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5 pages/≈1375 words
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MLA
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IT & Computer Science
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Coursework
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English (U.S.)
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Topic:

Data Science is a Core Pillar in this Subspace

Coursework Instructions:

prepare a short (~2-3 pages, 12 point single space) report that addresses as many of the following questions as are relevant:
• Describe the market sector or sub-space covered in this lecture.
• What data science related skills and technologies are commonly used in this sector?
• How are data and computing related methods used in typical workflows in this sector? Illustrate with an example.
• What are the data science related challenges one might encounter in this domain?
• What do you find interesting about the nature of data science opportunities in this
domain?
In addition,
(i) Please discuss how sellers and buyers may need different data features in an e-commerce platform such as e-Bay. (10 pts of the 80 C+R points in the rubric) )
(ii) Describe briefly the algorithmic steps involved in query correction as described in the lecture. (10 pts of the 80 C+R points in the rubric) )
(iii) Also, answer the following multiple-choice questions: You can list the quetsion number and the letter corresponding to the correct choice as Answer in your report, (2x5 = 10 pts of the 80 C+R points in the rubric)
Q1: Based on the lecture, there are 5 fundamental components of Data Science, which are Data Capturing, Data Maintenance, Data Processing, Data Analysis, and Data Communication. Which of the following part is NOT a component of Data Maintenance?
A. Data Warehousing
B. Data Cleansing
C. Data Reporting
D. Data Staging
Q2: Based on the lecture, there are 5 fundamental components of Data Science, which are Data Capturing, Data Maintenance, Data Processing, Data Analysis, and Data Communication. Which of the following is an example of a Data Analysis procedure?
A. Data Modeling
B. Text Mining
C. Data Visualization
D. Decision Making based on Data
Q3: Based on the lecture case studies, which of these is an INCORRECT statement?
A. Using high confidence predictions to filter product listings is a goal of Query Categorization Models
B. To collect training data for query categorization, we can manually label the queries, or use implicit feedback
C. Avoiding spelling error is not a goal of the query autocompletion procedure, but of the spelling correction procedure
D. Global query completions may be sub-optimal for personalized query autocompletion
Q4. Based on the lecture, select the INCORRECT statement about Spell Correction procedure
A. When a query is issued, spell correction will first do a Candidate Generation to verify if the query is valid or not
B. In all the mentioned models that are used to efficiently generate spell correction candidates, Compressed Suffix Trees model is the fastest
C. In all the mentioned models that are used to efficiently generate spell correction candidates, the Naïve model has an advantage of not using a memory footprint
D. In many cases, it is possible that in the candidate generation procedure, a query is identified as no spell correction needed, then the language model won’t be invoked
Q5. Based on the lecture, select all the benefits of an industrial Data Science implementation
1. Data science can empower management to make better decisions
2. Data science can direct actions based on market trends analysis
3. Data science can help in identifying potential opportunities
4. Data science can help in recruiting the right talent for the organization
A. 1,2,3 B. 1,2,4 C. 2,3,4 D. All of them
1.

Coursework Sample Content Preview:
Data Science is a Core Pillar in this Subspace  Describe the market sector or sub-space covered in this lecture. Data science is a multidisciplinary field that relies on scientific methods, processes, systems, and algorithms to extract useful information and insights from unstructured and structured data. Critical parts of this definition include 'multidisciplinary field' and 'extraction of useful information or insights, ' meaning that several disciplines are involved in extracting information from different data formats. In the lecture, eCommerce is the market sector under discussion, using eBay as a reference. eCommerce is the buying and selling of goods or services using the internet and the transfer of data and money as a means to execute transactions. Platforms like eBay are market places where different people meet virtually to buy and sell from each other. Data science is a core pillar in this subspace because organizations like eBay rely on data to optimize customer experience and sales to drive profits. Data is relevant in this sub-space because it improves customer engagement, allows for the personalization of content and customer experience, and boosts sales. Therefore, organizations in this sector must capture, process, analyze, communicate, and maintain data to benefit from it. Data science empowers decision-making, allows predictions to be made, and compels staff to focus on issues that matter and identify and refine the target audience. Ultimately, data science's objective in the sector is to help users find and discover products for purchase and maximize revenue per user session. What data science-related skills and technologies are commonly used in this sector?
Because of the relevance of data science in eCommerce, all data science skills are necessary if organizations plan to rely on data in making decisions. These skills include Machine Learning (ML), natural language processing, computer vision, and cloud computing, among others. eCommerce consists of many users whose activities generate humongous volumes of data. Thus, platforms like Google Cloud act as data warehouses where authorized employees and business partners have access, meaning cloud computing is a vital aspect of data science in eCommerce. Cloud computing skills are therefore necessary to optimize reliance on data. Natural Language Processing, on the other hand, focuses on the text data sources like product descriptions, customer reviews, and product titles to determine search outcomes and rank outcomes in the search. Therefore, this skill is necessary for improving search results, providing price suggestions, and implementing effective product categorization. In computer vision, the skill is necessary for organizations like eBay because it enhances visual product search and cataloging. For instance, deep tags convert images into metadata-rich signals that are easily searched, discovered, or sold. The accumulation of these skills or specializations in one is necessary for an individual to be competent in this job market and for organizations to actualize effective data strategies and plans. Lastly, machine learning skills are necessary because the algorithms enhance user experience. Given the vast data, organizations rely on machine learning to p...
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