CC5067NISmartDataDiscoveryY25SpringMainSitCWQP_f89a9efd-1e5c-4f71-a941-825b5d75b874_93472_

1^st sit Coursework Question Paper Spring Semester 2025

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© London Metropolitan University

PLAGIARISM

You are reminded that there exist regulations concerning plagiarism. Extracts from these regulations are printed overleaf. Please sign below to say that you have read and understood these extracts:

Extracts from University Regulations on Cheating, Plagiarism, and Collusion

Section 2.3: “The following broad types of offence can be identified and are provided as indicative examples ….

1. Cheating: including taking unauthorised material into an examination; consulting unauthorised material outside the examination hall during the examination; obtaining an unseen examination paper in advance of the examination; copying from another examinee; using an unauthorised calculator during the examination or storing unauthorised material in the memory of a programmable calculator which is taken into the examination; copying coursework.
2. Falsifying data in experimental results.
3. Personation, where a substitute takes an examination or test on behalf of the candidate. Both candidate and substitute may be guilty of an offence under these Regulations.
4. Bribery or attempted bribery of a person is thought to have some influence on the candidate’s assessment.
5. Collusion to present joint work as the work solely of one individual.
6. Plagiarism, where the work or ideas of another are presented as the candidate’s own.
7. Other conduct calculated to secure an advantage on assessment.
8. Assisting in any of the above.

Some notes on what this means for students:

1. Copying another student’s work is an offence, whether from a copy on paper or a computer file, and in whatever form the intellectual property being copied takes, including text, mathematical notation, and computer programs.

2. Taking extracts from published sources without attribution is an offence. To quote ideas, sometimes using extracts, is generally to be encouraged. Quoting ideas is achieved by stating an author’s argument and attributing it, perhaps by quoting, immediately in the text, his or her name and year of publication, e.g. “e = mc² (Einstein 1905)”. A reference section at the end of your work should then list all such references in alphabetical order of authors’ surnames. (There are variations on this referencing system which your tutors may prefer you to use.) If you wish to quote a paragraph or so from published work then indent the quotation on both left and right margins, using an italic font where practicable, and introduce the quotation with attribution.

CONTRACT CHEATING

Contract cheating (also known as assessment outsourcing, commissioning or ghost writing) is when someone seeks out another party, or AI generator service, to produce work or buy an essay or assignment, either already written or specifically written for them or the assignment to submit as their own piece of work.

Contract cheating undermines the integrity of the academic process and devalues the qualifications awarded by the university. Students are reminded that academic integrity is a fundamental principle of our institution. Engaging in contract cheating not only impacts the individual’s academic record but also the reputation of the university.

Students are encouraged to seek support if they are struggling with their coursework. The university offers a range of resources, including academic counselling, tutoring services, and workshops on study skills and time management. Utilizing these resources can help students achieve their academic goals without resorting to dishonest practices.

Penalty:

• Failure in the Module: The student must re-register for the same module, and the re-registered module will be capped at a bare pass.
• Ineligibility to Continue on the Course: Where re-registration of the same module, or a suitable alternative, is not permissible, the student will not be able to continue on the course. Additionally, the following penalty will be applied to the student’s final award:
  • Undergraduate Honors: The student’s final classification will be reduced by one level.
  • Unclassified Bachelors: Downgraded to Diploma in Higher Education.
  • Foundation Degree: Distinction downgraded to Merit; Merit downgraded to Pass; Pass downgraded to Certificate in Higher Education.
  • Masters: Distinction downgraded to Merit; Merit downgraded to Pass; Pass downgraded to Postgraduate Diploma.

Reporting and Consequences:

Instances of contract cheating will be thoroughly investigated, and students found guilty will face the penalties outlined above. It is the responsibility of every student to ensure that their work is their own and to avoid situations that could lead to accusations of academic misconduct. By adhering to these standards, students contribute to a fair and equitable academic environment, ensuring the value and recognition of their qualifications are maintained.

Coursework Assignment

The coursework is an individual assessment weighted 60% of the marks for the module. It is primarily an exercise in applying programming knowledge and skills to data analysis tasks, demonstrating your skills for problem-solving and critical thinking/evaluation. This assignment involves the Customer Service Requests analysis. You are expected to write Python programs and technical report on data understanding, preparation, exploration, and initial analysis.

Data Set Description

The data contains the information about various factors which can influence Customer Service request. The objective of this analysis is to perform a service request of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types. Domain: Customer Service

The primary objective of your work is to prepare data for further data mining and analysis.

Requirements Specifications

1. Data Understanding

• To understand what your data resources are and the characteristics of

those resources. Write down your findings. [10 Marks]

1. Data Preparation

• Import the dataset [5 marks]
• Provide your insight on the information and details that the provided dataset carries. [5 marks]
• Convert the columns “Created Date” and “Closed Date” to datetime

datatype and create a new column “Request_Closing_Time” as the time elapsed between request creation and request closing [10 Marks]

• Write a python program to drop irrelevant Columns which are listed below.

[‘Agency Name’,’Incident Address’,’Street Name’,’Cross Street 1′,’Cross Street 2′,’Intersection Street 1′, ‘Intersection Street 2′,’Address Type’,’Park Facility Name’,’Park Borough’,’School Name’, ‘School Number’,’School Region’,’School Code’,’School Phone Number’,’School Address’,’School City’,

‘School State’,’School Zip’,’School Not Found’,’School or Citywide Complaint’,’Vehicle Type’, ‘Taxi Company Borough’,’Taxi Pick Up location’,’Bridge Highway Name’,’Bridge Highway Direction’,

‘Road Ramp’,’Bridge Highway Segment’,’Garage Lot Name’,’Ferry Direction’,’Ferry Terminal Name’,’Landmark’,

‘X Coordinate (State Plane)’,’Y Coordinate (State Plane)’,’Due Date’,’Resolution Action Updated Date’,’Community Board’,’Facility Type’,

‘Location’]

[5 Marks]

• Write a python program to remove the NaN missing values from updated dataframe. [5 Marks]
• Write a python program to see the unique values from all the columns in the dataframe. [5 Marks]

1. Data Analysis

• Write a Python program to show summary statistics of sum, mean, standard deviation, skewness, and kurtosis of the data frame.

[5 Marks]

• Write a Python program to calculate and show correlation of all variables. [5 Marks]

1. Data Exploration

• Provide four major insights through visualization that you come up after data mining. [10 Marks]
• Arrange the complaint types according to their average ‘Request_Closing_Time’, categorized by various locations. Illustrate it through graph as well.

[10 Marks]

1. Statistical Testing

• Test 1: Whether the average response time across complaint types is similar or not.
• State the Null Hypothesis (H0) and Alternate Hypothesis (H1).
• Perform the statistical test and provide the p-value.
• Interpret the results to accept or reject the Null Hypothesis.

[10 Marks]

• Test 2: Whether the type of complaint or service requested and location are related.
  • State the Null Hypothesis (H0) and Alternate Hypothesis (H1).
  • Perform the statistical test and provide the p-value.
  • Interpret the results to accept or reject the Null Hypothesis.

[10 Marks]

1. Document Organization

• Report Structure [5 Marks]

All Python programs should have screen shots of testing, results, and brief user guide in the technical report. Python codes should include adequate comments.

Milestone 1 (Week 7)

• • • 1. Data Understanding
• To understand what your data resources are and the characteristics of

those resources. Write down your findings. [10 Marks]

• • • 1. Data Preparation
• Import the dataset [5 marks]
• Provide your insight on the information and details that the provided dataset carries. [5 marks]
• Convert the columns “Created Date” and “Closed Date” to datetime

datatype and create a new column “Request_Closing_Time” as the time elapsed between request creation and request closing [10 Marks]

• Write a python program to drop irrelevant Columns which are listed below.

[‘Agency Name’,’Incident Address’,’Street Name’,’Cross Street 1′,’Cross Street 2′,’Intersection Street 1′, ‘Intersection Street 2′,’Address Type’,’Park Facility Name’,’Park Borough’,’School Name’, ‘School Number’,’School Region’,’School Code’,’School Phone Number’,’School Address’,’School City’,

‘School State’,’School Zip’,’School Not Found’,’School or Citywide Complaint’,’Vehicle Type’, ‘Taxi Company Borough’,’Taxi Pick Up location’,’Bridge Highway Name’,’Bridge Highway Direction’,

‘Road Ramp’,’Bridge Highway Segment’,’Garage Lot Name’,’Ferry Direction’,’Ferry Terminal Name’,’Landmark’,

‘X Coordinate (State Plane)’,’Y Coordinate (State Plane)’,’Due Date’,’Resolution Action Updated Date’,’Community Board’,’Facility Type’,

‘Location’]

[5 Marks]

• Write a python program to remove the NaN missing values from updated dataframe. [5 Marks]
• Write a python program to see the unique values from all the columns in the dataframe. [5 Marks]
  • • 1. Data Analysis
• Write a Python program to show summary statistics of sum, mean, standard deviation, skewness, and kurtosis of the data frame.

[5 Marks]

• Write a Python program to calculate and show correlation of all variables.

[5 Marks]

Milestone 2 (Week 10)

1. Data Exploration

• Provide four major insights through visualization that you come up after data mining. [10 Marks]
• Arrange the complaint types according to their average ‘Request_Closing_Time’, categorized by various locations. Illustrate it through graph as well. [10 Marks]

1. Statistical Testing

• Test 1: Whether the average response time across complaint types is similar or not.
• State the Null Hypothesis (H0) and Alternate Hypothesis (H1).
• Perform the statistical test and provide the p-value.
• Interpret the results to accept or reject the Null Hypothesis.

[10 Marks]

• Test 2: Whether the type of complaint or service requested and location are related.
  • State the Null Hypothesis (H0) and Alternate Hypothesis (H1).
  • Perform the statistical test and provide the p-value.
  • Interpret the results to accept or reject the Null Hypothesis.

[10 Marks]

1. Document Organization

• Report Structure [5 Marks]

End of paper