CMT114 Pro-forma

Posted 2021-03-23 bizhui

Cardiff School of Computer Science and Informatics
Coursework Assessment Pro-forma
Module Code: CMT114
Module Title: Python for Data Analysis
Assessment Title: CMT114 Coursework
Assessment Number: 1
Date set: 25-10-2019
Submission date and time: 22-11-2019 at 9:30 am
Return date:
This assignment is worth 40% of the total marks available for this module. If coursework is
submitted late (and where there are no extenuating circumstances):
1 If the assessment is submitted no later than 24 hours after the deadline, the
mark for the assessment will be capped at the minimum pass mark;
2 If the assessment is submitted more than 24 hours after the deadline, a mark
of 0 will be given for the assessment.
Your submission must include the official Coursework Submission Cover sheet, which can
be found here:
https://docs.cs.cf.ac.uk/downloads/coursework/Coversheet.pdf
Submission Instructions
Your coursework should be submitted via Learning Central by the above deadline. You have
to upload the following files:
Description Type Name
Cover sheet Compulsory One PDF (.pdf) file Student_number.pdf
Your solution to question 1 Compulsory One Python (.py) file Q1.py
Your solution to question 2 Compulsory One Python (.py) file Q2.py
Your solution to question 3 Compulsory One Python (.py) file Q3.py
Replace ‘Student_number’ by your student number, e.g. C1234567890. Make sure to include
your student number as a comment in all of the Python files!
For question 1 and 2 submission follow below instructions:
Download the following files from Learning Central:
• Q1.py
• Q2.py
• Q3.py
• IEEEexample.docx
• APAexample.docx
• nobelprizes.json
• whitelist.txt
Test your implementation:
For Q1 and Q2, you can execute the function from the command line
> python3 Q1.py
> python3 Q2.py
You can change the parameters of the function call in the main part of Q1.py and Q2.py.
For Q3, you can use
> python3 Q3.py my_paper.docx IEEE
where my_paper.docx is an example journal paper and IEEE represents the target style.
Any deviation from the submission instructions above (including the number and types of
files submitted) will result in a mark of zero for the assessment or question part.
Staff reserve the right to invite students to a meeting to discuss coursework submissions
Assignment
Answer all of the following questions.
Question 1 – Random converter (Total 30 Marks)
Implement a function random_converter(x) that takes a variable x. It then returns the value
of x that has been randomly converted into int, float, bool, string or complex.
For instance, for x = 12 (an integer) random_converter(x) can return ’12’ (a string) or 12.0
(a float).
Further instructions:
• x can be any type in int, float, bool, string or complex.
• the assignment needs to be truly random, that is, if repeated several times, different
outcomes should result.
• If x cannot be converted (e.g. the string “house” cannot be converted to a number) the
function should print “cannot be converted” and return none.
As a starting point, use Q1.py from Learning Central. Do not rename the file or the function.
Question 2 – Nobel Prize Data Mining (Total 35 Marks)
You are provided with a dataset in json format (nobelprizes.json), which contains information
about Nobel prize winners. Specifically, you will find information about a winner’s name,
category, reason for award, year, etc. To load the dataset, you will need to use the json
module (import json), and the d = json.load(file_object) method.
Question 2.1
- Implement a function report(), which takes as input the json file loaded as a Python
dictionary (which is the default data structure returned by the json.load() method). This
function should return a Pandas DataFrame, where you include the years and categories in
which a Nobel Prize was awarded and those in which it was not. You are not expected to

代写CMT114留学生作业、代做Data Analysis作业
infer any missing information, you should only include years and categories for which there
is an explicit entry in the original dataset. The result should be of the following form (made
up values):
year category awarded_or_not
1963 chemistry True
1976 physics False
Further instructions:
• There is no field called ‘awarded_or_not’ in the dataset, you have to find this
information elsewhere. Discuss your solution in the code as comments.
• Years should be represented as integers, categories as strings and awarded_or_not
values should be boolean.
• Column names should be ‘year’, ‘category’ and ‘awarded_or_not’.
Question 2.2
- Write a function get_laureates_and_motivation() which takes as input three
arguments: the nobel prize dictionary (same as in Q2.1), year (a string) and category (a
string). This function returns a Pandas DataFrame containing one row per laureate (i.e., a
person who has won the Nobel prize). The returned DataFrame should be of the form below
(made up values):
category year id laureate motivation overall_motiva
tion
chemistry 1963 501 john doe he was great he was among
great minds
chemistry 1963 700 susan sarandon she was great NaN
Further instructions:
• The id values refer to the laureate id as per their identifier in the original dataset.
• Overall motivations are reasons for awarding Nobel prizes which apply to more than
one person in the same batch. However, not all laureates have an overall motivation
associated. In those cases, you should insert a NaN value in their ‘overall_motivation’
field.
• Categories, laureates and motivation should be strings, years and ids should be
integers, and overall_motivation should be either string or NaN.
• Use the column names shown in the sample table above, do not change them.
Question 2.3
- Write a function plot_freqs() which generates six plots, one for each category. The xaxis
should contain the 1st, 10th, 20th, 30th, 40th and 50th most frequent word across the
motivation sections for each category. The y-axis should refer to the frequency of each word
in that category. The resulting plot should have a similar arrangement as the one below.
Further instructions.
• You should only count the words provided in whitelist.txt, a text file available in
learning central, with one word per line. Do not count others.
• Your figures should have a title, legend, the frequency of each word, tick marks, labels
in the x axis for each word, be readable (e.g., big enough fonts), etc.
As a starting point, use Q2.py from Learning Central. Do not rename the file or the functions.
Question 3 – Citation Style Manager (Total 35 marks)
In scientific publications, a reference to a previous work (source) that is discussed in the
manuscript is called a citation. In different scientific disciplines, and sometimes even
different journals, different so-called citation styles are used. The citation style defines how a
citation is formatted. We will consider two different citation styles in this question:
- APA style: citation style of the American Psychological Association
(https://www.mendeley.com/guides/apa-citation-guide), see also Wikipedia page
(https://en.wikipedia.org/wiki/APA_style). This style is widely used in Psychology
and Social Sciences.
- IEEE: citation style of the Institute for Electrical and Electronics Engineers (IEEE) is
used in IEEE journals which cover engineering and related disciplines
(https://pitt.libguides.com/citationhelp/ieee). See the Learning Materials/Coursework
folder on Learning Central for more information on the IEEE style.
There are two main aspects to a publication where citation styles apply:
1. In-text citations: These are used in the text body whenever one refers to, summarises,
paraphrases, or quotes from another source. This is an example from Wikipedia
(https://en.wikipedia.org/wiki/APA_style) for a sentence including an in-text citation
of a paper by Schmidt and Oh in APA format:
In our postfactual era, many members of the public fear that the findings of
science are not real (Schmidt & Oh, 2016).
In IEEE format, references are given as numbers in square brackets. Example:
This is compounded by the fact that the field is evolving from work performed
by an individual that does data science to a team that does data science [1].
2. Reference list: In a scientific publication, the last section is typically the References
section, which provides full details on the in-text citations. For instance, the full
reference corresponding to the Schmidt & Oh (2016) in-text citation above would be:
Schmidt, F. L., & Oh, I.-S. (2016). The crisis of confidence in research
findings in psychology: Is lack of replication the real problem? Or is it
something else? Archives of Scientific Psychology, 4(1), 32–37.
https://doi.org/10.1037/arc0000029
In an article using IEEE format, every reference in the reference list needs to be
numbered:
1. J. Saltz, "The Need for New Processes Methodologies and Tools to Support
Big Data Teams and Improve Big Data Project Effectiveness", Big Data
Conference, 2015.
Your task: Implement a function change_style(filepath, style), which takes as input
two arguments: (1) filepath, which can be either IEEEexample.docx or APAexample.docx
and (2) style (a string being either IEEE or APA), and swaps their citation style (i.e.,
converts IEEE citations into APA and vice versa). You are not expected to consider cases
outside the two documents provided.
Detail instructions:
• To ease the task, you will be working with .docx files (working with PDFs or online
sources would be more difficult). Two example files (IEEEexample.docx and
APAexample.docx) are provided in Learning Central.
• Use the python-docx package to read, manipulate, and save doc files. You can install
it using e.g. pip install python-docx. Check the webpage (https://pythondocx.readthedocs.io/en/latest/index.html#)
or other online sources to familiarize
yourself with the package.
• After conversion, save the file by appending ‘_APA_style’ or ‘_IEEE_style’ to the
filename (e.g. ‘myfile_IEEE_style.docx’).
• We make the following simplifications
o In the reference list, you do not need to change the formatting of individual
references. Only make sure that there is numbering (for IEEE style) as
opposed to no numbering (for APA).
o For APA, the reference list should be sorted alphabetically. Example :
IEEE After conversion to APA
1. X. F. Li, The practice of life-insurance actuary,
Tianjin:NanKai University press, 2000.
2. S. H. Lu, "Information asymmetry and the Strategy of life
insurance underwriting", Insurance Studies, no. 9, pp. 39-
40, Sep. 2003.
3. X. A. Wang, "The underwriting of annuity insurance",
Insurance Studies, no. 3, pp. 45-46, Mar. 2004.
4. J. W. Han, M. Kamber, Data Mining: Concepts and
Techniques, San Francisco:Morgan Kaufmann Publishers,
2001.
J. W. Han, M. Kamber, Data Mining: Concepts and
Techniques, San Francisco:Morgan Kaufmann
Publishers, 2001.
X. F. Li, The practice of life-insurance actuary,
Tianjin:NanKai University press, 2000.
S. H. Lu, "Information asymmetry and the Strategy of
life insurance underwriting", Insurance Studies, no. 9,
pp. 39-40, Sep. 2003.
X. A. Wang, "The underwriting of annuity insurance",
Insurance Studies, no. 3, pp. 45-46, Mar. 2004.
o For IEEE, the reference list should be sorted numerically (smaller to greater),
where 1 refers to the first in-text citation in the paper, 2 refers to the next
citation, and so on. Example:
APA After conversion to IEEE
Cialdini, R. B. (2005). What‘s the best secret device for
engaging student interest? The answer is in the title. J. Soc.
Clin. Psychol. 24, 22–29. doi: 10.1521/jscp.24.1.22.59166
…
Vaughn, L., and Schick, T. (1999). How to Think About
Weird Things: Critical Thinking for a New Age. Mountain
View, CA: Mayfield Pub.
…
Willingham, D. T. (2008). Critical thinking: Why is it so
hard to teach? Arts Educ. Policy Rev. 109, 21–32.
[1] Willingham, D. T. (2008). Critical thinking: Why is
it so hard to teach? Arts Educ. Policy Rev. 109, 21–32.
[2] Cialdini, R. B. (2005). What‘s the best secret device
for engaging student interest? The answer is in the title.
J. Soc. Clin. Psychol. 24, 22–29. doi:
10.1521/jscp.24.1.22.59166
[3] Vaughn, L., and Schick, T. (1999). How to Think
About Weird Things: Critical Thinking for a New Age.
Mountain View, CA: Mayfield Pub.
…
o Your program should re-format all in-text citations.
• To implement your programme, you should only use basic Python including string
operations, as well as the docx module. Usage of Numpy, Pandas, the regular
expression module re, or any other modules not used in the first 4 lectures is not
permitted!
As a starting point, use Q3.py from Learning Central. Do not rename the file or the function.
Learning Outcomes Assessed
• Using the Python programming language to complete programming tasks
• Familiarity with basic programming concepts and data structures
• Reading and writing files
Criteria for assessment
Credit will be awarded against the following criteria; the coursework will allow students to
demonstrate their knowledge and practical skills and to apply the principles taught in
lectures. The functions you have implemented will be tested against different data sets. The
score each implemented function receives is judged by its functionality, efficiency, and/or
quality. The below tables explain the specific criteria for each question.
Criteria Distinction
(70-100%)
Merit
(60-69%)
Pass
(50-59%)
Fail
(0-50%)
Q1 Excellent working condition
with no errors
Mostly correct. Minor errors
in output
Major problem. Errors in
output
Mostly wrong or hardly
implemented
Feedback and suggestion for future learning
Feedback on your coursework will address the above criteria. Feedback and marks will be
returned within 4 weeks of your submission date via Learning Central. In case you require
further details you are welcome to schedule a one-to-one meeting. Feedback from this
assignment will be useful for next year’s version of this module as well as the Computational
Data Science module.
Criteria Distinction
(70-100%)
Merit
(60-69%)
Pass
(50-59%)
Fail
(0-50%)
Q2 Functionality
(70%)
fully working application that
demonstrates an excellent
understanding of the assignment
problem using relevant python
approach.
All required functionality is
met, and the application are
working probably with some
minors’ errors
Some of the
functionality
developed with and
incorrect output major
errors.
Faulty application with wrong
implementation and wrong
output
Quality (30%) Figures are elegant and show an
excellent understanding of
visualisation principles including
tick marks, labels, colouring, and
titles.
Figures show a good
understanding of visualisation
principles.
Figures show a basic
understanding of
visualisation
principles.
Missing figures.
Criteria Distinction
(70-100%)
Merit
(60-69%)
Pass
(50-59%)
Fail
(0-50%)
Q3 Functionality
(70%)
fully working application that
demonstrates an excellent
understanding of the assignment
problem using relevant python
approach.
All required functionality is
met, and the application are
working probably with some
minors’ errors
Some of the
functionality
developed with and
incorrect output major
errors.
Faulty application with wrong
implementation and wrong
output
Efficiency
(15%)
Excellent performance passing all
test cases
Good performance missed
some test cases
Passed some test cases
with incorrect output.
Did not pass any test case
Quality (15%) Excellent documentation with usage
of __docstring__ and comments
Good documentation with
minor missing of comments.
Fair documentation. No comments or documentation
at all

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