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14 pages/β‰ˆ3850 words
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Style:
MLA
Subject:
IT & Computer Science
Type:
Coursework
Language:
English (U.S.)
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Topic:

The Variety of Domains Covered in the Seminar

Coursework Instructions:

For your final report for this class, please review your notes over all the lectures to date and write a 7-10 page report that addresses the following questions:
a) Summarize your experience over all of these lectures and describe how data science impacts the variety of domains covered in the seminar. (60 of the 300 total points)
b) Carefully, draw out the commonalities and differences of data science problems with respect to the various domains covered in the lectures. Please be specific and cite the lectures when you describe these commonalities and differences. (60 of the 300 total points)
c) Comment on ideas introduced in the cross-cutting theme lecture i.e the. lecture on trust, privacy, ethics and legal aspects and its potential impact on how it might influence design and development of data science pipelines for an organization in any of the domains covered in the other lectures. (60 of the 300 total points)
d) Pick 3 case studies from different lectures (try to choose case studies you have not used in previous assignments) and write in some detail about each bringing out the data science methodologies and impact for the application. The description of the use cases should provide the reader with enough detail to understand the application and the technical solution proposed. (60 of the 300 total points)
e) Describe a potential use case of your choice (outside of those described in the lectures) and discuss the data that might be available for the use case, any preprocessing that you might need to do to the data, the goal of the application, and what potential data science techniques might be relevant for that use case and how you might use them. (60 of the 300 total points)
I expect you to list the question before the corresponding answer. Do not cut and paste responses from your previous assignment submissions for this report.
This final term report is worth 300 points and counts for 20% of the course grade. The rubric remains the same: 80% Content and Responsiveness, 15% Comprehension and 5% Style.

Coursework Sample Content Preview:
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...
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