The Variety of Domains Covered in the Seminar

The Variety of Domains Covered in the Seminar Understanding the different fields and technologies related to the practice of data science is essential for any professional. It allows him to appreciate better the skill set needed to thrive in the ever-growing yet competitive field. In line with this, this article will focus on analyzing the various impacts of data science in the various domains discussed in the lectures. I would also like to discuss some commonalities between the different data science fields. Overall, I believe that an in-depth understanding of these various themes and issues is essential for the overall development of data science professionals like me. 
Part I
Experience Summary
           Data science is one of the ever-growing fields that are essential for almost every field out there. With the help of data science, collection, synthesis, analysis, and even presentation of data becomes very efficient and usable in various case use. Primarily, my experience in the lectures made me realize the following themes; (1) that data science is a very diverse yet holistic field, and (2) that such a field would be essential to survive and thrive in a competitive landscape in the near future, (3) that it most successful case uses are those which know how to combine data with the “human side” of its application, (4) that data science has a long way to go which presents a huge opportunity for data scientists, and (5) that data scientists have a huge responsibility in society. 
Diversity and Holism of Data Science
           As said earlier, one of the things that I learned was that data science is a very diverse yet holistic field. On the one hand, the field requires various integrated fields like data mining, data visualization, machine learning engineering, and marketing data analysis, to name a few. Although all of these fields are somehow integrated, each of them also requires different skillsets that any professional should be able to combine in his professional practice efficiently. For example, one memorable experience I had in the lectures was the computational advertising discussion, which combines marketing and data science. As I have mentioned in my previous discussions, the field itself combines economics, computing, and machine learning, to name a few. 
Another notable example that left a deep imprint in my mind was using deep learning technologies in the enterprise market, mainly through query interpretation. Although query interpretation is primarily used for schema and indexing processes, skilled data scientists know that even these seemingly “technical processes” aim to improve user experience and conversion rates.  
           In other words, one of the main themes I realized in this practice is that while data science requires a diverse skill set, it nonetheless requires holistic thinking. Its end goal is even to predict the future by matching products with potential customer needs. 
           
The necessity of Data Science for Future Case Use
           Another essential theme that I realized throughout the lectures was that data science is and will be necessary to survive in the future. As early as the beginning of the lecture series, I already found out that the global data science market is already valued at “valued at $95.3 billion in 2021 and is expected to expand at a compound annual growth of 27.7%, reaching a $322.9 billion valuation in 2026” (ReportLinker). Although I was surprised by the sheer amount of market value for this field, I did not realize how important it is in the first place. Nonetheless, as we progressed throughout the lectures, I realized that all companies these days utilize the data science to survive in the severely competitive market. I learned that data science is used to predict hidden trends, automate processes, increase marketing effectiveness and conversion rates, and even predict future consumer trends to outmatch your competitors (Stoudt et al.). 
           In other words, without the help of data science, businesses (and organizations) are at a higher risk of failing to streamline their processes to meet consumer demand, outpace their competitors, and ultimately generate the revenues necessary to survive today's highly competitive market. 
Data Science is More than a Technical Field
           As I have said earlier, data science is more than just a technical field of study. Before taking the course, I imagined the practice primarily dealing with collecting data, synthesizing it, generating algorithms to match case use, and presenting them in a visual form. In other words, I initially viewed the field and data scientists as people who were mainly concerned with the technical side of programming. 
           However, as we proceeded in the lecture series, I realized that a number of the professionals that discussed their cases also know the “human side of the business.” As aptly said by Aragon (2022), a professor of engineering at the University of Washington, “the human side of data science can no longer be an afterthought." She also discussed that the practice of data science these days is made through a collaboration of various professionals, with the most prominent companies hiring social scientists and ethicists, to name a few. 
           Going back to the lecture, one of the things that piqued my attention was the lecture on BPOs (Business Process Outsourcing) companies and how companies are hiring these services to help find "actionable insights." Data science can help collect, filter, and analyze real-world data into “digestible chunks.” Still, without the help of individuals who know how to create metrics that combine objective data and the human condition, any actional information would not be as practical as it should be. 
Data Science has a Long-way to Go.
           Today's data science practice is way more advanced than a decade ago. It improved not only in terms of technology but also in terms of social, emotional, and psychological aspects. This is exemplified by integrating technology and social case use in almost every aspect of this field. Yet, the lectures taught me that this field is still far from maturity.
           Take, for example; some fields considered inherent offsprings of data science, such as Deep learning, Machine learning, and Artificial Intelligence (AI). Indeed, most of these fields have advanced years ahead compared to decades ago. One of the cases uses that were discussed before was the use of machine learning to utilize past online actions to predict future consumer trends. I realized that this was why most of the targeted ads I offered on digital platforms aligned with my previous interests and desires. Yet, studies and reports have shown that machine learning has a long way to go, primarily due to challenges in time and resources, accessibility, and rigid business models, to name a few.
Another exciting challenge I realized throughout the lectures was the vast opportunity to develop AI technologies in the future. One of the lectures throughout the series noted that data scientists are continuously being hired and employed to apply AI technologies to machine learning and vice versa. Yet, AI technology is still far from what we want it to be. Today, one of the most significant drawbacks of this kind of technology is the lack of essential human characteristics like emotions, common sense, and instincts (Gupta). These challenges are the ones that data scientists are other programmers would have to address to make a genuinely functioning AI. And consequently, this provides a massive opportunity for future scientists like myself who hope to delve deeper into this kind of technology. 
Part II
Commonalities and Differences
           As said earlier, data science is a diverse but holistic field. It requires diverse knowledge and skills while also needing the ability to piece them into one coherent whole. Nonetheless, throughout the lecture, I realized several commonalities between these problems that are experienced by data scientists, which include (1) making sense of data, ()(). In the following sections, I will discuss these areas in detail. 
Making Sense of Data
           One of the main commonalities in data science problems that cut across various domains is "making sense of data." Many data science-related skills require collecting, analyzing, and synthesis of voluminous data into something easily understandable and actionable. I realized this further as I went through the discussion. 
           One instance discussed in the lectures was using data science to collate vast raw amounts of data in making critical decisions in business process services. In that discussion, the lecturers discussed how critical this process is because it allowed scholars and authorities to validate the prevalence growth of obesity and diabetes in all 3,100 U.S. counties from 2004 to 2013. Besides its use in policy-making decisions, data science problems are also inherently used in the business sector. For example, it was shown that machine learning models created by data scient...