ASSESSMENT GUIDE Unit: ITEC102 Python fundamentals for data science, Semester 1, 2021 Assessment number (2) Assessment Artefact: Python Codes and Comments Weighting [30%] Why this assessment? What are...

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ASSESSMENT GUIDE Unit: ITEC102 Python fundamentals for data science, Semester 1, 2021 Assessment number (2) Assessment Artefact: Python Codes and Comments Weighting [30%] Why this assessment? What are the types of employability skills that I will acquire upon completion of this assessment? Assessment Overview: Purpose, as written in the EUO Due date: 5pm on Friday of Week 11 (21 May 2021) Weighting: 30% Length and/or format: Python codes,comments and markdown in jupyter notebook Learning outcomes assessed LO2 Graduate attributes assessed GA5, GA10 How to submit: task submission – via LEO Return of assignment: Via LEO within 2 weeks of submission Assessment criteria: Rubric: see end of document • The purpose is to assess students’ use of Python basics, e.g., lists and functions, as well as Python data science libraries NumPy and Pandas to explore data. Skill Type Developed critical and analytical thinking ☒ Developed ability to solve complex problems ☐ Developed ability to work effectively with others ☐ Developed confidence to learn independently ☒ Developed written communication skills ☒ Developed spoken communication skills ☐ Developed knowledge in the field study ☐ Developed work-related knowledge and skills ☒ 2 Context Data processing lab practical with Python basics and Python libraries NumPy and Pandas Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The f irst case was identified in Wuhan, China, in December 2019. The disease has since spread worldwide, leading to an ongoing pandemic. Monitoring the spread of the coronavirus disease is expected to monitor epidemiological trends, rapidly detect new cases, and based on this information, provide epidemiological information to conduct risk assessment and guide disease preparedness. In this practical you will have the chance to do initial exploratory about a COVID-19 dataset with learned skills of Python basics and Python data science libraries NumPy and Pandas. Instructions You will be given a COVID-19 dataset in csv format, and are required to do below tasks: 1. (5 marks) Good structure of Python Jupyter Notebook a. Containing title cells, subtitle cells. b. Python codes are reasonably separated into groups (code cells) with functionalities. c. Containing meaningful comments and sensible variable and function names. 2. (5 marks) Read in csv data with pandas 3. (5 marks) Display first 5 rows of the loaded data (2 marks) and do a short summary about the data (3 marks) 4. (5 marks) Get daily confirmed cases worldwide (hint: summarize daily confirmed cases over all countries.) 5. (5 marks) Get daily increasement of confirmed cases via defining a function (hint: use the confirmed cases of today minus the confirmed cases of yesterday from the data obtained in task 4.) 6. (5 marks) Get daily moving average of confirmed cases via defining a function a. hint 1: use the data obtained in task 4 b. hint 2: moving average formula- ??(?, ?) = ?? +??+1+⋯+??+?−1 ? , namely, to calculate the moving average confirmed case at the date ? with a window size ?, you add the confirmed cases of the following ? days and then divide the sum by ?. In other words, you calculate the mean of the confirmed cases of ? days. 7. (5 bonus marks) Visualize the data obtained in task 4 and task 6 with library matplotlib Structure Prepare a jupyter notebook for this assignment. The structure of the Jupyter notebook should alternate texts and python codes and cover topics listed the in specific tasks above. One template could be found in any week’s workshop resources in LEO. 3 How do I submit? Submit Jupyter notebook (.ipynb) to Assessment 2 via LEO assessment tile Note that: The code will be compared to other students’ submission in Turnitin to make sure the submission satisfies academic integrity. Submission checklist I have formatted my report as per the specifications ☐ I have checked my Turnitin report and taken appropriate actions to ensure that the submission satisfies academic integrity ☐ I have actioned feedback advice provided to me from labs feedback (if applicable) ☐ I have submitted my work before the due date/time ☐ I have submitted feed forward template along with my assignment submission ☐ Feed Forward Template (example) A template for students to use and act on feedback and provide recommendations for improvement. Note This is a task for any instance of follow-on assignment (assessment 2 and 3). This must be submitted as the first page of the follow-on assignment (assessment 2 and 3) to ensure you acted on the feedback provided to you in the previous assignment (this is not counted as part of the assessment word count). How did you act on the feedback? Feedback is an important component of learning. Please consider the feedback you received in your last assignment and provide a response on how you acted on, or intend to act upon, that feedback, and how it has informed the current assignment task. Submit this sheet along with your assignment. Questions Your learning from the previous assignment feedback How have you acted on the feedback from previous assignment to improve your work in this assignment? (e.g. based on my previous feedback, I made sure that I supported my discussion, position, ideas, concepts with peer reviewed journal references in this assignment) What is your expectation around the type of feedback that enhances your learning? (e.g. I want to know where I made a mistake and how I can correct them and not make the same mistake again i.e. I want specific feedback that will help me to improve my learning and performance in the next assignment) Did you have any difficulty understanding or acting on previous feedback? Please be as specific as possible so that you can gain further feedback/clarify anything you do not understand in the feedback (e.g. feedback provided in my previous assignment was very generic I did not know how to improve my work. So, I would like the teacher to explain more on xxxx aspects of the feedback or I would like an opportunity to have a dialogue to understand the feedback) 4 Some Helpful Websites and Resources LEO weekly materials Anaconda environment https://docs.anaconda.com/anaconda/ Python official website https://www.python.org/ Useful python packages: NumPy https://numpy.org/ Pandas https://pandas.pydata.org/ Who can help me? Academic skills Unit (ASU) Places – Lecturer Maoying Qiao ([email protected]) Lab demonstrator/Online consultation – Dr Maoying Qiao I’m having problems Special Consideration: This form is used by students to apply for Special Consideration for assessable work in studies at Australian Catholic University. Approval of such applications will only be granted to students who are legitimately disadvantaged in their assessment due to exceptional and unforeseen circumstances beyond their control. Referencing N/A Criteria The full criteria is compiled in a rubric, which can be found on the following page/s. https://docs.anaconda.com/anaconda/ https://www.python.org/ https://numpy.org/ https://pandas.pydata.org/ https://units.acu.edu.au/__data/assets/word_doc/0006/620655/SC_Application_for_Special_Consideration_20180214.docx 5 Rubric for Assessment 2 Relevant LO/GAs Criterion (related to a single GA from the related LO – one GA per criterion Does not meet expectations Meets expectations Exceeds expectations NN (0-49) PA (50-64) CR (65-74) DI (75-84) HD (85-100) GA5 LO2 Weight=15 marks TL=3 Learning stage = I and D Demonstrate skills of correct understanding and use of Python basics including data structures e.g., lists, control flow e.g. if-else and loops as well as functions Fail to adequately demonstrate skills of correct understanding and use of Python basics including data structures e.g., lists, control flow e.g. if-else and loops as well as functions and fail to implement any required tasks (0 – 7.35) Adequately demonstrate skills of correct understanding and use of Python basics including data structures e.g., lists, control flow e.g. if-else and loops as well as functions and correctly implement at least tasks 1 & 4 (7.5-9.6) Credibly demonstrate skills of correct understanding and use of Python basics including data structures e.g., lists, control flow e.g. if-else and loops as well as functions and correctly implement at least tasks 1 & 2 & 4 (9.75 – 11.1) Distinctively demonstrate skills of correct understanding and use of Python basics including data structures e.g., lists, control flow e.g. if-else and loops as well as functions and correctly implement at least tasks 1 & 2 & 4 & 5 (11.25 – 12.6) Highly distinctively demonstrate skills of correct understanding and use of Python basics including data structures e.g., lists, control flow e.g. if-else and loops as well as functions and correctly implement all required tasks (12.75 – 15) GA10 LO2 Weight=15 marks TL=3 Learning stage = I and D Demonstrate skills of correct understanding and use of Python data science libraries NumPy and Pandas to prepare and explore data Fail to adequately demonstrate skills of correct understanding and use of Python data science libraries NumPy and Pandas to prepare and explore data and fail to achieve any above tasks. (0 – 7.35) Adequately demonstrate skills of correct understanding and use of Python data science libraries NumPy and Pandas to prepare and explore data and correctly achieve at least the assignment tasks 1 & 3. (7.5-9.6) Credibly demonstrate skills of correct understanding and use of Python data science libraries NumPy and Pandas to prepare and explore data and correctly achieve at least the assignment tasks 1 & 3 & 4 (9.75 – 11.1) Distinctively demonstrate skills of correct understanding
Answered 1 days AfterMay 04, 2021

Answer To: ASSESSMENT GUIDE Unit: ITEC102 Python fundamentals for data science, Semester 1, 2021 Assessment...

Suraj answered on May 05 2021
151 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...3/20/213/21/213/22/213/23/213/24/213/25/213/26/213/27/213/28/213/29/21
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Afghanistan33.9391167.709953000000...56093561035615356177561925622656254562905629456322
1NaNAlbania41.1533020.168300000000...120541121200121544121847122295122767123216123641124134124419
2NaNAlgeria28.033901.659600000000...116066116157116255116349116438116543116657116750116836116946
3NaNAndorra42.506301.521800000000...11481115171154511591116381168711732118091185011888
4NaNAngola-11.2027017.873900000000...21696217332175721774218362191421961220312206322132
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"1 NaN Albania 41.15330 20.168300 0 0 \n",
"2 NaN Algeria 28.03390 1.659600 0 0 \n",
"3 NaN Andorra 42.50630 1.521800 0 0 \n",
"4 NaN Angola -11.20270 17.873900 0 0 \n",
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},
"execution_count": 51,
"metadata": {},
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}
],
"source": [
"# file reading\n",
"df=pd.read_csv(\"C:/Users/Hp/Desktop/data.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
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LatLong1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/20...3/20/213/21/213/22/213/23/213/24/213/25/213/26/213/27/213/28/213/29/21
count273.000000273.000000274.000000274.000000274.000000274.000000274.000000274.000000274.000000274.000000...2.740000e+022.740000e+022.740000e+022.740000e+022.740000e+022.740000e+022.740000e+022.740000e+022.740000e+022.740000e+02
mean20.53480423.0281432.0328472.3905113.4343075.2299277.72992710.68248220.35766422.507299...4.483573e+054.499046e+054.514252e+054.533016e+054.556254e+054.579990e+054.603318e+054.624626e+054.641794e+054.658543e+05
std25.19459273.59616626.87910126.97707733.58523846.74349465.32449388.014971215.981285217.304706...2.138916e+062.143138e+062.148134e+062.154077e+062.162328e+062.169929e+062.177723e+062.184733e+062.189918e+062.195915e+06
min-51.796300-178.1165000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...0.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+00
25%5.152149-19.0208000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...1.018750e+031.018750e+031.020250e+031.020250e+031.021000e+031.021000e+031.021500e+031.025250e+031.031250e+031.043750e+03
50%21.69400020.9394000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...1.396950e+041.396950e+041.396950e+041.396950e+041.443450e+041.443450e+041.443450e+041.443450e+041.443450e+041.443450e+04
75%41.11290084.2500000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...1.871545e+051.877745e+051.887062e+051.902122e+051.919832e+051.936948e+051.954990e+051.973580e+051.983818e+051.989432e+05
max71.706900178.065000444.000000444.000000549.000000761.0000001058.0000001423.0000003554.0000003554.000000...2.978594e+072.981970e+072.987127e+072.992489e+073.001184e+073.007928e+073.015662e+073.021868e+073.026238e+073.033179e+07
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" Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
"count 273.000000 273.000000 274.000000 274.000000 274.000000 274.000000 \n",
"mean 20.534804 23.028143 2.032847 2.390511 3.434307 5.229927 \n",
"std 25.194592 73.596166 26.879101 26.977077 33.585238 46.743494 \n",
"min -51.796300 -178.116500 0.000000 0.000000 0.000000 0.000000 \n",
"25% 5.152149 -19.020800 0.000000 0.000000 0.000000 0.000000 \n",
"50% 21.694000 20.939400 0.000000 0.000000 0.000000 0.000000 \n",
"75% 41.112900 84.250000 0.000000 0.000000 0.000000 0.000000 \n",
"max 71.706900 178.065000 444.000000 444.000000 549.000000 761.000000 \n",
"\n",
" 1/26/20 1/27/20 1/28/20 1/29/20 ... 3/20/21 \\\n",
"count 274.000000 274.000000 274.000000 274.000000 ... 2.740000e+02 \n",
"mean 7.729927 10.682482 20.357664 22.507299 ... 4.483573e+05 \n",
"std 65.324493 88.014971 215.981285 217.304706 ... 2.138916e+06 \n",
"min 0.000000 0.000000 0.000000 0.000000 ... 0.000000e+00 \n",
"25% 0.000000 0.000000 0.000000 0.000000 ... 1.018750e+03 \n",
"50% 0.000000 0.000000 0.000000 0.000000 ... 1.396950e+04 \n",
"75% 0.000000 0.000000 0.000000 0.000000 ... 1.871545e+05 \n",
"max 1058.000000 1423.000000 3554.000000 3554.000000 ... 2.978594e+07 \n",
"\n",
" 3/21/21 3/22/21 3/23/21 3/24/21 3/25/21 \\\n",
"count ...
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