proceed.  The iterations are also depicted in the diagram as below.

Iteration 1:  Brainstorming

The Brainstorming session will be the first iteration in this research, and it will entail the introduction to the data scientist and the need for them in the business world. The researcher intends to meet some experts and consult with the data scientists, and through the same, he will be able to understand them better and why they are in the requirement in the business world today. 

Iteration 2: Gathering Requirements

During this iteration, the researcher intends to find out the skill set and the qualifications for one to become a competent data scientist. The researcher will use various resources including the journals, books, magazines, and the web to determine these requirements. These will help him to prepare on how to acquire those skills adequately.

Iteration 3: Training

In this Iteration, the researcher will get training using a video tutorial from YouTube and the aim of the training will so as to acquire the skills required to become a competent data scientist.  The training from the video will also be accompanied with some consultation from resource persons so as to clarify some of the technical areas. It will also be accompanied by some assessment test so as to determine how much the researcher has been able to comprehend.

Iteration 4: Practicing the Data Scientist Role and Reflecting on the Research

During this last iteration, the researcher will request one of the renowned companies to give him a chance to practice the things he will have learned from the previous iterations. The researcher will then evaluate what he will have learned throughout the research work and make any suggestions for improvement in the future.

 

References

Cleveland, W. S. (2001). Data science: an action plan for expanding the technical areas of the field of statistics. International statistical review, 69(1), 21-26.

Davenport, T. H., & Kim, J. (2013). Keeping up with the quants: Your guide to understanding and using analytics. Harvard Business Review Press.

Davenport, T. H., & Patil, D. J. (2012). Data scientist. Harvard business review, 90, 70-76.

Few, S. (2009). Now you see it: simple visualization techniques for quantitative analysis. Analytics Press.

Loukides, M. (2011). What is data science?. " O'Reilly Media, Inc.".

Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".

Scientist, D. (2012). The Sexiest Job of the 21st Century. Harvard Business Review Magazine: 

Swan, A., & Brown, S. (2008). The skills, role and career structure of data scientists and curators: An assessment of current practice and future needs. Report to the JISC. Truro: Key Perspectives Ltd. Retrieved, 26(2), 2013.

Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012). Harness the power of big data The IBM big data platform. McGraw Hill Professional.

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in legit research paper writing services if you need a similar paper you can place your order for research essay writing services.